Novel Developments in Web-Based Learning Technologies: Tools for Modern Teaching

Novel Developments in Web-Based Learning Technologies: Tools for Modern Teaching Nikos Karacapilidis University of Patras, Greece

InformatIon scIence reference Hershey • New York

Director of Editorial Content: Director of Book Publications: Development Editor: Typesetter: Quality control: Cover Design: Printed at:

Kristin Klinger Julia Mosemann Julia Mosemann Deanna Zombro, Sean Woznicki Jamie Snavely Lisa Tosheff Yurchak Printing Inc.

Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com/reference Copyright © 2010 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Novel developments in web-based learning technologies : tools for modern teaching / Nikos Karacapilidis, editor. p. cm. Includes bibliographical references and index. Summary: "This book explores a series of issues related to the current state, objectives and future trends of collaborative learning"--Provided by publisher. ISBN 978-1-60566-938-0 (hardcover) -- ISBN 978-1-60566-939-7 (ebook) 1. Web-based instruction. 2. Education--Computer network resources. 3. Teaching--Computer network resources. 4. Internet in education. I. Karacapilidis, Nikos. LB1044.87.N67 2010 371.33'44678--dc22 2009052655

British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher.

Advances in Web-Based Learning Series (AWBL) ISBN: 1935-3669

Editor-in-Chief: Nikos Karacapilidis, University of Patras, Greece Web-Based Education and Pedagogical Technologies: Solutions for Learning Applications Liliane Esnault; EM Lyon, France

IGI Publishing • copyright 2008 • 364 pp •H/C (ISBN: 978-1-59904-525-2) The rapid development and expansion of Web-based technologies has vast potential implications for the processes of teaching and learning world-wide. Technological advancements of Web-based applications strike at the base of the education spectrum; however, the scope of experimentation and discussion on this topic has continuously been narrow. Web-Based Education and Pedagogical Technologies: Solutions for Learning Applications provides cutting-edge research on such topics as network learning, e-learning, managing Web-based learning and teaching technologies, and building Web-based learning communities. This innovative book provides researchers, practitioners, and decision makers in the field of education with essential, up-to-date research in designing more effective learning systems and scenarios using Web-based technologies.

Solutions and Innovations in Web-Based Technologies for Augmented Learning: Improved Platforms, Tools, and Applications Nikos Karacapilidis, University of Patras, Greece

Information Science Reference • copyright 2009 • 374 pp •H/C (ISBN: 978-1-60566-238-1) The proper exploitation of Web-based technologies towards building responsive environments that motivate, engage, and inspire learners, and which are embedded in the business processes and human resources management systems of organizations, is highly critical. Accordingly, the research field of technology-enhanced learning continues to receive increasing attention. Solutions and Innovations in Web-Based Technologies for Augmented Learning: Improved Platforms, Tools, and Applications provides cutting-edge research on a series of related topics and discusses implications in the modern era’s broad learning concept. Addressing diverse conceptual, social, and technical issues, this book provides professionals, researchers, and practitioners in the field with up-to-date research in developing innovative and more effective learning systems by using Web-based technologies.

The Advances in Web-based Learning (AWBL) Book Series aims at providing an in-depth coverage and understanding of diverse issues related to the application of web-based technologies for facilitating and augmenting learning in contemporary organizational settings. The issues covered address the technical, pedagogical, cognitive, social, cultural and managerial perspectives of the Web-based Learning research domain. The Advances in Web-based Learning (AWBL) Book Series endeavors to broaden the overall body of knowledge regarding the above issues, thus assisting researchers, educators and practitioners to devise innovative Web-based Learning solutions. Much attention will be also given to the identification and thorough exploration of good practices in developing, integrating, delivering and evaluating the impact of Web-based Learning solutions. The series intends to supply a stage for emerging research in the critical areas of web-based learning to further expand to importance of comprehensive publications on these topics of global importance.

Hershey • New York Order online at www.igi-global.com or call 717-533-8845 x100– Mon-Fri 8:30 am - 5:00 pm (est) or fax 24 hours a day 717-533-7115

Editorial Advisory Board Anil Aggarwal, University of Baltimore, USA Katy Campbell, University of Alberta, Canada Antonio Cartelli, University of Cassino, Italy Liliane Esnault, EM LYON, France Carol Lerch, Daniel Webster College, USA Cher Ping Lim, Edith Cowan University, Australia Katia Passerini, New Jersey Institute of Technology, USA Roy Rada, University of Maryland at Baltimore County, USA

Table of Contents

Preface ..............................................................................................................................................xviii Chapter 1 Virtual Communities of Practices in Higher Education: Which Processes and Technologies? Evidence from a Case Study ................................................................................................................... 1 Gianluca Elia, University of Salento, Italy Giustina Secundo, University of Salento, Italy Cesare Taurino, University of Salento, Italy Chapter 2 Exploiting Virtual Environments and Web 2.0 Immersive Worlds to Support Collaborative e-Learning Communities ...................................................................................................................... 20 Christos Bouras, University of Patras, Greece Eri Giannaka, University of Patras, Greece Thrasyvoulos Tsiatsos, Aristotle University of Thessaloniki, Greece Chapter 3 Web Technologies and E-Learning Strategies for New Teaching Paradigms ....................................... 46 Antonio Cartelli, University of Cassino, Italy Chapter 4 Teaching Dimension in Web-Based Learning Communities ................................................................ 59 Francesca Pozzi, Istituto Tecnologie Didattiche – CNR, Italy Chapter 5 Management of Lecture Time: Using the Web to Manipulate Extrinsic Cognitive Load .................... 69 Michael A. Chilton, Kansas State University, USA Anil Gurung, Neumann College, USA

Chapter 6 Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education ................ 83 Gérard Fillion, University of Moncton, Canada Moez Limayem, University of Arkansas, USA Thérèse Laferrière, Laval University, Canada Robert Mantha, Laval University, Canada Chapter 7 Profiling Group Activity of Online Academic Workspaces: The Hellenic Open University Case Study ............................................................................................................... 118 D. Karaiskakis, Hellenic Open University, Greece D. Kalles, Hellenic Open University, Greece Th. Hadzilacos, Hellenic Open University, Greece Chapter 8 A Method of Building Learner Model in Personalized E-Learning ................................................... 133 Xiyuan Wu, Xi’an Jiaotong University, China Qinghua Zheng, Xi’an Jiaotong University, China Hao Wang, Xi’an Jiaotong University, China Haifei Li, Union University, USA Guangdong Liu, Xi’an Jiaotong University, China Chapter 9 OrPAF: An Environment for Adaptive Hypermedia Courses in the Semantic Web Context ............. 159 Amel Yessad, Université Badji Mokhtar, Algérie Catherine Faron-Zucker, Université de Nice Sophia, France Peter Sander, Université de Nice Sophia, France Med Tayeb Laskri, Université Badji Mokhtar, Algérie Chapter 10 E-Learning with Wikis, Weblogs and Discussion Forums: An Empirical Survey about the Past, the Presence and the Future ........................................................................................ 174 Reinhard Bernsteiner, University for Health Sciences, Austria Herwig Ostermann, University for Health Sciences, Austria Roland Staudinger, University for Health Sciences, Austria Chapter 11 Social Tagging and Learning: The Fuzzy Line between Private and Public Space ............................ 199 A. Kohlhase, German Centre for Artificial Intelligence, Germany M.Reichel, Waterford Institute of Technology, Ireland

Chapter 12 Some Key Success Factors in Web-Based Corporate Training in Brazil: A Multiple Case Study ........................................................................................................................ 211 Luiz Antonio Joia, Brazilian School of Public and Business Administration of Getulio Vargas Foundation and Rio de Janeiro State University, Brazil Mário Figueiredo Costa, Brazilian School of Public and Business Administration of Getulio Vargas Foundation, Brazil Chapter 13 A Virtual World Environment for Group Work .................................................................................. 233 E. Brown, Anglia Ruskin University, UK M. Hobbs, Anglia Ruskin University, UK M. Gordon, Anglia Ruskin University, UK Chapter 14 Reading Comprehension as a Competence to Digital Media Expert Performance ............................ 245 Maria Cristina Rodrigues Azevedo Joly, São Francisco University, Brazil Ronei Ximenes Martins, São Francisco University, Brazil Chapter 15 Implementation of Efficient Proactive Computing Using Lazy Evaluation in a Learning Management System ........................................................................................................................... 258 Denis Zampunieris, University of Luxembourg, Luxembourg Chapter 16 EVAWEB V2: Enhancing a Web-Based Assessment System Focused on Non-Repudiation Use and Teaching ............................................................................................................................... 265 A. I. González-Tablas, Universidad Carlos III De Madrid, Spain A. Orfila, Universidad Carlos III De Madrid, Spain B. Ramos, Universidad Carlos III De Madrid, Spain A. Ribagorda, Universidad Carlos III De Madrid, Spain Chapter 17 SOA-Frameworks for Modular Virtual Learning Environments: Comparing Implementation Approaches ............................................................................................ 277 Fredrik Paulsson, Umeå University, Sweden Mikael Berglund, Umeå University, Sweden Chapter 18 Towards Automated Specifications of Scenarios in Enhanced Learning Technology ........................ 294 A. Rius Gavidia, UOC Open University of Catalonia, Spain M. A. Sicilia Urbán, UAH University of Alcalá, Spain E. García-Barriocanal, UAH University of Alcalá, Spain G. Macarro Palazuelos, UAH University of Alcalá, Spain

Chapter 19 Bridging the Gap between Instructional Design and Double-Loop Learning .................................... 305 Howard Spoelstra, Open University of The Netherlands, The Netherlands Ellen Rusman, Open University of The Netherlands, The Netherlands Jan van Bruggen, Open University of The Netherlands, The Netherlands Rob Koper, Open University of The Netherlands, The Netherlands Maristella Matera, Politecnico di Milano, Italy Chapter 20 The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD) ............................................................................................................................. 316 Evangelos Triantafillou, Center of Educational Technology, Greece Elissavet Georgiadou, Center of Educational Technology, Greece Anastasios A. Economides, University of Macedonia, Greece Chapter 21 Integrating Awareness Mechanisms in Web-Based Argumentative Collaboration Support Environments ........................................................................................................................ 327 Manolis Tzagarakis, Research Academic Computer Technology Institute, Greece Nikos Karousos, Research Academic Computer Technology Institute, Greece Nikos Karacapilidis, University of Patras, Greece Compilation of References ............................................................................................................... 341 About the Contributors .................................................................................................................... 379 Index ................................................................................................................................................... 391

Detailed Table of Contents

Preface ..............................................................................................................................................xviii Chapter 1 Virtual Communities of Practices in Higher Education: Which Processes and Technologies? Evidence from a Case Study ................................................................................................................... 1 Gianluca Elia, University of Salento, Italy Giustina Secundo, University of Salento, Italy Cesare Taurino, University of Salento, Italy This chapter hypothesizes that Virtual Community of Practices (VCoPs) are valuable to Business Schools and Universities because they support effectively the emerging paradigms of just-in-time, action based and collaborative learning. It presents a case study of a VCoPs called “Virtual eBMS” in Higher Education setting, described as a process-oriented model, composed by four main components: The People participating in the community, the Processes enabling the knowledge flows within the community, the Purpose of the community in terms of value created for the Business School, and the Technology facilitating the interactions between the community members. Indeed, from the technological point of view, the community is supported by an integrated Web Learning and Knowledge Management platform, whose functionalities support the corresponding knowledge processes and actions. Some preliminary results expressed in terms of Intellectual Capital will end the chapter, along with the value created for the community members. Chapter 2 Exploiting Virtual Environments and Web 2.0 Immersive Worlds to Support Collaborative e-Learning Communities ...................................................................................................................... 20 Christos Bouras, University of Patras, Greece Eri Giannaka, University of Patras, Greece Thrasyvoulos Tsiatsos, Aristotle University of Thessaloniki, Greece The main goal of this chapter is to facilitate educational designers and developers by providing a point of reference for making decisions on how to incorporate 3D environments into the applications they develop as well as for extending their capabilities by integrating more functionality. Therefore, this chapter presents the design principles for virtual spaces, which aim at supporting multi-user communication in

web-based learning communities. In addition the implementation of these principles is presented using as point of reference EVE Training Area. This environment constitutes a three-dimensional space where participants, represented by 3D humanoid avatars, have the ability to use a variety of 3D e-collaboration tools for learning together. Furthermore, this chapter presents how these principles could be used as criteria for validating and extending ready Web2.0 Immersive worlds for supporting collaborative e-learning. Finally, collaborative e-learning usage scenarios that could be realized by exploiting collaborative virtual environments are described. Chapter 3 Web Technologies and E-Learning Strategies for New Teaching Paradigms ....................................... 46 Antonio Cartelli, University of Cassino, Italy The chapter aims at presenting some teaching experiences the author made for the introduction of the ICT and especially of Web technologies in teaching. In the introduction the need for the introduction of digital literacy strategies in education and the acquisition of digital competences in the knowledge society are discussed. Soon after, the problems teachers have to face in today society are analyzed and the recommendations of national and supra-national institutions for the continuous education of these professionals are reported (i.e., the European case is almost exclusively discussed). Subsequently a new teaching paradigm based on the implementation of practices is proposed and the features of the information system TETIS, which made possible the use of that paradigm in a master course for teachers, is explained. It is evidenced, among other things, how that system (born for making transparent teaching processes and implementing teachers’ practices) has been used to implement the changes induced by the new regulations in the Italian school. The results from the final questionnaire for the evaluation of the use of the TETIS platform, submitted to the teachers who attended the master course, are at last discussed and new proposals for an effective introduction of new technologies at school is proposed. Chapter 4 Teaching Dimension in Web-Based Learning Communities ................................................................ 59 Francesca Pozzi, Istituto Tecnologie Didattiche – CNR, Italy The chapter tackles the issue of the teaching dimension in computer-supported collaborative learning (CSCL) contexts. In particular, it describes two Web-based courses that were held in 2006—one by the Istituto Tecnologie Didattiche – CNR and one by the University of Genoa, which, while sharing the socioconstructivist theoretical framework, adopt different approaches as far as the teaching dimension is concerned: While in the former course tutors were asked to cover all the functions typically required by e-tutors, in the latter, experience functions were distributed across a variety of actors. The aim of the work is to foster reflections about strong points and weaknesses of the two approaches, thus leading to considerations concerning the applicability of the models even in contexts different from the original ones. Chapter 5 Management of Lecture Time: Using the Web to Manipulate Extrinsic Cognitive Load .................... 69 Michael A. Chilton, Kansas State University, USA Anil Gurung, Neumann College, USA

The variety of new technologies available for classroom use requires a choice not just between the technological options, but among them as well, since an educator may choose a single option or include a mix of media. This study investigates a particular mix of advanced technology and its effect on student learning outcomes. The authors’ experimental design compares outcomes from a traditional teaching format with those of a more advanced web-based format. This model is based on cognitive load theory, is developed from perceptions of the students, and is analyzed using factor analysis. The results based on this qualitative model show promise for delving further into the assessment of learning. This would provide researchers with additional tools to help evaluate their results and educators with a basis on which to make decisions regarding which advanced technologies to use. Chapter 6 Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education ................ 83 Gérard Fillion, University of Moncton, Canada Moez Limayem, University of Arkansas, USA Thérèse Laferrière, Laval University, Canada Robert Mantha, Laval University, Canada For the past two decades, information and communication technologies (ICT) have transformed the ways professors teach and students learn. This study aims to investigate the perceptions of onsite and online students and professors. It was conducted into ICT-supported or technology-rich environments at a Faculty of Administration of a large Canadian university. To conduct the study, a moderator-type theoretical research model was developed, out of which nine hypotheses were formulated. The authors used a multimethod approach to collect data, that is, a Web survey involving open- and closed-ended questions, as well as a structured interview. The sample was composed of 313 students who completed an electronic survey on a Web site and 16 professors teaching to these students who participated in a structured interview. The quantitative data analysis was performed using a structural equation modeling software, that is, Partial Least Squares (PLS); the qualitative data were analyzed following a thematic structure using QSR NVivo software. Chapter 7 Profiling Group Activity of Online Academic Workspaces: The Hellenic Open University Case Study ............................................................................................................... 118 D. Karaiskakis, Hellenic Open University, Greece D. Kalles, Hellenic Open University, Greece Th. Hadzilacos, Hellenic Open University, Greece All undergraduate and postgraduate students of the Hellenic Open University (HOU) attend courses at a distance. The lack of a live academic community is reported by many as a drawback in their studies. Systematic exploitation of new communication and collaboration technologies is desirable in HOU but cannot be imposed universally as the average student’s IT competence level is relatively low. This work presents a key aspect of the development of an integrated communication environment in which collaboration spaces serving as open communities play a key role in user engagement in the whole communication environment. To track and evaluate user participation, the authors propose to use indices drawn from inexpensively collected usage data. Such indices, when combined with detailed knowledge of the internal workings of user groups, provide concrete evaluation of the community online activity.

Chapter 8 A Method of Building Learner Model in Personalized E-Learning ................................................... 133 Xiyuan Wu, Xi’an Jiaotong University, China Qinghua Zheng, Xi’an Jiaotong University, China Hao Wang, Xi’an Jiaotong University, China Haifei Li, Union University, USA Guangdong Liu, Xi’an Jiaotong University, China Learner modeling is the key aspect in personalized e-learning. The quality of the personalization largely depends on the accuracy of the learner model. The core data of a learner model include generally learner’s personality characteristics, interesting etc. While personality characteristics can describe a learner’s stable traces internally, interest can describe something that a learner wants externally. But, a learner’s personality characteristic may have many attributes, and all of them may not have equal values, while learner interests exist implicitly in the information of learner network behavior. The work discusses and evaluates how to find the key personality attributes and their weight, and how to mine learner interest from learner behavior. The method has been successfully used in China e-learning for a major research university. The experimental evaluation shows the modeling method is effective in personalized e-learning. Chapter 9 OrPAF: An Environment for Adaptive Hypermedia Courses in the Semantic Web Context ............. 159 Amel Yessad, Université Badji Mokhtar, Algérie Catherine Faron-Zucker, Université de Nice Sophia, France Peter Sander, Université de Nice Sophia, France Med Tayeb Laskri, Université Badji Mokhtar, Algérie Adaptive learning support for learners becomes very important in the context of increasing re-use of resources from heterogeneous and distributed learning repositories. This chapter presents OrPAF, an Adaptive Educational Hypermedia (AEHS) and web-based System which integrates semantic web models and technologies in order to achieve interoperability with e-learning systems. The key feature of OrPAF is the construction of adaptive hypermedia courses: both the course structure and the course content are dynamically generated and adapted to learners. On the one hand, a learning ontology is proposed to describe, at a meta-level, abstract characteristics of an e-learning system. This learning ontology is instantiated to construct learning models: domain model, learner model and pedagogical model. On the other hand, semantic annotations and a semantic relevance measure are proposed to improve the LOM metadata associated to learning resources in order to reuse and share them. The authors tested the prototype on learners in order to evaluate the usability of OrPAF and to determine the conceptual capabilities developed by learners who used it. Chapter 10 E-Learning with Wikis, Weblogs and Discussion Forums: An Empirical Survey about the Past, the Presence and the Future ........................................................................................ 174 Reinhard Bernsteiner, University for Health Sciences, Austria Herwig Ostermann, University for Health Sciences, Austria Roland Staudinger, University for Health Sciences, Austria

This chapter explores how social software tools can offer support for innovative learning methods and instructional design in general and those related to self-organized learning in an academic context in particular. In the first section the theoretical basis for the integration of wikis, discussion forums and weblogs in the context of learning are discussed. The second part presents the results of an empirical survey conducted by the authors and explores the usage of typical social software tools which support learning from a student’s perspective. The chapter concludes that social software tools have the potential to be a fitting technology in a teaching and learning environment. Chapter 11 Social Tagging and Learning: The Fuzzy Line between Private and Public Space ............................ 199 A. Kohlhase, German Centre for Artificial Intelligence, Germany M.Reichel, Waterford Institute of Technology, Ireland

Social tagging systems celebrate enormous growth rates on the World Wide Web. This chapter looks at social tagging from an educational perspective, particularly its use for educational environments. The authors identify the processes underlying social tagging from an embodied interaction perspective. The authors will support the thesis that emerging folksonomies are at the base of meaningful interaction processes between user and system and also, at the same time, social processes between groups of people. This chapter argues that the fuzzy line between private and public space plays a crucial role. Moreover, tags represent embodied conceptualizations, whose potential effectiveness for learning will be discussed in this chapter. The authors will provide an example of a learning software for children (Amici, implemented by one of the authors) in which social tagging is used to support sharing in a programming environment to showcase how embodiment of conceptualization as well as constant coupling through moving between private and public space is achieved through tagging in the system. Chapter 12 Some Key Success Factors in Web-Based Corporate Training in Brazil: A Multiple Case Study ........................................................................................................................ 211 Luiz Antonio Joia, Brazilian School of Public and Business Administration of Getulio Vargas Foundation and Rio de Janeiro State University, Brazil Mário Figueiredo Costa, Brazilian School of Public and Business Administration of Getulio Vargas Foundation, Brazil Brazilian companies are increasingly turning to Web-based corporate training by virtue of the fact that they need to train their employees within tight budget constraints in a country of continental dimensions. However, most of these companies do not know what the critical success factors in these endeavors are. Therefore, this article seeks to investigate some key success factors associated with such digital enterprises. In order to achieve this, the multiple case study method is used, whereby two cases, both conducted within the same Brazilian company, leading to opposite outcomes—a success and a failure—are analyzed in depth. The conclusions reached in this chapter were that goal orientation, source of motivation, and metacognitive support were the three critical dimensions in these two Web-based corporate training programs under analysis. Lastly, some managerial implications of these results are outlined.

Chapter 13 A Virtual World Environment for Group Work .................................................................................. 233 E. Brown, Anglia Ruskin University, UK M. Hobbs, Anglia Ruskin University, UK M. Gordon, Anglia Ruskin University, UK This chapter seeks to show that a virtual world can provide a useful addition in the use of computermediated learning tools. The authors discuss the underlying educational context and link this to the properties of virtual worlds and, in particular, that of Second Life. They report on the progress of a project for developing group work that seeks to link affordances in the environment to learning outcomes and employs a socially situated, constructivist, pedagogical framework. The authors found that a virtual world environment can enable autonomous, differentiated learning through the use of suitably structured tasks, and postulate that an individual’s depth of engagement with the environment may be linked to the learning style. Chapter 14 Reading Comprehension as a Competence to Digital Media Expert Performance ............................ 245 Maria Cristina Rodrigues Azevedo Joly, São Francisco University, Brazil Ronei Ximenes Martins, São Francisco University, Brazil The information and communication technologies (ICTs) present in the Brazilian education system determine the development of technology literacy among teachers and students, which can be measured by ICT performance. The Technology Performance Scale (EDETEC) is a self-reporting psychometric instrument to verify what the students’ conceptions are about ICT and their performance in using technology tools. Considering the necessity of the acquisition of both technology literacy and reading comprehension skills to use ICT resources, this study aimed to know the ICT performance, reading comprehension achievement, and the possible relations among them. The participants were 63 Brazilian students from K10 and K11. The EDETEC and Cloze Test with options were applied by school and grade. The best ICT performance referred to the concept and productivity tools factor (F2), and the ANCOVA (analysis of covariance) statistic test identified the influence of the grade and genre in it. There was positive correlation between reading comprehension and EDETEC. Chapter 15 Implementation of Efficient Proactive Computing Using Lazy Evaluation in a Learning Management System ........................................................................................................................... 258 Denis Zampunieris, University of Luxembourg, Luxembourg Zampunieris (2006) proposed a new kind of learning management system, proactive LMS, designed to help users to better interact online by providing programmable, automatic, and continuous analyses of the users’ actions, augmented with appropriate actions initiated by the LMS itself. The proactive part of this LMS is based on a dynamic rules-based system. However, the main algorithm the authors proposed in order to implement the rules-running system suffers some efficiency problems. This chapter proposes a new version of the main rules-running algorithm that is based on lazy evaluation in order to avoid unnecessary and time-costly requests to the LMS database when a rule is not activated, that is, when its actions part will not be performed because preliminary check(s) failed.

Chapter 16 EVAWEB V2: Enhancing a Web-Based Assessment System Focused on Non-Repudiation Use and Teaching ............................................................................................................................... 265 A. I. González-Tablas, Universidad Carlos III De Madrid, Spain A. Orfila, Universidad Carlos III De Madrid, Spain B. Ramos, Universidad Carlos III De Madrid, Spain A. Ribagorda, Universidad Carlos III De Madrid, Spain Security is one of the main problems in Web-based assessment systems, particularly in guaranteeing the non-repudiation of test submissions. The authors have developed EVAWEB, a Web-based assessment system that addresses this issue by using digital signatures. Moreover, the use of this technology in EVAWEB provides a real context to students for learning how digital signatures work. This chapter focuses on the enhancements that have been incorporated into EVAWEB in order to develop an improved second version of the system. Chapter 17 SOA-Frameworks for Modular Virtual Learning Environments: Comparing Implementation Approaches ............................................................................................ 277 Fredrik Paulsson, Umeå University, Sweden Mikael Berglund, Umeå University, Sweden A general SOA framework for Virtual Learning Environments, based on the VWE Learning Object Taxonomy, is suggested in this chapter. Five basic and general services are suggested for implementation of modular Virtual Learning Environments. The design of the service framework was tested by implementation in two prototypes, using two different approaches where a Java-RMI based implementation was compared to a Web Service (SOAP) based implementation. By implementing the VWE Learning Object Taxonomy and the VWE SOA framework, the prototypes showed that a level of modularity, similar to the level of modularity of Learning Objects, could be achieved for the Virtual Learning Environment as well. Using the VWE Learning Object Taxonomy, this was accomplished by including the learning content and the Virtual Learning Environment into the same conceptual space. The comparison of the prototypes showed that the Web Service approach was preferred in favor of the Java-RMI approach. This was mainly due to platform neutrality and the use of the http-protocol. The study was supplemented by an analysis of the two approaches in relation to a third, REST-based approach. Chapter 18 Towards Automated Specifications of Scenarios in Enhanced Learning Technology ........................ 294 A. Rius Gavidia, UOC Open University of Catalonia, Spain M. A. Sicilia Urbán, UAH University of Alcalá, Spain E. García-Barriocanal, UAH University of Alcalá, Spain G. Macarro Palazuelos, UAH University of Alcalá, Spain Recent standardization efforts in e-learning technology have resulted in a number of specifications, however, the automation process that is considered essential in a learning management system (LMS) is a less explored one. As learning technology becomes more widespread and more heterogeneous, there

is a growing need to specify processes that cross the boundaries of a single LMS or learning resource repository. This chapter proposes to obtain a specification orientated to automation that takes on board the heterogeneity of systems and formats and provides a language for specifying complex and generic interactions. Having this goal in mind, a technique based on three steps is suggested. The semantic conformance profiles, the business process management (BPM) diagram, and its translation into the business process execution language (BPEL) seem to be suitable for achieving it. Chapter 19 Bridging the Gap between Instructional Design and Double-Loop Learning .................................... 305 Howard Spoelstra, Open University of The Netherlands, The Netherlands Ellen Rusman, Open University of The Netherlands, The Netherlands Jan van Bruggen, Open University of The Netherlands, The Netherlands Rob Koper, Open University of The Netherlands, The Netherlands Maristella Matera, Politecnico di Milano, Italy The implementation of double-loop-learning-based educational scenarios in instructional design in workflow-like e-learning systems appears to be showing a gap; whereas the former assumes that processes can be reflected upon and can be modified or amended by the learners, the latter only predefines a limited set of rigid instructional processes. However, an important advantage of instructional designs implemented in workflow-like e-learning systems using modeling standards is the ease with which they can be exchanged with other (educational) institutions. The workflow environment described here aims to make leaner reflection and change to instructional processes feasible while maintaining portability. The authors present a description of the implementation of the educational scenario of the virtual company in their workflow environment that makes use of dynamic workflow processes. Learners are provided with process building blocks, called “atomic actions,” which they can use to create and revise processes on the fly, thus supporting double-loop learning. Chapter 20 The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD) ............................................................................................................................. 316 Evangelos Triantafillou, Center of Educational Technology, Greece Elissavet Georgiadou, Center of Educational Technology, Greece Anastasios A. Economides, University of Macedonia, Greece The introduction of mobile devices into the learning pedagogy can compliment e-learning and e-testing by creating an additional channel of assessment with mobile devices. The current study describes the design issues that were considered for the development and the implementation of a CAT on mobile devices, the CAT-MD (Computerized Adaptive Testing on Mobile Devices). The system was implemented in two phases, where initially, a standalone prototype application was developed in order to implement the architecture of the CAT-MD. After a formative evaluation, the second phase took place, where a server-based application was developed in order to add new functionalities to the system so that CATMD can be an effective and efficient assessment tool that can add value to the educational process. The mobility of the CAT-MD eliminates the need for a specialized computer lab, as it can be used anywhere, including a traditional classroom.

Chapter 21 Integrating Awareness Mechanisms in Web-Based Argumentative Collaboration Support Environments ........................................................................................................................ 327 Manolis Tzagarakis, Research Academic Computer Technology Institute, Greece Nikos Karousos, Research Academic Computer Technology Institute, Greece Nikos Karacapilidis, University of Patras, Greece Much research has been performed on how computer-based technologies might facilitate awareness among cooperating actors. However, existing approaches in providing awareness services prove to be inadequate in data-intensive instances of argumentative collaboration. Moreover, they fail to address the needs of dynamic, web-based communities. In this context, this chapter presents a list of awareness mechanisms that have been integrated in an innovative web-based collaboration support tool, namely CoPe_it!, the ultimate aim being to satisfy the requirements associated to the above remarks. The proposed mechanisms are described and elaborated with respect to various awareness types reported in the literature. Compilation of References ............................................................................................................... 341 About the Contributors .................................................................................................................... 379 Index ................................................................................................................................................... 391

xviii

Preface

AbstrAct It is broadly admitted that collaboration can facilitate and augment learning in many ways. Accordingly, collaborative learning models, tools and technologies should receive increasing attention. Aiming at further contributing to this directive, this chapter elaborates a series of issues related to the current state, objectives and future trends of collaborative learning. Particular attention is given to the identification of requirements imposed by contemporary communities and learning contexts, as well as to the consideration of approaches that could significantly contribute to their fulfillment.

IntroductIon The proper exploitation of Web-based technologies towards building responsive environments that motivate, engage, and inspire learners, and which are embedded in the business processes and human resources management systems of organizations, is highly critical. Accordingly, the research field of technology-enhanced learning continues to receive increasing attention. However, as widely admitted, learning evolves, and this is only partly due to the reduced cost of the related software and hardware. The changing nature of our society and organizations, being more and more knowledge-based (Holsapple & Joshi, 2002), has a major impact on how individual and organizational learning is - and will be - delivered or experienced. In this evolving context, it is broadly considered that collaboration is a highly desirable and effective action towards learning. More specifically, argumentative collaboration, conducted by a group of people working towards solving a problem, can admittedly facilitate and augment learning in many ways, such as in explicating and sharing individual representations of the problem, maintaining focus on the overall process, maintaining consistency, increasing plausibility and accuracy, as well as in enhancing the group’s collective knowledge (Koschmann, 1999; Andriessen et al., 2003; Ravenscroft & McAlister, 2006). According to the above, learning and teaching technologies should further focus on (i.e. exploit and augment) the collaboration among learners. Such technologies should support self-directed and personalized learning through the engagement of learners in collaborative learning settings and scenarios (Dillenbourg, 1999; Stahl et al., 2006). Formal and informal learning should be considered in parallel, together with the overall social and organizational context. The appropriate management of the related knowledge resources and user-generated content is another critical issue to be addressed during the development of the contemporary collaborative learning technologies. In any case, these technologies

xix

should make it easier for learners to follow the evolution of an ongoing collaboration, comprehend it in its entirety, and meaningfully aggregate data in order to resolve the issue under consideration. In the context sketched above, this chapter elaborates a series of issues related to the current state, objectives and future trends of collaborative learning. Particular attention is given to the identification of requirements imposed by contemporary communities and learning contexts, as well as to the consideration of approaches that could significantly contribute to their fulfillment.

supportIng collAborAtIon Recent advances in computing and Internet technologies, together with the advent of the Web 2.0 era, resulted to the development of a plethora of online, publicly available environments such as blogs, discussion forums, wikis, and social networking applications (Summerford, 2008). These offer people an unprecedented level of flexibility and convenience to participate in complex collaborative activities, such as long online debates of public interest about the greening of our planet through renewable energy sources or the design of a new product in a multinational company. Information found in these environments is considered as a valuable resource for individuals and organizations to solve problems they encounter or get advice towards making a decision. In any case, people have to go through some type of sorting, filtering, ranking and aggregation of the existing resources in order to facilitate sensemaking. Yet, these activities are far from being easy. This is because collaboration settings are often associated with ever-increasing amounts of multiple types of data, obtained from diverse sources that often have a low signal-to-noise ratio for addressing the problem at hand. In turn, these data may vary in terms of subjectivity, ranging from individual opinions and estimations to broadly accepted practices and indisputable measurements and scientific results. Their types can be of diverse level as far as human understanding and machine interpretation are concerned. They can be put forward by people having diverse or even conflicting interests. At the same time, the associated data are in most cases interconnected, in a vague or explicit way. Data and their interconnections often reveal social networks and social interactions of different patterns. The above bring up the need for innovative software tools that can appropriately capture, represent and process the associated data and knowledge. Such tools should shift in focus from the collection and representation of information to its meaningful assessment and utilization. They should facilitate argumentation, i.e. discussion in which reasoning and disagreements exist, not only discourse for persuasion, logical proof and evidence-based belief (Kunz & Rittel, 1970), the ultimate aim being to support collaborative sense-making (and/or decision-making), and accordingly enhance learning. This can be seen as a special type of social computing where various computations concerning the associated context and group’s behavior need to be supported. Designing software systems that can adequately address users’ needs to express, share, interpret and reason about knowledge during an argumentative collaboration session has been a major research and development activity for more than twenty years. Technologies supporting argumentative collaboration usually provide the means for discussion structuring and visualization, sharing of documents, and user administration. They support argumentative collaboration at various levels and have been tested through diverse user groups and contexts. Furthermore, they aim at exploring argumentation as a means to establish a common ground between diverse stakeholders, to understand positions on issues, to surface assumptions and criteria, and to collectively construct consensus.

xx

While helpful in particular settings, the above solutions prove to be inadequate in cognitively-complex situations.

relAted work Existing approaches to support argumentative collaboration vary in terms of the problem dimension they principally address and the context they particularly target. One category, focusing on a meaningful representation of the related items and their interconnections, builds on the concepts of IBIS (Issue Based Information System), introduced back in 1970 (Kunz & Rittel, 1970). For instance, gIBIS (Conklin & Begeman, 1989) is a pioneer argumentation structuring tool that allows users to create issues, assert positions on these issues, and make arguments in favor or against them. QuestMap (Conklin et al., 2001) resembles to a ‘whiteboard’ where all messages, documents and reference material for a project, together with their relationships, are graphically displayed, the aim being to capture the key issues and ideas during meetings and create a shared understanding in a knowledge team. Hermes (Karacapilidis & Papadias, 2001) builds on concepts from the areas of Decision Theory, Non-Monotonic Reasoning, Constraint Satisfaction and Truth Maintenance, and offers an integrated consideration of classical decision making and argumentation principles. Compendium (http://www.compendiuminstitute.org) is a tool that supports dialogue mapping and conceptual modeling in a meeting scenario, and can be used to gather a semantic group memory. In the same context, Belvedere (Suthers et al., 1995) is used for constructing and reflecting on diagrams of one’s ideas, such as evidence maps and concept maps. It represents various logical and rhetorical relations within a debate and supports problem-based collaborative learning scenarios through the use of a graphical language. In the context of argumentation theory, systems supporting the visualization of argumentation have played a considerable educational role by supporting the teaching of critical thinking and reasoning skills. For instance, Araucaria (Reed & Rowe, 2004) supports the contextual analysis of a written text and provides a tree view of the premises and conclusions, also reflecting stereotypical patterns of reasoning. In the same line, ArguMed (Verheij, 2003) builds on a formal argumentation approach to addresses the issues of argument mapping. The Reason!Able argumentation tool (van Gelder, 2002) provides a well structured and user-friendly environment for reasoning. Through the use of an argumentation tree, a problem can be decomposed to its logically related parts, whereas missing elements can also be identified. MindDraw (http://info.cwru.edu/minddraw/) is another educational software providing assistance in the creation and sharing of visual images of ideas; it enables users to produce maps of causal relationships, and has been proven to be useful for students and learners of all ages. Athena Standard and Athena Negotiator (http://www.athenasoft.org) are two more examples of argument mapping software. Athena Standard is designed to support reasoning and argumentation, while Athena Negotiator is designed to facilitate analysis of decisions and two-party negotiations. The last two systems are efficient argumentation structuring tools, but do not employ any knowledge management features. As derives from the above, the majority of existing argumentative collaboration support systems mainly focus on the expression and visualization of arguments. Generally speaking, existing approaches provide a cognitive argumentation environment that stimulates reflection and discussion among participants. However, their features and functionalities are limited, they pay no or limited attention to data and knowledge management issues, they are mostly tested in academic environments, they are not interconnected with other tools, and they do not efficiently tackle the technological and social dimensions of

xxi

cognitively-complex collaboration. They receive criticism related to their adequacy to clearly display each collaboration instance to all parties involved (usability and ease-of-use issues), as well as to the formal structure used for the representation of collaboration. In most cases, they merely provide a sort of threaded discussion forums, where messages are linked passively. This usually leads to an unsorted collection of vaguely associated positions, which is extremely difficult to be exploited in future collaboration settings. Also important, they do not integrate, in most cases, any reasoning mechanisms to (semi)automate the underlying decision making processes required in a collaboration setting. Thus, there is a lack of alternative formalization, consensus seeking and decision-making support abilities. It has been also admitted that these solutions often require that users carry out activities that do not naturally belong to their work, or they support activities which are infrequent in normal work; thus, such activities are often considered artificial or insignificant by users. As a result, traditional argumentation software approaches are no longer sufficient to support contemporary communication and collaboration needs (de Moor & Aakhus, 2006). There is a need to provide alternative representational features in order to demonstrate a significant effect on the users’ collaborative knowledge building process.

requIrements to be met Design of a smart solution to augment individual and organizational learning during a cognitively-complex argumentative collaborative session is certainly a big challenge. Towards addressing it, we have performed a series of interviews with members of diverse communities in order to identify the major issues they face during their ordinary practices. Twelve communities, coming from three distinct work environment types (management, engineering and learning), and ranging in size from a few decades to a few hundreds of members, were involved (7 of these communities were moderated). In total, 37 people went through a semi-structured interview (the vast majority of them were ‘early adopters’ with more than 5 years hands-on experience with collaborative technologies). All people selected were highly active members in their communities and/or they were having a moderator role. Major issues identified were: •

•

Cognitive overhead and management of information overload: This is primarily due to the extensive and uncontrolled exchange of diverse types of data and knowledge resources. For instance, such a situation may appear during the exchange of numerous ideas about the solution of a public issue, which is accompanied by the exchange of big volumes of positions and arguments in favor or against each solution. In such cases, individuals usually have to spend much effort to conceptualize the current state of the collaboration and grasp its contents. The need to consider an overwhelming amount of resources may ultimately harm a community’s objectives. To avoid that, functionalities for scalable filtering and timely processing of the associated big amounts of data need to be offered. Social behavior: The representation and visualization of social structures, relationships and interactions taking place in a collaborative environment with multiple stakeholders are also of major importance. This is associated to the perception and modeling of actors, groups and organizations in the diversity of collaborative contexts. A problem to be addressed is to provide the means to appropriately represent and manage user and group profiles, as well as social relationships. However, neither relationships nor contexts are static; they are emerging and change over time, which necessitates the development of adaptive services.

xxii

•

•

•

•

Collaboration modes: Interviews indicated that the evolution of collaboration proceeds incrementally; ideas, comments, or any other type of collaboration objects are exchanged and elaborated, and new knowledge emerges slowly. When members of a community participate in a collaborative session, enforced formality may require them to specify their knowledge before it is fully formed. Such emergence cannot be attained when the collaborative environment enforces a formal model from the beginning. On the other hand, formalization is required in order to ensure the environment’s capability to support and aid the collaboration efforts. In particular, the abilities to support decision making or estimation of the present state benefit greatly from formal representations of the information units and relationships. Generally speaking, solutions to the problem under consideration should be generic enough to address diverse collaboration modes and paradigms. Expression of tacit knowledge: A community of people is actually an environment where tacit knowledge (i.e. knowledge that the members do not know they possess or knowledge that members cannot express with the means provided) predominantly exists and dynamically evolves. Such knowledge must be efficiently and effectively represented in order to be further exploited in a collaborative environment. Integration of legacy resources: Many resources required during a collaborative session have either been used in previous sessions or reside outside the members’ working environment (e.g. in e-mailing lists or web forums). Moreover, outcomes of past collaboration activities should be able to be reused as input in subsequent collaborative sessions. The inherent issues of liability and preservation of intellectual rights need particular attention in such cases. Data processing and decision making support: In the settings under consideration, timely processing of data related to both the social context and social behavior is required. Such processing will significantly aid the members of a community to conclude the issue at hand, extract meaningful knowledge and reach a decision. This means that their environment (i.e. the tool used) needs to interpret the knowledge item types and their interrelationships in order to proactively suggest trends or even aggregate data and calculate the outcome of a collaborative session.

The above issues delineate some categories of crucial requirements to be met during the development of contemporary collaborative learning models, tools and technologies. At the same time, it was made obvious that argumentative collaboration, as a particular social computing type, is also knowledgeintensive, in that access to and manipulation of large quantities of knowledge is required.

desIgn Issues This section elaborates a series of issues to be thoroughly considered during the design of contemporary collaborative learning solutions. These concern diverse collaboration aspects and should be taken into account together with the overall collaboration context.

Incremental Formalization When engaged in the use of existing technologies and systems supporting argumentative collaboration, users have to follow a specific formalism. More specifically, their interaction is regulated by procedures that prescribe and - at the same time - constrain their work. This may refer to both the system-supported

xxiii

actions a user may perform (e.g. types of discourse or collaboration acts), and the system-supported types of argumentative collaboration objects (e.g. one has to strictly characterize a collaboration object as an idea or a position). In many cases, users have also to fine-tune, align, amend or even fully change their usual way of collaborating in order to be able to exploit the system’s features and functionalities. Such formalisms are necessary towards making the system interpret and reason about human actions (and the associated resources), thus offering advanced computational services. However, there is much evidence that sophisticated approaches and techniques often resulted in failures (Shipman & Marshall, 1994; Shipman & McCall, 1994). This is often due to the extra time and effort that users need to spend in order to get acquainted with the system, the associated disruption of the users’ usual workflow (Fischer et al., 1991), as well as to the “error prone and difficult to correct when done wrong” character of formal approaches (Halasz, 1988). Complex contexts imply additional disadvantages when using formal approaches. Such approaches impose a structure which is not mature enough to accommodate the management of huge amounts of data coming from diverse sources. They do not allow users to elaborate and digest these data at their own pace, according to the evolution of the collaboration. Instead, a varying level of formality should be considered. This variation may either be imposed by the nature of the task at hand (e.g. decision making, deliberation, persuasion, negotiation, conflict resolution), the particular context of the collaboration (e.g. medical decision making, public policy making), or the group of people who collaborate each time (i.e. how comfortable people feel with the use of a certain technology or formalism). The above advocate an incremental formalization approach. In other words, formality and the level of knowledge structuring should not be considered as a predefined and rigid property, but rather as an adaptable aspect that can be modified to meet the needs of the tasks at hand. By the term formality, we refer to the rules enforced by the system, with which all user actions must comply. Allowing formality to vary within the collaboration space, incremental formalization, i.e. a stepwise and controlled evolution from a mere collection of individual ideas and resources to the production of highly contextualized and interrelated knowledge artifacts, can be achieved.

Visualization and reasoning It has been widely argued that visualization of argumentation conducted by a group of experts working collaboratively towards solving a problem can facilitate the overall process in many ways, such as in explicating and sharing individual representations of the problem, in maintaining focus on the overall process, as well as in maintaining consistency and in increasing plausibility and accuracy (Kirschner et al., 2003). Moreover, it leads to the enhancement of the group’s collective knowledge. For the above reasons, visualization issues should receive much attention while shaping the proposed innovative collaborative learning solutions. Alternative projections of a virtual collaboration space may constitute the ‘vehicle’ that permits incremental formalization of argumentative collaboration (Karacapilidis & Tzagarakis, 2007). A projection can be defined as a particular representation of the collaboration space, in which a consistent set of abstractions able to solve a particular organizational problem during argumentative collaboration is available. With the term abstraction, we refer to the particular data and knowledge items, relationships and actions that are supported through a particular projection, and with which a particular problem can be represented, elaborated and be solved. The foreseen solutions should enable switching from one projection to another, during which abstractions of a certain formality level are transformed to the appropriate

xxiv

abstractions of another formality level. This transformation should be rule-based (and context-specific); such rules can be defined by users and/or the facilitator of the collaboration and reflect the evolution of a community’s collaboration needs. It should be up to the community to exploit one or more projections of a collaboration space (upon users’ needs and expertise, as well as the overall collaboration context). Each projection of the collaboration space should provide the necessary mechanisms to support a particular level of formality (e.g. projection-x may cover only needs concerning collection of knowledge items and exploitation of legacy resources, whereas projection-y may provide decision making functionalities). The more informal a projection is, the more easiness-of-use is usually implied; at the same time, the actions that users may perform are intuitive and not time consuming (e.g. drag-and-drop a document to a shared collaboration space). Informality is associated with generic types of actions and resources, as well as implicit relationships between them. However, the overall context is more human (and less system) interpretable. As derives from the above, the aim of an informal projection of the collaboration space should be to provide users the means to structure and organize data and knowledge items easily, and in a way that conveys semantics to them. Generally speaking, informal projections may support an unbound number of data and knowledge item types. Moreover, users may create any relationship among these items; hence, relationship types may express agreement, disagreement, support, request for refinement, contradiction etc. While such a way of dealing with data and knowledge resources is conceptually close to practices that humans use in their everyday environment, it is inconvenient in situations where support for advanced decision making processes must be provided. Such capabilities require resources and structuring facilities with fixed semantics, which should be understandable and interpretable not only by the users but also by the tool. Hence, decision making processes can be better supported in environments that exhibit a high level of formality. The more formal projections of a collaboration space come to serve such needs. The more formal a projection is, easiness-of-use is usually reduced; actions permitted are less intuitive and more time consuming. Formality is associated with fixed types of actions, as well as explicit relationships between them. However, a switch to a more formal projection is highly desirable when (some members of) a community need to further elaborate the data and knowledge items considered so far. Such functionalities are provided by projections that may enable the formal exploitation of collaboration items patterns and the deployment of appropriate formal argumentation and reasoning mechanisms. A switch to a projection of a higher level of formality should disregard less meaningful data and knowledge items, resulting to a more compact and tangible representation of the collaboration space. This effect is highly desirable in cognitively-complex situations.

Information triage Concepts originally coming from the area of spatial hypertext and the information triage process (Marshall & Shipman, 1997), i.e. the process of sorting and organizing through numerous relevant materials and organizing them to meet the task at hand, should be also exploited towards the proposed collaborative learning solutions. According to the above, users must effortlessly scan, locate, browse, update and structure knowledge resources that may be incomplete, while the resulting structures may be subject to rapid and numerous changes. Information triage related functionalities enable users to meaningfully organize the big volumes of data and knowledge items in a collaborative setting. Spatial hypertext is admittedly a promising approach to address issues in argumentative collaboration environments, as it introduces a visual language in an attempt to take advantage of the humans’

xxv

visual memory and their ability to recognize patterns. Exploiting these human capabilities can greatly reduce the negative impacts of cognitively-complex environments. Spatial hypertext removes the barrier between reading and writing processes enabling articulation of tacit knowledge and ambiguity, as well as establishment of emerged problem-solving strategies. Thus, users are incrementally processing information and are not forced to predefined structural commitments. The corresponding features and functionalities should enable users to create and organize information by making use of spatial relationships and structures, giving them the freedom to express relationships among information items through spatial proximity and visual cues. Such cues could be related to the linking of collaboration items (e.g. coloring and thickness of the respective links) and the drawing of colored rectangles to cluster related items. As highlighted above, the foreseen solutions should permit an ordinary and unconditioned evolution of data and knowledge structures. Such solutions should also provide abstraction mechanisms that allow the creation of new abstractions out of existing ones. Abstraction mechanisms may include: • • • •

annotation and metadata (i.e. the ability to annotate instances of various knowledge items and add or modify metadata); aggregation (i.e. the ability to group a set of data and knowledge items so as to be handled as a single conceptual entity); generalization/specialization (i.e. the ability to create semantically coarse or more detailed knowledge items in order to help users manage information pollution of the collaboration space); patterns (i.e. the ability to specify instances of interconnections between knowledge items of the same or a different type, and accordingly define collaboration templates).

exploitation of legacy resources The foreseen solutions should also reduce the overhead of entering information by allowing the reuse of existing resources. Generally speaking, when legacy resources have to be reused during a collaborative session, complexity is increased. This is not only due to the additional amount of data involved, but also to the conceptual overhead and distractions imposed to the user from switching among applications and environments. One way of dealing with this situation is to enable the ubiquitous access of legacy resources from within the collaboration environment by seamlessly integrating the systems involved. Towards this direction, interoperability among various applications should be carefully considered.

social networking Management of social structures, interactions and relationships is also critical in a contemporary collaborative learning framework. Applications and projects dealing with social relationships mainly support explicit and abstract structures. However, social structures may gain from the expertise of structure domain research, including various structure abstractions or ways for implicit structuring. Another issue to be addressed concerns the elaboration of social relationships in their contexts, that is, how they relate to assets, locations, or change over time. Social network analysis (Castells, 2004) has to be extensively used to find who is depending on whom in a network. Such an analysis will also help to detect hidden hierarchy of social networks. Other requirements of this category concern the (semi)automatic role-

xxvi

specific cognitive mapping for each participant, based on his/her overall behavior, and the development of artifacts-related collaboration metrics. The foreseen solutions should integrate a sophisticated user and role modeling module to tackle the above issues. This module should build on an explicit representation of the notion of user/group, which in turn should be based on a predefined attribute hierarchy. The associated attributes can be domainspecific. They can be categorized, depending on how they are populated and who may modify them, as explicit (their values are provided by users themselves and include personal data such as name, address, birth date, preferences, competencies, skills etc.) or implicit (their values are not provided by users explicitly, but implicitly, by observing their behavior within the system). User/group modeling should be also associated with mechanisms for the acquisition of the abovementioned implicit information of users/groups. These mechanisms will observe and log the operations and discourse moves of users within the system and record them in the user’s profile. Finally, such a sophisticated user and role modeling module should integrate an inference engine. The role of such an engine is to analyze all data present in the profile, together with data from the collaborative workspaces, in order to extract meaningful information about social structures, interactions and relationships. Contrary to most user modeling approaches, this approach pays much attention to community-related aspects (i.e. relationships between individual users and relationships between users and artifacts).

conclusIon This chapter has elaborated a series of issues related to the collaborative learning paradigm, the ultimate aim being to sketch the appropriate tools and technologies that will facilitate and augment it. A series of critical requirements imposed by contemporary communities and learning contexts have been identified, while approaches that could significantly contribute to their fulfillment have been discussed. We argue that the proper tuning and integration of these approaches is able to fully support the evolution of a cognitively complex (and/or data intensive) collaboration, while it provides the means for addressing the issues related to formality in collaborative knowledge building and learning systems. The foreseen solutions support argumentative collaboration between people and groups, enable social feedback, and facilitate the building and maintenance of social networks. By no means, one would argue that the list of issues discussed in this chapter covers fully the diversity and complexity of the research field under consideration. Related works, derived from other perspectives, should be also taken into account during the development of innovative collaborative learning systems (see, for instance, (Amy, 2003), (Dimitracopoulou, 2005) and (Kamtsiou et al., 2006)). Furthermore, the improvement of collaboration among learners should not be considered as the sole research direction towards further enhancing web-based learning. The augmentation of the quality (not the quantity) of e-learning material and the establishment (and global adoption) of e-learning standards consist two other essential directions to be thoroughly investigated. Nikos Karacapildis, Editor University of Patras, Greece

xxvii

reFerences Amy, W. (2003). Supporting Electronic Discourse: Principles of Design from a Social Constructivist Perspective. Journal of Interactive Learning Research, 14(2), 167-184. Andriessen, J., Baker, M., & Suthers, D. (2003). Argumentation, computer support, and the educational context of confronting cognitions. In J. Andriessen, M. Baker, & D. Suthers (Eds.), Arguing to learn: confronting cognitions in computer-supported collaborative learning environments (pp. 1-25). Dordrecht, The Netherlands: Kluwer Academic Publishers. Castells, M. (2004). The Network Society: A Cross-cultural Perspective. Northampton, MA: Edward Elgar. Conklin, J., & Begeman, M. (1989). gIBIS: A tool for all reasons. Journal of the American Society for Information Science, 40(3), 200-213. Conklin, J., Selvin, A.M., Buckingham Shum, S. & Sierhuis, M. (2001). Facilitated hypertext for collective sense-making: 15 years on from gIBIS. In Proc. of the 12th ACM Conference on Hypertext and Hypermedia (pp. 123-124). ACM Press. de Moor, A., & Aakhus, M. (2006). Argumentation support: from technologies to tools. Communications of ACM, 49(3), 93-98. Dillenbourg P. (1999). What do you mean by ‘collaborative learning’? In P. Dillenbourg (Ed.), Collaborative Learning: Cognitive and Computational Approaches (pp. 1-19). Oxford: Elsevier. Dimitracopoulou, A. (2005). Designing collaborative learning systems: current trends and future research agenda. In Proc. of the 2005 Conference on Computer Support for Collaborative Learning (pp. 115-124). Taipei, Taiwan, May 30-June 4, 2005. Fischer, G., Lemke, A.C., McCall, R., & Morch, A. (1991). Making Argumentation Serve Design. Human Computer Interaction, 6(3-4), 393-419. Halasz, F. (1988). Reflections on NoteCards: Seven Issues for the Next Generation of Hypermedia Systems. Communications of the ACM, 31(7), 836-852. Holsapple, C. W., & Joshi, K. D. (2002). Knowledge management: A threefold framework. The Information Society, 18, 47-64. Kamtsiou, V., Naeve, A., Stergioulas, L., & Pappa, D. (2006). Future Visions of Technology-Enhanced Professional Learning. In Proc. of the ICALT-2006 Conference (pp. 542-543), Kerkrade, The Netherlands, 5-7 July, 2006. Karacapilidis, N., & Papadias, D. (2001). Computer supported argumentation and collaborative decision making: The HERMES System. Information Systems, 26(4), 259-277. Karacapilidis, N., & Tzagarakis, M. (2007). Supporting Incremental Formalization in Collaborative Learning Environments. In E. Duval, R. Klamma, & M. Wolpers (Eds.), Proceedings of the 2nd European Conference on Technology Enhanced Learning (EC-TEL 2007), Crete, Greece, September 17-20, 2007 (LNCS 4753, pp. 127–142). Berlin: Springer-Verlag.

xxviii

Kirschner, P., Buckingham Shum, S., & Carr, C. (2003). Visualizing Argumentation: Software Tools for Collaborative and Educational Sense-Making. London: Springer. Koschmann, T. D. (1999). Toward a dialogic theory of learning: Bakhtin’s contribution to understanding learning in settings of collaboration. In C.M. Hoadley & J. Roschelle (Eds.), Proc. of the CSCL’99 Conference (pp. 308-313). Mahwah, NJ: Lawrence Erlbaum Associates. Kunz, W., & Rittel, H. (1970). Issues as Elements of Information Systems. Technical Report 0131, Universität Stuttgart, Institut für Grundlagen der Planning. Marshall, C., & Shipman, F.M. (1997). Spatial Hypertext and the Practice of Information Triage. In Proc. of the 8th ACM Conference on Hypertext (pp. 124-133). Ravenscroft, A., & McAlister, S. (2006). Designing interaction as a dialogue game: Linking social and conceptual dimensions of the learning process. In C. Juwah (Ed.), Interactions in Online Education: implications for theory and practice (pp. 73-90). Routledge. Reed, C.A., & Rowe, G.W.A. (2004). Araucaria: Software for Argument Analysis, Diagramming and Representation. International Journal of AI Tools, 14(3-4), 961-980. Shipman, F.M., & Marshall, C.C. (1994). Formality Considered Harmful: Issues, Experiences, Emerging Themes, and Directions (Technical Report ISTL-CSA-94-08-02). Xerox Palo Alto Research Center. Shipman, F.M., & McCall, R. (1994). Supporting knowledge-base evolution with incremental formalization. In Proc. of the CHI ’94 Conference (pp. 285-291). Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. In R. Keith Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (pp. 409–425). New York: Cambridge University Press. Summerford, S. (2008). Web 2.0 for the Classroom Teacher: An Internet Hotlist on Web 2.0. Retrieved Dec. 17, 2008, from http://www.kn.pacbell.com/wired/fil/pages/listweb20s.html Suthers, D., Weiner, A., Connelly, J., & Paolucci, M. (1995). Belvedere: Engaging students in critical discussion of science and public policy issues. In Proc. of the 7th World Conference on Artificial Intelligence in Education, pp. 266-273. van Gelder, T.J. (2002). Argument mapping with Reason!Able. The American Philosophical Association Newsletter on Philosophy and Computers, 2(1), 85-90. Verheij, B. (2003). Artificial argument assistants for defeasible argumentation. Artificial Intelligence, 150(1-2), 291-324.

1

Chapter 1

Virtual Communities of Practices in Higher Education: Which Processes and Technologies? Evidence from a Case Study Gianluca Elia University of Salento, Italy Giustina Secundo University of Salento, Italy Cesare Taurino University of Salento, Italy

AbstrAct This chapter hypothesizes that Virtual Community of Practices (VCoPs) are valuable to Business Schools and Universities because they support effectively the emerging paradigms of just-in-time, action based and collaborative learning. It presents a case study of a VCoPs called “Virtual eBMS” in Higher Education setting, described as a process-oriented model, composed by four main components: The People participating in the community, the Processes enabling the knowledge flows within the community, the Purpose of the community in terms of value created for the Business School, and the Technology facilitating the interactions between the community members. Indeed, from the technological point of view, the community is supported by an integrated Web Learning and Knowledge Management platform, whose functionalities support the corresponding knowledge processes and actions. Some preliminary results expressed in terms of Intellectual Capital will end the chapter, along with the value created for the community members.

IntroductIon The rapid, discontinuous and non linear changes of today’s economy, their qualitative and quantiDOI: 10.4018/978-1-60566-938-0.ch001

tative leaps (flux), the technological revolution, the collapse of time and space, and the increase of complexity are affecting not only the business environment, but also the education. If the new tasks is to educate students for highly dispersed, flexible, unstable organizations, with great emphasis

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Virtual Communities of Practices in Higher Education

on value reinventing processes, the educational community must increasingly address issues of identifying, understanding and articulating information, experience and knowledge (Baets & Van der Linden, 2003). New styles of learning approaches characterized by efficiency, just in time delivery, solutions orientation, knowledge applications and anywhere access based on internet based learning process are arising (Maureer & Sapper, 2001). The paradigm’s shifts in management education require that students are not simply passive recipients of expertise but rather co-creators of their Just in Time and action learning. Learning is more characterised by interpretation, experimentation, problem solving than description and analysis. It’s a journey through the world in which individuals live, and through networks of self-knowledge and self-development (Baets & Van der Linden, 2003). Hence the learning environment should be considered as a place where different stakeholders (program coordinators, faculty, executives, director, corporate sponsors, advisory board) and students mutually engage in developing new understanding, approaches and unbounded sets of perspectives. These conditions trigger a rethink of the traditional Business Schools and Universities models: new organizational forms based on Virtual Communities of Practices (VCoPs) are strongly recommended. There is no doubt that the concept of VCoPs is relevant in order to discuss learning approaches in Higher Education. Wenger’s approach gives us the possibility to analyse learning as a social practice that goes on at the micro-social level, largely through engagement in the tasks at hand (Lave & Wenger, 1991). Starting from the above considerations, this chapter is aimed at: •

2

defining an Organisational and Technological VCoPs model supporting all the knowledge management (KM) processes in higher education setting;

•

describing an integrated Web Learning and Knowledge Management system aimed at enhance learning experiences and research practices.

In order to address these points, at first we reviewed the CoPs literature to present VCoPs as relevant organizational model for emerging learning approach in Higher education setting; then we propose an integrative model of VCoPs named “Virtual eBMS”Community, organized along the four main components People, Processes, Purpose and Technology, as a result of an empirical study of a higher education community, the Euro- Mediterranean Incubator at Scuola Superiore ISUFI – University of Salento (Italy). Finally some results will be presented in terms of value created by the “Virtual eBMS” Community for the education and research activities of the Incubator.

Vcops As emergIng model In HIgHer educAtIon theoretical background on Vcops The concept of communities of practice (CoPs) has rapidly gained ground in fields such as KM and organisational learning since it was first identified by Lave and Wenger (1991) and Brown and Duguid (1991). CoPs are “groups of people informally bound together by shared expertise and passion for a joint enterprise” (Wenger & Snyder, 2000). CoPs which are groups “of people who share a concern, a set of problems, or a passion about a topic, and who deepen their knowledge and expertise in this area by interacting on an ongoing basis” (Wenger, McDermott, & Snyder, 2002), are seen as an innovative way to manage knowledge and sustain innovation (Lesser & Prusak, 1999). With origins that take us back to the corporations of craftsmen in classical Greece and the guilds of the Middle Ages, where practices were transmitted

Virtual Communities of Practices in Higher Education

mainly verbally, CoPs are not a new phenomenon (Wenger et al., 2002; Wenger & Snyder, 2000). Organizations nowadays show increased interest because of the possibility of taking this old concept into the third millennium. Among the different definitions of CoPs, one of the most appropriate for the purpose of our work is the following: “A groups of individuals who participate in a collection of activities, share knowledge and expertise, and function as an interdependent network over an extended period of time with the shared goal of furthering their ‘practice’ or doing their work better”(Allen, Evans, & Ure, 2005). While CoPs were previously conceptualized as a phenomenon emerging spontaneously in organizations, it is now believed that organizations play a critical role in nurturing these communities (Brown & Duguid, 2001; McDermott, 2000b; Swan et al., 2002; Thompson, 2005; Wenger & Snyder, 2000). CoPs typically involve people who where located in the same vicinity. Within global organizations and emerging forms of organizations, interacting face-to-face on a regular basis is costly and time-consuming. Since information and communication technologies (ICTs) and Internet Technologies can transcend space and time, organizations are increasingly interested in exploiting their capabilities to support CoPs. As a consequence, a new typology of communities emerges: the Virtual Communities of Practices (VCoPs). A VCoPs can be seen as a distributed community of practice, which refers to a group of geographically distributed individuals who are informally bound together by shared expertise and shared interests or work. Such individuals depend on information and communication technologies (ICTs) to connect to each other (Daniel, Schwier, & McCalla, 2003). Moreover, when adding “virtuality” to the concept of CoPs we mean, following the Cohen and Prusak’s definition (Cohen & Prusak, 2001), any work carried out over a distance of time and space, usually with the aid of ICTs . A VCoPs may use a large array of traditional media

(phone, teleconference, fax, etc.) and more or less sophisticated technological tools, such as e-mail, videoconference, newsgroup, on-line meeting space, common database, Website, intranet to establish a common virtual collaborative space. This allows VCoPs people to communicate easily, immediately, universally, and inexpensively. But virtuality represents a great challenge for the knowledge creation processes, because technology can not completely substitute face-to-face interactions. In this sense virtual communities will supplement, not supplant, traditional communities. As White and Pagano (2007) comment, the concept of a CoP has been given currency in Higher Education discourse by practitioners in emergent areas of networked learning. Lave and Wenger’s work did not produce a new pedagogical approach but provided an analytical view of learning, questioning the place of formal education. This shifted the emphasis from the abstract bodies of knowledge taught in formal education towards the ‘situated learning’that occurs as people engage with real-world problems in ways which may already be mediated for them by existing CoPs (Brown, Collins & Duguid 1989; Fox 2005). As Fox (2005) says, networked learning has, “as no educational process has had before, the capability to facilitate and enable new forms of imagined community” (Fox 2005: 108). Lave and Wenger (1991) discuss learning as participation in a social world describing how people learn better in social settings and through social interaction. VCoPs encompass this concept in that they establish a networked environment where the necessary interactions that improve learning can occur (Wenger et al., 2002). In the next two sections we will discuss about some rising approaches in teaching and learning methodologies in Business Schools, by also providing a set of conditions which are common in the context of Business Schools and that we think they can be supported by VCoPs, demonstrating VCoPs as organisational model in Higher Education setting .

3

Virtual Communities of Practices in Higher Education

emerging paradigm shifts in learning Approaches in Higher education Business schools have traditionally provided a reflective learning space, which is a place to absorb information and knowledge. The new learning agenda is more practical and action-oriented rather than theory-driven and discipline-based (Secundo and Passiante, 2007). Emerging trends in teaching and learning approach in Business Schools sustain that learning occurs in informal exchanges and is largely based on constructivism paradigms (Kowch & Schwier, 1997). Concepts of knowledge or information transfer have been under increasing attack. Constructivism paradigm provides a new way of looking the learning process, because it asserts that learners construct knowledge as they interact with the world, strive to make sense of their experiences, and seek meaning. The learner centered approach substitutes the teacher’s centered approach, allowing individuals to be engaged in developing knowledge and cognitive models through a process of co-participation with others members in a shared learning community. Learners become builders of facts in constructing contents of knowledge, rather than passive recipients of knowledge from the instructor. Learning theorists (Lave & Wenger, 1991) have rejected transfer models of knowledge, which isolate knowledge from practice, and have developed a view of learning as social construction, putting knowledge back into the contexts in which it has meaning. This view of learning requires learners engaged in active, constructive, authentic and cooperative learning (Jonassen, Davidson, Collins, Campbell, & Hag, 1995).

Vcops and constructivism in Higher education Constructivists (Duffy & Jonassen, 1992, Barrows & Tamblyn, 1980) believe that the following four

4

conditions must be met for learning to occur in Higher Education setting. We believe that these conditions are addressed in VCoPs and we explain how this happens: •

•

learning must be embedded in complex, realistic, and relevant environments increasing the interaction among learners. The National Research Council emphasizes the importance of community in learning environments and upholds that learning environments should be a combination of learner-centered, knowledge-centered, assessment-centered, and community-centered environments (Bransford, Brown, & Cocking, 1999). VCoPs provide opportunities for learners and researchers to interact with others, engaging learners in learning activities with peers. VCoPs members generate an increase information flow and new ideas through interactions. These methods include: asking and answering questions, chatting with experts, problem solving, resource and information sharing, connecting with other VCoPs, creating sub-communities around special interest topics and sharing best practices. These increases in information and ideas are powerful tools that benefit VCoPs members and business schools. social negotiation must be provided as an integral part of learning: other researchs have also reinforced the idea that knowledge accrues through social interaction and cultural experience, and that learning and social negotiation are inseparable practices. Learning theorists indicate that people learn by co-constructing their knowledge with the help of experts and peers in a situated context. They assert that by doing so, people are elevated to new planes of knowledge and awareness (Cole & Engestrom, 1993; Driscoll, 2000; Lave & Wenger, 1991; Leont’ev, 1981; Luria, 1976; Rogoff

Virtual Communities of Practices in Higher Education

•

•

& Wertsch, 1984; Salomon & Perkins, 1998; Scribner, 1985; Vygotsky, 1987). As VCoP members interact and learn from each other, they meet these social and cultural expectations. learners must be encouraged to own their learning and basing on his/her existing knowledge. Ownership as it relates to learning means that people are aware of what knowledge they need and are involved in satisfying those needs (Driscoll, 2000). Therefore, learners must take control of their own learning by seeking for answers to their questions and solutions to their problems. This is exactly what happens in informal learning exchanges – individuals decide they need to know something to do their work while they are doing their work and take steps to learn it (Brandenburg & Binder, 1999; Sorohan, 1993; Weintraub; 1995). Individuals often join CoPs to search for knowledge they lack or to share knowledge they have. Members of CoPs help one another discover knowledge and solve problems by taking responsibility for their own and other’s Knowledge. learning should be just-in-time and should provide context-specific solutions to problems. Everyday experience, not theory, helps people solve a problem or achieve a goal. The logic is thus reversed: application and experience are not a means to learning, but end from which learning originates involving observation, reflection and systematization. Just-in-time and ondemand learning processes are appropriate for employees to develop competencies required in the workplace. According to Kolb (1984) the first step in learning is to motivate learners; learning is no longer considered as a classroom-specific activity, but is regarded as a continuous on-going process where the pivot actors of the process are the learners with specific competencies to

acquire. The philosophy is that learners learn best not only by receiving knowledge but also by experimenting it, interpreting it, and learning through discovery while also setting the pace of their own learning. In this perspective, faculty acts as mentor of the learning process. Mentor guides the learning process presenting the complex problem to solve, introducing the topics and monitoring the learner in their processes. VCoPs members can access then needed resources at any time and from any place. The ability to identify solutions to problems increases learners’ performance capabilities. Recent developments in the design of web based tools for communication and problem solving suggest new way of delivering courses, based on collaborative and skills-based learning. In the process of getting and giving answers to solve problems, VCoPs members learn how to acquire what they need to know and do. Thus, members of VCoPs become aware of their learning processes as they learn, they became self directed learners. In summary, VCoPs enable an efficient and effective process of learning through the sharing of knowledge with a wide range of members, and enhance action learning process which is widely recognized in the literature as essential for managerial competency development (Gibb, 1997, Gorman et al., 1997, Deakins & Freel, 1998).

reseArcH desIgn research questions Starting from the above consideration, in our work we address the following questions: •

Can we define an integrated organisational and technological VCoPs model that will

5

Virtual Communities of Practices in Higher Education

•

support all the knowledge management processes in Higher Education setting? Is it possible to develop an integrated Web Learning and Knowledge Management platform enhancing learning opportunities for education and research practices?

For answering to these questions, we propose an integrative conceptualization of VCoPs model, supported by learning processes, as an innovative way to apply KM to Higher Education. The model named “Virtual eBMS”Community is the result of an empirical study of a Higher Education community, the Euro-Mediterranean Incubator (hereafter Incubator), at the Scuola Superiore ISUFI, University of Salento, Italy. The Incubator is an advanced education and research center branded “Business Innovation Leadership”. The brand refers to the integration of Innovation, Learning, Leadership and Change Management processes, enabled by the Information and Communication Technologies (ICTs). The Incubator was established in 2008 after a 9-year experimentation in which the cross-disciplinary and scientific identity, as well as the international academic and corporate relationships, were consolidated. The mission of the Incubator is to facilitate the diffusion of digital, organizational and strategic innovation in private organisations and public institutions with a specific focus in Southern Mediterranean countries through human capital development programs, joint research activities, pilot projects and prototypes development. The Incubator is today the central node of a network of competence centres localized in Jordan, Morocco and Tunisia, created in partnership with local academic and industrial institutions, and opened to other Southern Mediterranean Countries. The close relationship between Italy and other countries has been recognized as the critical success factors in order to assure learners a deep understanding of the business reality of their origin countries and for developing a CoPs interacting on virtual basis.

6

research method For the study, we applied a participative observation (Yin, 1994), because we observed the Incubator community, while taking part in it, from November 1999 till November 2008. According to the methodology chosen, researchers actively participated in the Incubator meetings of facilitators that initiated the community research, in the education activities and learning processes. In the first phase of the research the focus of our data collection was on the expectations of the members for developing a community model, and in a later phase on aspects that were learned from belonging to the community, as well as on the value created by the community. Individuals who were either involved or familiar with the initiative were identified and interviewed using a semi-structured questionnaire. The rationale for conducting these types of interviews was to draw rich, contextual details which could not have been elicited via closed ended survey instruments. These interviewees included management staff, core groups members, who represented the leadership of the community, community members and teaching staff who where not directly part of the community but were the intended recipients of outputs generated by the community. In particular, 9 researchers, 5 executive staff, 70 students, the Director and 6 Laboratory’s Coordinators and 10 teaching staff were involved. The involvement of such a wide variety of stakeholders allowed data to be obtained from multiple levels and perspectives.

deVelopIng A Vcops model In HIgHer educAtIon: tHe “VIrtuAl ebms” cAse According the methodology chosen, we developed the model of VCoPs, as the output of the experiences of the Incubator launched in 1999. We named our community “Virtual eBMS” community.

Virtual Communities of Practices in Higher Education

Table 1. Knowledge domains for the “Virtual eBMS” knowledge base Knowledge category General purpose

Knowledge Domain Internet-Worked Global Business Organisational Learning, Innovation and Leadership Internet Business Management Strategic Management and Entrepreneurship Business Innovation Leadership e-Business Design and Implementation

Specific context

e-Tourism e-Agrifood Collaborative Product design in Aerospace e-Government

For the purpose of our study, we define the “Virtual eBMS”Community as a web based integrated learning environment, in which thinking, studying and acting are strongly correlated to reinforce and improve the effectiveness of the knowledge creation processes, through action learning projects, to invent new ICT-based Business Configurations. The basic values of the “Virtual eBMS” Community are: •

•

Networking. Wide collaboration and international mindset represent two important facilitators to activate and sustain learning processes and knowledge flows. They are strongly enabled by the Information and Communication Technologies (ICTs) that are changing the way people live and work, as well as the way companies and institutions organize themselves. They allow developing an international network of learners, companies and organisations. Cross-disciplinarity. Knowledge should be created through a cross-disciplinary approach integrating Business Management and ICT Management issues, supported by experiential laboratories, where participants think, work and learn together. The Knowledge sources in the “Virtual eBMS”

•

•

are organized around knowledge domains, shown in Table 1 (both general purpose and specific context) defined according to the education and research activities of the Incubator. Diversity and Internationality. The “Virtual eBMS” community strongly encourages a multicultural environment based on mutual respect and trust because innovation springs from comparison and exchange of different experiences, background and language. Public and private Partnerships. This “hybrid” typologies of partnership with national and international research Institutions and Companies represent a strategic lever to assure competence growth and the internationalization of higher education and research activities.

The “Virtual eBMS” Governance Structure is a mid level control from leaders and coordinators. In the “Virtual eBMS”, community coordinators are well respected members of the community: the Director, the Executive Staff and the eBMS Laboratories’ coordinators. Their role is to keep the community alive, connecting members with each other, helping the community to focus on important issues, and bringing new ideas when

7

Virtual Communities of Practices in Higher Education

Table 2. The “Virtual eBMS” Community Model. People Staff; Student; Lecturer; Partner; Testimonial; Alumni; Visitor.

Processes Knowledge organisation

Purpose Value for Higher Education processes.

Knowledge creation Knowledge application Knowledge sharing Technology Virtual eBMS platform

the community starts to loose energy. Moreover, the community coordinators maintain, organize and distribute the central knowledge to the other members.

the “Virtual ebms” community model’s components The integrated model of the “Virtual eBMS” community is composed of 4 main elements: •

•

•

•

People, i.e. the participants taking part in the community as organizational assets and knowledge owners / distributors; Processes, i.e. the knowledge flows and learning processes characterizing the life of the community; Purpose, i.e. the output of the community in terms of value created by the community’s members; Technology, i.e. the technological platform integrating Web Learning and Knowledge Management services enabling a participative learning environment where the activities take place simultaneously.

The model explains the KM processes (i.e. Knowledge Creation, Knowledge Organisation, Knowledge Application, Knowledge Sharing) as they happen in a VCoPs. Table 2 presents the complete view of the “Virtual eBMS” Community model.

8

All the elements showed in Tab. 2, lays at tdifferent levels, but that interact bi-directionally and continuously, enabling the community people (INPUT) to benefit from a variety of value (OUTPUT) through the continuous interactions of the KM processes. PEOPLE: The totality of the members belonging to the “Virtual eBMS” community can be described according the typology shown in Tab. 3. PROCESSES: The main processes of the “Virtual eBMS” Community are knowledge sharing, knowledge organization, knowledge application, and knowledge creation. The knowledge sharing retrieves knowledge from the organisational memory and makes it accessible to the users. Individuals, teams and laboratories often share ideas, opinions, knowledge and expertise in meetings held in face-to-face format or virtually. The knowledge organisation stage takes the nuggets of knowledge and classifies them and adds them to the organisational memory. Much of this knowledge can be represented in electronic form as expert systems. This is where even tacit, intangible knowledge assets are transformed to tangible one. Knowledge application allows the utilization of the existing and retrieved knowledge in different contexts and situations. The “Virtual eBMS” approach to knowledge creation is based on a self-organizing kind of learning, derived from direct experience, as it is exposed in Kolb’s theory of experiential learning framed into the Constructivism approach. Ac-

Virtual Communities of Practices in Higher Education

Table 3. People of the “Virtual eBMS” community Members typology

Profile

Staff

Researcher, professors and administrative staff of the institution

Student

Students attending the advanced education activities of the institution

Lecturer

Researchers or professors from other institutions

Partner

People of industries or universities involved in joint activities

Testimonial

Outstanding people invited for conferences, talks and meetings

Alumni

People that attended with success educational program of the institution

Visitor

People that occasionally visit the community

cording to Kolb (1984), the knowledge creation processes are expressed in terms of: •

•

• •

Concrete Experience: learners are involved in an active exploration of experience used to test out ideas and assumptions rather than to obtain practice passively. Reflective Observation: Learners must selectively reflect on their experience in a critical way rather than take experience for granted and assume that the experience on its own is sufficient. Abstract Conceptualization: Learners create theories to explain one’s observations. Active Experimentation: Learners use theories to solve problems and make decisions.

PURPOSE: The “Virtual eBMS” community purpose is to enhance intellectual capital creation. Intellectual Capital could be framed into three interdependent elements: human, social, and structural capital (Seeman et al., 2000). Human capital is defined as knowledge, skills and attitudes created in people. Social Capital reflects the ability of groups to collaborate and to work together and is a function of trust. Structural capital is usually defined as buildings, software, processes, patents, organization’s image and values, information system, databases. The human capital component is the most important among the three, because it can create the others. For this

reason, the “Virtual eBMS” community sponsors the continuous development of its people’s managerial competencies (mainly postgraduate students and researchers) through their participation in research activities strongly integrated with Higher Education programs. By managerial competencies we adopt the following definition: a bundling of strategic resources and intellectual technologies underlying managerial roles and practices, and processes to understand, connect and exploit them in a uniquely competitive way (Baets & Van der Linden, 2003). TECHNOLOGY: The technological platform supporting the “Virtual eBMS” community has been conceived as an integrated and completely web-based platform providing Web Learning, Knowledge Management and e-Business services. Its main objective is to support the “Virtual eBMS” learning community, both from the organizational and the technological point of view.

the components and Functionalities of the “Virtual ebms” technological platform The “Virtual eBMS” technological platform represents the technological system of the “Virtual eBMS” community. It allows the definition of a dynamic Virtual Learning Environment in which to build rich and effective learning experiences (Romano et al., 2001) for all the members of the community, supporting the constructiv-

9

Virtual Communities of Practices in Higher Education

Figure 1. The logical architecture of “Virtual eBMS” system BPR Lab

Knowledge Management

Web Learning

Knowledge Mining Lab

“Virtual eBMS” system

Knowledge

External Intranets

Web Learning

e-Business

Management Web Portals

ist approach to learning. Such integrated Web Learning and Knowledge Management platform enables problem-solving, decision-making, selfpace learning attitudes and capabilities, both in competency development and life-long learning initiatives. The system has been designed and developed under a two years project founded by the Italian Ministry of University and Research. In 2006 the project got the “2006 Brandon Hall Excellence in Learning Award”, since it was recognized as one of the three worldwide best projects in learning technology. Such complex system has been created by integrating different market products and some components developed ad hoc, by using Java based integration framework and open source middleware. Figure 1 shows the logical architecture of the “Virtual eBMS” system, and the links that could be created with other external resources (portals, labs, intranets), since the architecture is completely web based. The services architecture of the”Virtual eBMS” system is articulated into five different and integrated functional components:

10

Multimedia Lab

•

•

knowledge management component, for an efficient and effective management of individual, groups, as well as organizational knowledge. The main services for this component are: Document Management, Content Management, e-Library, Search & Retrieval, Community, Web Mining, Recommendation Engine and, finally, the Semantic Navigator that allows an explicit surfing of conceptual maps and taxonomies by ensuring a gradual and continuous transferring of both tacit and explicit knowledge to the final users. project management component, for a flexible and integrated management of research and training projects allowed by a strong integration with the Document Management system (to ensure the exchange and reuse of tacit knowledge) and the Web Learning component (to ensure the development of ad-hoc competences required to execute a specific task of the project). The main services for this component are: Phase and Activity Management, Workflow Definition, Reporting, Recommendation.

Virtual Communities of Practices in Higher Education

•

web learning component, for an efficient and effective management of individual, groups, as well as organizational learning. Beside the delivery of traditional course catalogues SCORM based (Curriculadriven approach), the web learning system of ‘Virtual eBMS’ allows the delivery of an innovative typology of learning modules, organized according to the Problem-Based Learning (PBL) strategy (Problem-driven approach). Such a PBL strategy is embedded into the system since an ad hoc software layer has been designed and developed to integrate a Learning Management System (LMS), a Virtual Classroom (VC) system, a tool for the delivery in streaming of multimedia Web Seminars, a Recommendation System and a Search

Engine (both implemented ad hoc), as well as tracking and reporting services. collaboration services component, to create and sustain domain-focused as well as interdisciplinary CoPs. The main collaboration services are: individual or group chat, threaded discussion forums, document and file sharing, workgroup and application sharing, Audio - video conference, Virtual meeting. e-Business component, an integrated platform providing Customer Relationship Management (CRM) services, Enterprise Resource Planning (ERP) services, Supply Chain Management (SCM) services, Supply Resource Management (SRM) services, and Business Intelligence (BI) services. All these services are focused on

•

•

Figure 2. The operational architecture of “Virtual eBMS” system WEB LEARNING PROCESSES

WEB LEARNING COMPONENTS

Structured Learning Programs Management (curricula-based and PBL-based)

LMS Extensions for PBL

Skill-Gap Analysis

Unstructured Learning Programs Management (curricula-based and PBL-based)

Competence Profile Management

Curricula Monitoring/Reporting and Personal Competence Tracking

SCORM Objects Web Links

Games

Multimedia Learning Modules KM System Resources

Virtual Classroom

Monitoring/Reporting System Recommendation System

Recommendation (Contents and Modules)

Multimedia Knowledge Objects Exercises and Tests

Search Engines

Multimedia Delivery System

Competence taxonomy

Web Seminars

Discussion Forums

Competence profile Virtual Classrooms

WEB LEARNING CONTENTS

11

Virtual Communities of Practices in Higher Education

the creation, management and support of Digital Marketplaces in Agrifood industry as well as of Destination Management Systems in Tourism industry.

• • • •

Beside these five macro-components, there is a cross services layer, which represents the backbone for the other services, since it allows the access, management, personalization and monitoring of all the other sub-systems. The main cross services are: Authentication, User Management, Personalization, Dashboard (based on Social and Organizational Network Analysis). The web learning component of the “Virtual eBMS” system represent the main sub-system adopted for activating and supporting the competency development processes within the “Virtual eBMS” community. Figure 2 shows its operational architecture, by underling:

•

• • •

the enabled learning processes; the typologies of available learning material; the specific technological components.

As for the learning processes enabled by the Web Learning component, they are: •

• • •

•

Skill Gap Analysis & Competence Profile Management, based on self-assessment processes; Structured Learning Program Management, based on expected competence profile; Unstructured Structured Learning Program Management, based on personal interests; recommendation of PBL curricula, based on personal interests and competence profile; Curricula Monitoring and Reporting, and Personal Competence Tracking.

From the other side, the typologies of content available for the Web Learning component can be classified as follows:

12

SCORM objects; multimedia knowledge objects; multimedia learning modules; competence taxonomy and profile (based on an interdisciplinary approach); knowledge base (composed of external knowledge resources and web links, and documents coming from the knowledge management system).

The above mentioned typologies of content represent the structure of the Learning Base (Baets & Van der Linden, 2003) of the system, which effectively integrates typical Learning Management System resources with Knowledge Management System ones. To have a multitype Learning Base increases the flexibility of the overall system, since it allows triggering a multi-feeding process. Indeed it is possible to feed the “Virtual eBMS” Learning Base in different ways: • • •

learning content created with traditional authoring tools; learning content gathered from the market; learning content created “on fly” with an ad-hoc tool, embedded into the system, that allows even to users with no competencies or experiences on authoring methodologies and technologies to easily and rapidly create useful learning patterns.

Finally, the following tables (Table 4 and Table 5) shows the two main categories of services (LMS – Learning Management System, LCMS – Learning Content Management System) for the Web Learning component of Virtual eBMS, with the list of functionalities provided and the related description. The main strength points of the “Virtual eBMS” System can be referred especially to the Web Learning component, and are:

Virtual Communities of Practices in Higher Education

Table 4. Functionalities provided by the Learning management system of “Virtual eBMS” LEARNING MANAGEMENT SYSTEM (LMS) FUNCTIONALITY

DESCRIPTION

On-line Curricula Access

Access to learning plans, learning modules, learning objects, knowledge objects, web resources and tests.

Classroom Event Catalogue Administration

Classroom events management, calendar, enrolment request management, waiting list management, mentor/testimonial management.

Virtual Classroom Catalogue Administration

Virtual classroom management, calendar, enrolment request management (before and during the on-line session), waiting list management, tutored/moderated sessions, whiteboard, application sharing, chat, audio/video, registered sessions.

Course Tracking

SCORM Objects on-line/off-line tracking. In the off-line mode: course download and attend, synchronization with the e-Learning system.

Collaboration Services

Chat, forum, personal notes, e-meeting rooms, workplace areas.

Enrolment Requests Management

Approval/reject of learner requests of enrolment.

Skill Gap Analysis

Analysis of learner competence-gap based on self-assessment. Textual and graphic visualization of the competence-gap.

Assessment

Assessment of competence acquisition through tests (automatic) and/or deliverables (by mentors)

Deliverable Management

Deliverable submission by learners, deliverable approval/reject by mentors.

Reports and Statistics

Textual and graphic report on learner activities and results, as well as on the usage of resources and learning material.

Notifications and Alerting

Notifications and Alerting system.

Free Search of Leaning Resources

Full-text and taxonomy based search of learning resources (Knowledge Objects, SCORM Objects, Learning Modules, Learning Plans), both from mentors (back-office area) and from learners (frontoffice-area).

Table 5. LEARNING CONTENT MANAGEMENT SYSTEM (LCMS) FUNCTIONALITY

DESCRIPTION

Curricula Management

Creation and management of structured and unstructured learning curricula.

Knowledge Object Management

Creation and management of knowledge objects, metadata management, document search and retrieval, knowledge object references management.

Test Management

Creation and management of tests. Possible question types: single choice, multiple choice, true/ false, fill in the blank, matching.

Learning Module Management

Creation and management of learning modules, by assembling multimedia presentations, Web resources, knowledge objects, SCORM objects, virtual classroom and classroom event activities, exercises and games, deliverable and tests.

Learning Plan Management

Creation and management of learning plans by assembling learning modules, glossary management, multimedia presentation management.

Competence Taxonomy and Competence Level Management

Competences management through three-levels competence taxonomy (domain – competence – learning objective), adaptable to different business contexts.

Pedagogical Relations Management

Creation and management of pedagogical relations with which to create some logical and propaedeutic links between learning content.

Competence Profile Management

Creation and management of learning profiles competence based

Resource Classification and Mapping

Classification of contents and learning material according to the competence taxonomy

13

Virtual Communities of Practices in Higher Education

•

•

•

•

•

•

a systemic architecture integrating a LMS, a LCMS, an authoring tool, streaming audio/video collaborative tools, and a project management tool; an ad hoc developed software layer, integrated in the overall architecture, ensuring the delivery of learning curricula according to the Problem Based Learning (PBL) methodology. a cross-disciplinary competence taxonomy integrated to the KM taxonomy and organized around the Business Management, Technology Management and Internet Business Management domains; an integrated Knowledge+ Base made up of multimedia learning objects (compliant to the SCORM standard), external knowledge resources (i.e. web links) and knowledge objects extracted from the knowledge management repository; a recommendation system that suggests PBL curricula according to the knowledge workers’ competence profile, interests and skill gaps; a search engine that supports both taxonomy-based and problem-oriented searches.

A process And tecHnologY orIented VIew oF tHe “VIrtuAl ebms” communItY In the following table (Table 6) an integrated process and technology oriented view of the “Virtual eBMS” community is offered. For each community process (Knowledge organisation, knowledge creation, knowledge application and knowledge sharing) the main actions performed by the community members are identified. Each process is supported by some functionalities of the Web Learning subsystem of the “Virtual eBMS”, grouped in its own components (LMS – Learning Management Systems, LCMS – Learning Content Management Systems).

14

prelImInArY results The “Virtual eBMS” community model supports the interconnectivity of people belonging to different cultural environment, provides linkages to reference materials, provide feedback from staff, learners, alumni, experts and industry partners, improving personal members’ responsiveness. The model described in this chapter involved progressively in the last nine years a community of almost 150 young talented people selected between 450 applicants. These people participated in different international master edition (9 in total) and they came from the Southern Mediterranean Countries (mainly from Morocco, Jordan, Tunisia and Egypt). More than 450 mentors, speakers and testimonials, mostly coming from Europe, North-America and Japan, have been involved in the learning activities through seminars, lectures, mentorship and tutorship activities, testimonies. Daily, about 35 researchers and 60 students share knowledge within the community. All these people (human capital) contributed to feed and sustain the “Virtual eBMS” community. The people belonging to the “Virtual eBMS” community have also contributed to develop the structural capital – what is usually defined as buildings, pilot applications, projects, software, processes, organization’s image and values, information systems, databases. These act at the same time as enabling factors and as catalysts of the learning-in-action environment of the school. Specifically, community members are involved in providing solutions, in creating new knowledge assets and technological artifacts, as well as in developing new projects. The social capital of the “Virtual eBMS” community is based on trust and commitment of the strong partnerships’ network worldwide, contributing to feed the education and research activities. The network includes research institutions and companies which collaborate over time. They represent both facilitators of the learning processes and knowledge flows, and fundamental

Virtual Communities of Practices in Higher Education

stakeholders involved in the placement phase of Master and PhD programs. Moreover, another dimension in social capital is represented by the loyalty of the alumni community in developing their life long learning processes in collaboration with the Incubator.

conclusIon VCoPs enable an efficient and effective process of knowledge creation through the sharing of knowledge with a wide range of members and enhance experiential learning process essential for action learning. In this chapter we have hy-

pothesized the relevance of VCoPs as emerging organizational model in higher education setting, describing a four components model (People, Processes, Purpose and Technology) applied within the “Virtual eBMS” Community, defined as “a web based integrated learning environment, in which thinking, studying and acting are strongly correlated to reinforce and improve the effectiveness of the knowledge creation processes, through action learning projects, to invent new ICT-based Business Configurations”. The innovativeness of the model is represented by the integration among the organisational processes of the Community and technological platform supporting learning and knowledge flows, with a specific reference

Table 6. A process / technology oriented view of the “Virtual eBMS” Community VIRTUAL EBMS COMMUNITY SUBSYSTEMS COMMUNITY PROCESSESS AND ACTIONS

Learning Management System

Learning Content Management System

KNOWLEDGE ORGANISATION O1. Storing community knowledge O2. Looking for community members and Subject Matter Experts (SME) O3. Developing Knowledge maps O4. Searching in the knowledge and learning base

Deliverable submission by learners, deliverable approval/reject by mentors, tracking and assessment, reporting. Blue pages and expert locator. Course attending and synchronization (for off-line mode).

Creation and management of learning and knowledge objects, competence taxonomy and metadata, learning plans, learning modules, files and multimedia presentations, tests and web resources, virtual meetings and web seminars. Document search and retrieval (full text, taxonomies, knowledge map, and recommendation), automatic indexing, taxonomy categorization, e-library and document management.

KNOWLEDGE CREATION C1. Debriefing after attending seminars and meeting C2. Documenting knowledge into manuals, deliverables, papers, diagrams C3. Create competences according to the personal profile

Self creation and management of learning plans by assembling learning modules, learning and knowledge objects, glossary management, multimedia presentation management. Skill gap analysis based on self-assessment.

Creation of learning plans, learning modules, learning and knowledge objects, files and multimedia presentations, tests and web resources, virtual meetings and web seminars. Textual and graphic report on learner activities and results, as well as on the usage of resources and learning material.

KNOWLEDGE APPLICATION A1. Proving just in time and workplace learning A2. Providing content specific solutions to problems A3. Having access to specialised libraries and on line repository

Problem-based and Competence-based search of learning materials. Access to learning catalogue available for knowledge objects, SCORM objects, learning modules and learning plans.

Context-specific and Competence-based recommendation of learning materials. Integration of heterogeneous systems and knowledge sources.

KNOWLEDGE SHARING S1. Stimulate Dialogue S2. Mentoring, coaching and other forms of action learning S3. Virtual meeting and on line discussions

One to one communications tools (chat, email); one to many communication tools (forum, questionnaires, virtual classroom); one to all communications tools (news, bookmark, whiteboard).

Services for creating and managing the communications tools and enrollment activities.

15

Virtual Communities of Practices in Higher Education

Table 7. The valued added by the “Virtual eBMS” Community Nature of Value Added

Attributes that create Value

Improvement of knowledge creation for individual development

− Reflection process that occur at the end of a virtual meeting consolidates learning − Diversity in membership and less emphasis on hierarchical status increase the probability of team reflection and observation − Active learning as part of a community is more effective than learning alone − Learning opportunities are embedded within the community processes

Greater capacity to deal with knowledge application

− Research activities occur under a set of complex goals rather than tasks − Knowledge leaders are allowed to emerge on the basis of projects developed rather than by single assignments

More effective knowledge sharing among research laboratories

− Voluntary participation implies higher motivation that turn leads to faster, deeper internalization of learning − Long term relationships increase trust

More effective knowledge delivery

− Self paced and not sequential learning via resource-based and technology enhanced activities − Mentors and tutors are just facilitators for learners to experiment new knowledge

Innovative Knowledge organization

− Flexible and non sequential learning, specifically targeted to learning needs − Cross-disciplinary curriculum design and development connected to actions and projects

to the complementarities existing among knowledge management processes and web learning components. The “Virtual eBMS” Community model adds a new dimension to academic research and learning. Benefits of networking and interaction include rendering physical location unimportant and isolation from the peer group less problematic when academics are scattered geographically or work in small institutions. To conclude this chapter, we would like to describe the primary benefits the people receive participating in the “Virtual eBMS” community. In a series of questions, we asked members of the Community to explain the main benefits; almost everyone responded in terms of value created by the community. Table 7 presents the results of the responses in terms of Nature of value added with the Attributes that create value. VCoPs enable an efficient and effective process of knowledge creation through the sharing of knowledge with a wide range of members and enhance experiential learning process which is widely recognized in the literature as essential for action learning. The Virtual eBMS supports the interconnectivity allowing access of differ-

16

ent cultural environment, provide linkages to reference materials, resource people on a global scale, provide feedback from staff, learners, alumni, experts and industry partners, improving responsiveness by monitoring and incorporating lessons learned from the experiences of colleagues, student evaluations, and corporate or other constituent input. Future developments steps of the work will consist in the identification of measurements systems to evaluate the development and the renewal of the community as well as the inclusion in the technological component of the community of the promising Web 2.0 technologies. Indeed, with the emergence of Web 2.0 technologies, learners are able to utilize innovative tools to gain experience through experimentation and action. Some of the Web 2.0 applications like blogs (or weblogs) and wikies have experienced a rapid growth in recent years. These tools are considered to be highly beneficial applications for supporting distributed learning communities. Web 2.0 technologies also allow supervisors to monitor knowledge acquisition and knowledge sharing processes in all the project phase, thus improving collaborative learning within and across the community (O’Reilly,

Virtual Communities of Practices in Higher Education

2005). Despite these assumptions there is still a lack of empirical research to better understand how learning methods can be improved using these applications although an increasing number of researchers and practitioners are now interested in understanding how to use Web 2.0 technologies to support effective learning (Parker and Chao, 2007; Dawnes, 2005). The future research will include these aspects to improve the efficacy of the technological system in supporting all the knowledge management processes as they happen in the community.

reFerences Allen, S., Evans, S., & Ure, D. (2005). Virtual Communities of Practices: vehicles for organisational learning and improved job performance. International Journal of Learning Technology, 1(3), 252–272. doi:10.1504/IJLT.2005.006517 Andrews, N., & Tyson, L. D. (2004). The upwardly Global MBA. Fall 2004. Retrieved from http:// www.strategy-business.com Baets, W., & Van der Linden, G. (2003). Virtual Corporate Universities: a Matrix of Knowledge and Learning for the New Digital Dawn. Kluwer Academic Publisher. Barrows, H. S., & Tamblyn, R. M. (1980). Problem-Based Learning: An Approach to Medical Education. New York: Springer Publishing Co. Brandenburg, D. C., & Binder, C. V. (1999). Emerging trends in human performance interventions. In H.D. Stolovitch & E.J. Keeps (Eds.), Handbook of Human Performance Technology: Improving Individual and Organizational Performance Worldwide (pp. 843-866). San Francisco: Jossey-Bass. Bransford, J., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How People Learn. Washington, DC: National Academy Press.

Brown, J. S., & Duguid, P. (2001). Structure and spontaneity: Knowledge and organization. In I. Nonaka & D. J. Teece (Eds.), Managing industrial knowledge: Creation, transfer and utilization (pp. 44-67). London: Sage. Cohen, D., & Prusak, L. (2001). In Good Company: How Social Capital Makes Organizations Work. Boston, MA: Harvard Business School Press. Cole, M., & Engestrom, Y. (1993). A culturalhistorical approach to distributed cognition. In G. Salomon (Ed.), Distributed Cognitions: Psychological and Educational Considerations. New York: Cambridge University Press. Damiani, E., Corallo, A., Elia, G., & Ceravolo, P. (2002). Standard per i learning objects: Interoperabilità ed integrazione nella didattica a distanza. Convegno internazionale: eLearning: una sfida per l’Universita’ - Strategie Metodi Prospettive. Daniel, B., Schwier, R. A., & McCalla, G. (2003). Social capital in virtual learning communities and distributed communities of practice. Canadian Journal of Learning and Technology, 29(3), 113–139. Dawnes, S. (2005). E-learning 2.0. eLearn Magazine. Association for Computing Machinery, Inc. Retrieved November 9, 2005, from http:// elearnmag.org/subpage.cfm?section=articles&a rticle=29-1 Deakins, D., & Freel, M. (1998). Entrepreneurial learning and the growth process in SMEs. The Learning Organization, 144–155. doi:10.1108/09696479810223428 Driscoll, M. P. (2000). Psychology of Learning for Instruction. Boston, MA: Allyn and Bacon. Duffy, T., & Jonassen, D. H. (1992). Constructivism: New Implications for Instructional Technology. In T. Duffy & D. H. Jonassen (Eds.), Constructivism and the Technology of Instruction: A Conversation. Hillsdale, NJ: Erlbaum.

17

Virtual Communities of Practices in Higher Education

Fox, S. (2005). An actor-network critique of community in higher education: implications for networkedlearning. StudiesinHigherEducation, 30(1), 95–110. doi:10.1080/0307507052000307821 Gibb, A. (1997). Small firms training and competitiveness: building up the small business as a learning organisation. International Small Business Journal, 3, 13–29. doi:10.1177/0266242697153001 Gorman, G., Hanlon, D., & King, W. (1997). Some research perspectives on entrepreneurship education, enterprise education and education for small business management: a ten year literature review. International Small Business Journal, 3, 56–77. doi:10.1177/0266242697153004 Jonassen, D., Davidson, M., Collins, M., Campbell, J., & Hag, B. (1995). Constructivism and Computer-mediated Communication in Distance Education. American Journal of Distance Education, 9(2), 7–26. doi:10.1080/08923649509526885 Kolb, A. (1984), Experiential learning: experience as the source of learning and development. Boston: McBer and Company. Kowch, E. G., & Schwier, R. A. (1997). Characteristics of Technology-Based Virtual Learning Communities. Retrieved September 27, 2003 from http://www.usak.ca/education/ coursework/802papers/communities/community. Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. New York: Cambridge University Press. Leont’ev, A. N. (1981). The problem of activity in psychology. In J. V. Wertsch (Ed.), The Concept of Activity in Soviet Psychology (pp. 37-71). Armonk, NY: Sharpe. Lesser, E., & Prusack, L. (1999). Communities of practice, social capital and organizational knowledge. Information Systems Research, 1(1), 3–10.

18

Lesser, E., & Prusak, L. (2000). Communities of Practices Social capital and Organisational knowledge. In E. Lesser, M. Fontaine, & J. Slusher (Eds.), Knowledge and Communities. Butterworth- Heinemann. Luria, A. R. (1976). Cognitive Development: Its Cultural and Social Foundations. Cambridge, MA: Harvard University Press. McDermott, R. (2000b). Planned spontaneity. Knowledge Management Review, 3(4), 5. O’Reilly, T. (2005, September 30). What is Web 2.0: Design Patterns and Business Models for the next generation of software. O’Reilly. Parker, K. R., & Chao, J. T. (2007). Wiki as a Teaching Tool. Interdisciplinary Journal of Knowledge and Learning Objects, 3, 57–72. Rogoff, B., & Wertsch, J. (1984). Children’s Learning in the Zone of Proximal Development. San Francisco: Jossey-Bass. Romano, A., Elia, V., & Passiante, G. (2001). Creating Business Innovation Leadership. An Ongoing Experiment: The e-Business Management School at ISUFI. Edizioni Scientifiche Italiane, Naples. Salomon, G., & Perkins, D. N. (1998). Individual and social aspects of learning. In P.D.Pearson & A. Iran-Nejad (Eds.), American Educational Research Association, 2(3), 1–24. Scribner, S. (1985). Vygotsky’s uses of history. In J.V. Wertsch (Ed.), Culture, Communication, and Cognition: Vygotskian Perspectives (pp. 119-145). Cambridge, UK: Cambridge University Press. Secundo, G., & Passiante, G. (2007). An innovative approach to creating business leaders: evidence from a case study. Int. Journal of Management Education, 1(3), 214–230. Sorohan, E. G. (1993). We do; therefore we learn. Training & Development, 47(10), 47–55.

Virtual Communities of Practices in Higher Education

Swan, J., Scarbrough, H., & Robertson, M. (2002). The construction of “communities of practice” in the management of innovation. Management Learning, 33(4), 477–496. doi:10.1177/1350507602334005 Thompson, M. (2005). Structural and epistemic parameters in communities of practice. Organization Science, 16(2), 151–164. doi:10.1287/ orsc.1050.0120 Vygotsky, L. S. (Ed.). (1987). The Collected Works of Vygotsky. New York: Plenum Press. Weintraub, R. (1995). Transforming mental models through formal and informal learning: a guide for workplace educators. In S. Chawla, & J. Renesch (Eds.), Learning Organizations: Developing Cultures for Tomorrow’s Workplace (pp. 417-429). Portland, OR: Productivity Press.

Wenger, E., McDermott, R., & Snyder, W. M. (2002). Cultivating communities of practice. Boston: Harvard Business School Press. Wenger, E. C., & Snyder, W. M. (2000, Jan-Feb). Communities of practice: The organizational frontier. Harvard Business Review, 139–145. White, I. K., & Pagano, R. (2007). Making It Stick: The use of Online Discussion Fora to Support Continuing Professional Development in Higher Education Communities of Practice. In Proceedings of the 2nd International Conference on e-Learning, New York, 28-29 June. Yin, R. K. (1994). Case Study Research. Design and Methods. Thousand Oaks, CA: Sage.

19

20

Chapter 2

Exploiting Virtual Environments and Web 2.0 Immersive Worlds to Support Collaborative e-Learning Communities Christos Bouras University of Patras, Greece Eri Giannaka University of Patras, Greece Thrasyvoulos Tsiatsos Aristotle University of Thessaloniki, Greece

AbstrAct The main goal of this chapter is to facilitate educational designers and developers by providing a point of reference for making decisions on how to incorporate 3D environments into the applications they develop as well as for extending their capabilities by integrating more functionality. Therefore, this chapter presents the design principles for virtual spaces, which aim at supporting multi-user communication in web-based learning communities. In addition the implementation of these principles is presented using as point of reference EVE Training Area. This environment constitutes a three-dimensional space where participants, represented by 3D humanoid avatars, have the ability to use a variety of 3D e-collaboration tools for learning together. Furthermore, this chapter presents how these principles could be used as criteria for validating and extending ready Web2.0 Immersive worlds for supporting collaborative e-learning. Finally, collaborative e-learning usage scenarios that could be realized by exploiting collaborative virtual environments are described.

1. IntroductIon Nowadays, the use of Internet has been widely broadened and is being adopted not only for accessing information for news and entertainment but also DOI: 10.4018/978-1-60566-938-0.ch002

for facilitating the creation of on-line communities in order to assist the interaction among individuals that share common interests and goals. These communities are described by the term “virtual communities” for highlighting their “on-line” substance. A key factor for the success and the subsistence of

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

the virtual communities is a strong interest among the people concerned. Such a case could form a group of people that want to share knowledge and learn together and consequently constitute a learning community. A variety of tools and technologies have been developed and used for supporting e-learning communities. The current components, tools and systems available can be divided into three different basic categories as described in the literature (Spellmann et al, 1997; Bouras & Tsiatsos, 2006): a) document-focused web-based training tools, b) meeting-focused tools, and c) three dimensional (3D)- centered multi-user tools, which are based on multi-user Virtual Reality (VR) technology. In particular, the document-focused web-based training tools (e.g., WebCT, www.webct.com) focus on the management of documents and on individual learning. As far as it concerns the meeting-focused tools, they focalize on the support of synchronous communication of a user group, which is independent of place. These tools that can be separated into video-conferencing tools (e.g., Microsoft’s NetMeeting, www.microsoft. com) and synchronous training tools (e.g., Centra Symposium, www.centra.com), offer web-based communication support, where participants are represented by their name and live video picture. Some of the video conferencing tools were designed especially for the purpose of training situations. The approach of these tools is to virtually represent the concept of frontal learning – that is the situation of a lecturer sending information to a group of learners, with rather little feedback and almost no intended horizontal communication among the learners (Koubek & Müller, 2002). A general problem of these tools is the reduced social presence of the participants that are represented in windows, by means of live pictures. Often, these pictures are simple icons that have a low resolution and are quite small. Therefore, participants in such e-learning sessions experience a feeling of distance more than a feeling of group awareness (Kuljis & Lees, 2002). As far as it concerns

multi-user Virtual Reality tools, in their majority, focus on letting each participant experience the existence of other participants as well as the interaction between them. The participants of a 3D virtual session are represented by avatars, which can navigate through 3D environments, and are able to view the actions of all other participants. Multi-user Virtual Reality technology tools, when used as communication media, offer the advantage of creating proximity and social presence, thereby making participants aware of the communication and interaction processes with others. In case that multi-user VR technology is used for supporting collaboration among the users we refer to collaborative virtual environments (CVEs). Multi-user VR technology tools as well as meeting-focused tools could be used for supporting learning communities. However, current e-learning applications have many limitations that should be overcome. Some of the main limitations involve the lack of peer contact and interaction of learners/users working alone and the need for flexible, available tutorial support. In addition, the theoretical advantages of multi-user VR technology are not exploited in an extended manner as they mainly offer text chat communication and users’representation through avatars. For example, advanced communication features, as voice or user gestures are not commonly utilized. The main goal of this chapter is to facilitate educational designers and developers by providing a point of reference for making decisions on how to incorporate 3D environments into the applications they develop as well as for extending their capabilities by integrating more functionality. Furthermore, this chapter presents collaborative e-learning usage scenarios that could be realized by exploiting CVEs. The remainder of this chapter is structured as follows. In Section 2 some basic issues of adopting virtual reality for supporting learning versus traditional methods are presented. Section 3 summarises the related work on VR in education, training and collaboration, while Section 4

21

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

proposes design principles for tools and spaces aiming at supporting learning communities and e-collaboration. Section 5 presents the implementation of 3D collaborative virtual environments used for e-collaboration and e-learning, for demonstrating the way that the principles could be applied. The section that follows proposes collaborative e-learning usage scenarios that exploit multi-user Virtual Reality environments. Finally, some concluding remarks and planned next steps are briefly described.

2. Virtual reality vs. traditional methods According to Kalawsky (1998), there are many areas where VR could be used for supporting education: (a) simulation of complex systems, where the benefit compared to traditional methods is the ability to observe system operations from a number of perspectives, aided by high quality visualisation and interaction; (b) macroscopic and microscopic visualization, where the benefit compared to traditional methods is the observation of system features that would be either too small or too large to be seen on a normal scale system; and (c) fast and slow time simulation, where the benefit compared to traditional methods is the ability to control timescale in a dynamic event. This feature could operate like a fast forward or rewind preview of a video recorder. Other significant characteristics of VR that could be exploited for supporting education are the following: •

22

High levels of interactivity that VR allows: The benefit compared to traditional methods is that most people learn faster by ‘doing’ (Tornincasa, 2001) and the VR system provides significantly higher levels of interactivity than other computer-based systems. Given the fact that the interfaces are intuitive and easy to use, the degree of interactivity could be very beneficial.

•

•

Sense of immersion: Sense of immersion is a powerful characteristic, especially in applications, where the sense of scale is extremely important. For example, architecture is an area where the sense of scale is required for visualising the impact of a building design on the external environment and the inhabitants. Inherent flexibility/adaptability: The inherent flexibility of a VR system arises from the underlying software nature of the virtual environment. A VR system can be put to many uses by loading different application environments. This means that it is feasible to use a VR system for a wide range of learning applications (Kalawsky, 1998).

In Liebregt (2005), a good overview of these systems is presented, showing that there is a wide variety of possible roles for Collaborative Virtual Environments in education: (a) supporting social awareness of students; (b) increasing communication and discussions possibilities on a wide scale; (c) supporting constructivist learning of ecological and cultural concepts; (d) increasing information available to users and possibilities for collaborative culmination of knowledge; (e) making available virtual experiences for learning difficult concepts; and (f) incorporating aspects of direct learning into indirect learning and the other way around. Winn and Jackson (1999) describe various propositions related to the usage of virtual environments in education. Koubek & Müller (2002) believe that four of these propositions are of special interest. The first proposition is that virtual environments create a feeling of presence by techniques, which shift attention from the real world to the virtual world. The second significant proposition is that virtual environments situate learning in a meaningful context. The environment’s “landmarks” play a special role. The third proposition states that collaboration is possible and efficient in virtual environments. Addition-

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

ally, users represented by avatars in the virtual world support the feeling of presence and the joy while learning. Finally, as it becomes possible to learn by interacting with other students and virtual objects in virtual environments in a way similar to the interaction with real people and objects, it becomes important to investigate the design principles that should be adopted by educational designers for designing effectively virtual spaces for e-learning and e-collaboration. The current research on the design of collaborative e-learning virtual environments results in various issues and aspects of such environments. This chapter, later presents a list of design principles for virtual spaces that are focused on supporting collaborative e-learning.

3. related work This section presents an overview of the related work on the usage of VR technology in distance education, learning and collaboration. VR has been exploited in various projects for supporting education, training and/or collaboration. A very good overview of relative projects is presented in Hay et al. (2002). These projects aimed at creating learning environments based on the exploration of various scientific concepts. Concerning education, much research has been done on the exploration of the unique features of VR and their interaction with cognition and learning in highend, laboratory-based projects. Examples are the following: (a) exploration of scientific concepts where 3D models were important to conceptual development; (b) exploitation of VR’s ability to shrink or expand 3D distances to make the models easy to manipulate; and (c) usage of the simulation mode of integrating models into learning environments and capitalization VR’s ability to more accurately present the phenomena to the learner, thus building superior understandings. Furthermore, VR technology has been used in other areas such as military training and medical education and training. Examples are:

NPSNET-IV (Macedonia et al, 1995), Gorman’s Gambit (Weil et al, 2005), VirRAD (Virtual Radiopharmacy, http://www.virrad.eu.org/) European project; Medical Readiness Trainer project (http://www-vrl.umich.edu/mrt/index.html) and CeNTIE project (Hutchins et al, 2005). In addition, multi-user Virtual Reality technology, which allows collaboration among users (and then referred as Collaborative Virtual Environments), integrates networking technology with immersive virtual environments and supports synchronous interaction of multiple users at various locations (Singhal & Zyda, 1999). Multi-user Virtual Reality technology is being used for co-operative work (Dumas et al, 1999), for education and training, for engineering and design, for commerce and entertainment, and is being studied extensively in 3D and time dependent representations of scientific and technical models (Singhal & Zyda, 1999; Hay et al, 2002). VR applications, which are specifically designed to support learning, come in many different forms, from desktop virtual worlds to fully immersive virtual environments (Jackson & Winn, 1999). 3D collaborative e-learning environment adopt ideas of distributed constructionism to allow multiple users to work together in the same virtual space and to provide them with the power to construct shared representations of the topic they investigate. Examples of current applications of Collaborative Virtual Environments in education are: CVE-VM (Kirner et al, 2001), DeskTOP (Portugal et al, 2000), DigitalEE and DigitalEE II (Okada et al 2001; Okada et al, 2003), Viras (Prasolova-Førland & Divitini, 2003), and NICE (Roussos et al, 1997; Johnson et al, 1998). A very good review about Web 2.0 Immersive Virtual environments has been elaborated by Redecker (2008). According to this review Web 2.0 Immersive Virtual Environments like Second life (SL, http://secondlife.com/) or similar online 3D virtual worlds, such as Active Worlds (http://www. activeworlds.com) provide users with a online game-like 3D digital environment to which users

23

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

subscribe (OECD, 2007). SecondLife appears to have a rapidly growing base of 1.3 million “active residents”, representing an increase of 46% in the number of active residents from January 2007, 61% of which are European (Pascu, 2008). According to Calongne (2007) In March, 2007, more than 250 universities, 2500 educators and the New Media Consortium (NMC), with over 225 member universities, museums and research centres, had a presence in Second Life. Furthermore, NMC has conducted a survey (NMC, 2007) among 209 educators using Second Life, and found that the manifold uses of 3D environments for educational purposes are the following: 60% of educators took (43%) or are planning to take (17%) a class in Second Life; 58% taught (29%) or are planning to teach (28%) a class in Second Life. Other activities include:

4. towArds A set oF desIgn prIncIples For VIrtuAl spAces Focused on collAborAtIVe e-leArnIng

• • • • • •

For implementing a functional and effective elearning collaborative virtual environment, the first step is to investigate its main functional features. These functional features should differentiate an e-learning environment from other virtual environments (3D or not), which are designed and implemented for general use. The virtual spaces should be designed in accordance to the concepts introduced by Dourish & Harrison (1996) about space and place: “A space is always what it is, but a place is how it’s used” (p. 69). In addition, according to Dourish & Harrison (1996), design has to deal with some aspects of the “real world”, which can be exploited by virtual spaces for collaboration and learning. The real-world value of the features listed below is that they provide critical cues, which allow individuals to organize their behaviour accordingly (such as moving towards people to talk to them, or referring to objects so that others can find them). Every tool designed for supporting e-collaboration should exploit aspects of space and spatial mechanisms, such as providing identity, orientation, a locus for activity and a mode of control, which can be considered as powerful

supervising class projects and/or activities conducting research in SL class meetings virtual office hours mentoring student research projects student services and support activities.

According to the same survey (NMC, 2007), 8% of respondents taught a real life class entirely in Second Life; 19% are planning to do so. Asked about the potential of Second Life for education, a majority of respondents see a significant or high potential for role-playing (94%), simulation and scenario activities (87%), artistic expression (86%), group work, collaboration and meetings (78%), distance learning programs (74%), team building (73%), conducting training (71%), professional development (68%), and teaching full courses (60%).

24

This section presents the design principles that should be taken into account by designers and developers when designing a virtual space for collaborative e-learning communities. Before defining these principles, the main aspects of Collaborative Virtual Environments as well as the fundamental design elements of collaborative e-learning environments are presented. Issues in the design of Collaborative Virtual Environments in education are also listed and taken into account in the proposed set of design principles.

4.1 main Aspects of a collaborative Virtual environment

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

tools for the design. According to the above, the designer of a CVE should include specific tools and take into account specific aspects in order to support the creation of places by the collaborators/ students. By that way the designer facilitate the emergence of places by the collaborators/students people who is able to create meaning of things by engaging in social interactions. In particular, these aspects are: •

•

•

•

Relational orientation and reciprocity: The spatial organization of the tools should be the same for all participants. Since people know that the world is physically structured for others in exactly the same way as it is for them, they can use this understanding to orient their own behaviour for other people’s use. Proximity and activity: People act, more or less, where they are. They pick up objects that are near, not at a distance; they carry things with them and they get closer to things to view them clearly. An understanding of proximity helps relating people to activities and to each other. The learners/ collaborators in the environment should not be passive but active and able to interact. Partitioning: Following on from the notion of proximity and activity is the notion of partitioning. Since actions and interactions fall off with distance, this distance can be used for partitioning activities and the extent of interaction. Presence, awareness and support of users’ representation: The sense of other people’s presence and the ongoing awareness of activity allow them to structure their own activity, integrating communication and collaboration seamlessly, progressively and easily. The use of avatars for user representation in virtual environment is a key feature for supporting e-collaboration and collaborative e-learning. According to Clark & Maher (2006) “the role of the

lecturer as a facilitator is supported by the visualisation of students represented as avatars in the virtual place” and “the visualisation of students in a location helps the lecturer to gather students to specific locations, which provides a context for discourse in the virtual place”. Therefore, it might be useful to represent the users by avatars that can support mimics and gestures, for supporting virtual and social presence as well as for enhancing the ways of communication among the users with non-verbal communication.

4.2 design elements of a collaborative e-learning environments Additional design elements of a virtual space, which is focused on e-collaboration and e-learning, could be extracted by a generalization of the design elements presented in Bouras & Tsiatsos (2006) (mainly focused on collaborative e-learning using only 3D virtual environments) and based on Dillenbourg’s interpretation of collaborative learning (Dillenbourg, 1999), and Moshman’s interpretation of dialectical constructivism (Moshman, 1982). These design elements are the following: •

•

Situated remote communication by supporting multiple communication channels such as avatar gestures, voice chat and text chat. Remote task collaboration: Distributed environments allow users to collaborate on tasks. This design element could be realized by: ◦ Tools such as manipulation of shared objects, brainstorming board tool, locking /unlocking shared objects, user handling, as well as slide presentation and creation. ◦ Supporting users who have different roles and rights when visiting the environment.

25

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

•

•

•

Remote task support: Remote support by other learners, teachers, moderators and participants. This design element could be realized by uploading material in the virtual space and data sharing. Scaffolding tools: Tools that can support collaborative scenarios as well as support the learners to undertake tasks in the virtual space. This design element could be represented by whiteboard, brainstorming and slide creation tools. For example, the whiteboard tool could support the learner in making a presentation of a task that s/he has undertaken. Similarly, both the brainstorming tool and slide creation could support the learners to exchange and collect ideas for a task that has been assigned to them by the tutor. Representation of the environment by various representation forms, which can range from simple text to 3D worlds.

4.3 Issues for the design of collaborative Virtual environments in education According to test elaborated and presented in Liebregt (2005) there are important issues that should be taken into account when using CVEs and developing CVEs in the future. These issues are listed below: • •

•

•

26

The tutors should be able to guide the learners There is a requirement for natural communication possibilities including realistic avatars and the possibility to use body language It is important to prevent the users from over engagement with subtasks not directly related to the main goal of the CVE It is important to avoid frustration or distraction caused by unnecessarily complex interfaces

Further problems of computer-mediated group learning could be summarised as follows: •

•

Reduced social presence - problem of social and cognitive orientation (Hsi & Hoadley, 1997) ◦ group members tend to feel more as an individual than as a group member ◦ the problem of “virtual group identity” leads to a depersonalisation of the group members ◦ low collaboration is taking place ◦ reduced feeling of social presence, togetherness and group identity Unnecessarily high amount of extraneous load (Sweller, 1988) ◦ split-attention effect: separation of related information sources increases extraneous load ◦ poor exploitation of working-memory capacity due to poor utilisation of prior knowledge (rules and causal connections known from reality cannot be used).

Finally, Kreijns et al. (2002) said that “there are two major pitfalls impeding achievement of the desired social interaction in collaborative e-learning environments: (a) taking social interaction in groups for granted and (b) the lack of attention paid to the social psychological dimension of social interaction outside of the task context.

4.4 design principles Based on the aspects and design elements presented above, this subsection proposes a set of principles for assisting design and implementation of desktop collaborative e-learning environments. Their use is mainly viewed as augmenting rather than replacing in overall existing design principles. They express a new emphasis in the use of these environments rather than a radically distinct set of intentions. These principles are the following:

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

• • • • • • • •

Principle 1: Design to support multiple collaborative learning scenarios Principle 2: Design to maximise the flexibility within a virtual space Principle 3: Augmenting user’s representation and awareness Principle 4: Design to reduce the amount of extraneous load of the users Principle 5: Design a media - learning centric virtual space Principle 6: Ergonomic design of a virtual place accessible by a large audience Principle 7: Design an inclusive, open and user-centred virtual place Principle 8: Design a place for many people with different roles

The above principles are analysed in the following paragraphs.

4.4.1 Principle 1: Design to Support Multiple Collaborative Learning Scenarios A useful tool for collaboration would support the execution of many e-learning scenarios. Examples of such scenarios are brainstorming/roundtable (Millis & Cottell, 1998; Osborne, 1963), think pair share (Lymna, 1981), jigsaw (Aronson et al, 1978), quickwrites / microthemes (Young, 1997), and structured academic controversies (Johnson et al, 1998). An e-learning environment can support many groups of users (i.e. classes) in various subjects, so some scenarios could fit better in a subject than others. Therefore, the tutor should be provided with the ability to choose among various collaborative learning scenarios. Furthermore, a variety of student-centred and collaborative approaches to learning would increase the variation in student activity in formal classes.

4.4.2 Principle 2: Design to Maximize the Flexibility within a Virtual Space Due to the need of multi-functionality within a collaborative on-line synchronous session, it should be possible to quickly re-organise the virtual place for a particular activity or scenario. An approach for increasing flexibility is the division of the virtual environment in smaller areas, so as to allow for specific functions (e.g. various virtual spaces for formal classes, group work, closed meeting, etc). This approach, however, may result in disorientation and increment of the cognitive load of the users concerning the operation of the virtual space. This chapter proposes a design of a virtual space, where the seating in a formal class area will not impede group work, discussions and interaction.

4.4.3 Principle 3: Augmenting User’s Representation and Awareness Both current e-learning systems and collaborative virtual environments lack of awareness. There are two types of awareness: the awareness of other people and the awareness of objects. The suitable combination of these two types of awareness is very critical in e-learning environments in order the users to be aware, not only of the others and the content but also of the e-learning procedure. The goal for satisfying the need of awareness is to concentrate on both the visualization of other users and the representation of their actions on the objects they are communicating about. The collaborative virtual environments support the awareness of other people and their activity effectively. The avatars, along with gestures and mimics, represent not only the users but they also make their activity shared to the rest of participants. In the case of objects’ awareness, collaborative virtual environments can support shared virtual objects and, generally, media that can be integrated in a virtual world such as pictures, audio and video.

27

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Furthermore, documents and/or learning content that cannot be displayed in the virtual world should be supported in an e-learning platform. In addition, the participants should be aware of the number and the identity of the users who view the document each time. Also, the actions on the objects and the documents should be visible from the other users. This could be achieved, for example, by application sharing. Combining gestures, mimics, user representation, audio and text chat communication as well as application sharing provides to the users the ability to share their views, to show the object that they are talking about while other users are also aware of who and for what they talk about.

4.4.4 Principle 4: Design to Reduce the Amount of Extraneous Load of the Users The main objective of an e-learning environment is to support the learning process. Therefore, the users should be able to understand the operation of the learning environment and easily participate in the learning process. The major commands of interfaces should be available in a graphical user interface fashion. Since virtual environments, in their majority, include multiple ways of interaction (i.e. voice chat, text chat, video representation, shared areas), all these functions and tools should be placed in the same window, separating the shared areas from the non shared. It should be possible for the user to see at once the users who participate as well as their contribution.

4.4.5 Principle 5: Design a Media - Learning Centric Virtual Space The virtual space should be enhanced by multiple communication and media layers. Each media type (e.g. text, graphics, sound, etc.) has advantages (Schneider, 1996). The virtual space should integrate many communication channels (such as gestures, voice chat, text chat, etc.) in order to

28

enhance the awareness and the communication among the users. VEs need multiple communication channels but they should be available on a basis of needs and availability, i.e. communication should not become intrusive but people should be able to use the right channel for the right task and a social practice for using different channels must be created (Schneider, 1996). There are three approaches regarding the design of CVEs. The first one (VR-centric view) characterizes CVEs as systems based only on virtual reality technology and nothing else. The second approach, which is a step-up of the VR-centric view, is the adoption of mixed reality systems. In these systems the main user interface is VR and the users can interact with the system navigating only in the 3D world and accessing the rest of media only within the 3D area. The third approach (media centric view described by Robinson et al, 2001) tends to integrate more media in a CVE system. Audio, text, documents, video, etc. are such media. However, in this approach VR is not the access point for the rest of media, and is regarded as one medium among the others. Regarding e-learning, the most suitable approach seems to be the media centric view. However, this approach needs to be extended in order to realise the e-learning scenarios and to satisfy the users’ needs. For supporting a learning centric view we need to take into account the necessary media derived from the above-referred scenarios. Main features and media are the content (learning content), web, virtual reality, video, audio, application sharing and text chat. These media should be integrated in a way that assists the user to learn and to use the system effectively. An example is depicted in Figure 1. E-learning systems supported by collaborative virtual environments should be based on three main categories: Content, Learning Context and Communication Media. Both Web and virtual environments are the media to support the community and the e-learning context giving the users the feeling that they are in the same place, in an

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Figure 1. Learning centric view

easy way. Communication media (text and audio chat, application sharing, message board, etc.) can support the communication and interaction between the users. The main aim of the communication media is to support and offer communication affordances to the users in order to facilitate the transformation of the learning space to a social learning place. Content is the core medium for learning and supporting learners to learn and tutors to teach. The integration of a module that could be used for the creation and management of the learning material is of critical importance. However, for supporting collaborative e-learning effectively, more tools for sharing information should be investigated and implemented, such as a presentation table, where all users can present their own content and have the ability to open it, view it and collaborate on it.

4.4.6 Principle 6: Ergonomic Design of a Virtual Place Accessible by a Large Audience The designers of a virtual place should take into account that a virtual place for e-learning could be used by various individuals with different backgrounds and level of expertise in Information and Communication Technologies. Therefore, the virtual place should be easily accessible and of high usability, even for users, who are not experts on Internet/Web based learning and/or community platforms. In addition, the access to the virtual place should be extremely fast and simple in terms of user registration, software download and installation. In some cases the virtual spaces address multilingual and multicultural audiences. In such a case it is proposed to design a multilingual virtual space that would support a multicultural, diverse community, which is not dominated by one single culture.

29

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

4.4.7 Principle 7: Design an Inclusive, Open and User-Centred Virtual Place A collaborative virtual space should be characterized by the following characteristics in order to support as much users as possible: •

•

•

Inclusive: The virtual place should be accessible as much as possible. Any registration process should be easy, quick and unbureaucratic. Open: Access to the virtual place should not be restricted by the will of a single person or board, but general rules for access should be formulated and guide the moderators, tutors or generally the people who is responsible for it. User-centred: The development of a virtual place should be centred on the users. For each piece of technology implementation, processes should be developed within the user community, which guarantee the continuation and broad implementation of such technology.

4.4.8 Principle 8: Design a Place for many People with Different Roles An e-learning system should support a variety of roles with different access rights. For example, in a collaborative learning scenario the participants could be moderators, tutors, or learners. The virtual space should be designed accordingly for differentiating these roles. For example, the virtual space could provide to the moderator administration and moderation tools, which are not available to a learner. By using these tools the moderator could be able to moderate the communication interaction, to expel members from the virtual space, to admit new members to the virtual space, etc. Also the learners could easily recognise who is the moderator of the session. For example in a three dimensional (3D) virtual space a special chair could imply that this is the moderator’s chair.

30

Another proposal is to support the exchange of roles in the virtual space. This problem could be implemented by using customisable interfaces and virtual places according to the e-learning scenario, supported by a database, which handles the users’ profiles.

5. ImplementIng tHe prIncIples This section describes the way that the design principles presented in the previous section could be implemented in 3D collaborative virtual environments in order to support collaborative e-learning communities. For demonstrating how the principles could be applied, the EVE Training Area tool is used (Bouras & Tsiatsos, 2002; Bouras et al., 2005). This tool is a three-dimensional space where participants, represented by 3D humanoid avatars, can use a variety of e-collaboration tools. In some cases other tools are used in order to demonstrate different implementations and design approaches. As previously referred in this chapter, current research on the design of collaborative e-learning virtual environments results in various issues and aspects of such environments. Based on the work done in this chapter the designers could be facilitated by exploiting a list of design principles for virtual spaces that are focused on supporting collaborative e-learning. Although, this list of design principle is useful, another major problem is emerging: What is a best practice to transform the set of design principles into modelling concepts and or specific and concrete functional features? In the case of EVE, the first step was to investigate the main functional features. During this investigation and the design analysis the list of design principles have been taken into account. The next step was the creation of a prototype, which has been evaluated by users as described in Bouras & Tsiatsos (2006).

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Figure 2. User interface of the training area

Therefore, the transformation process of the principles was based on prototyping-evaluation process. It should be noted that is difficult (and maybe restrictive for the educational designers) to follow a set of rules for transforming the design principles to functional features. The main problem here is the huge set of parameters that should be taken into account e.g.: collaborative e-learning techniques that will be used, user requirements, users’ profile etc. A possible solution to that problem is the usage of these principles as guide during a “Design Rationale” process of software engineering. According to Jarczyk et al. (1992), design rationale in its simplest, is the explicit listing of decisions made during a design process and the reasons why those decisions were made. This definition hides many of the issues that cause the design of systems to support the capture and use of design rationale to be difficult. So the design

principles proposed in this chapter could help on that direction as rules-of-thumb. Furthermore, these principles could be used as criteria to review and select a 3D CVE platform for supporting collaborative e-learning scenarios. In that chapter we have selected the most used Web 2.0 Virtual environment for educational purposes, namely Second life (http://secondlife.com/). In the following paragraphs, EVE training area is presented and the way that every principle is met in this environment is described. Furthermore, we are validating SL.

5.1 eVe training Area EVE Training Area (http://ouranos.ceid.upatras.gr/ vr) is designed and implemented for hosting synchronous e-learning and e-collaboration sessions. It combines 2D and 3D features for providing the

31

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

users with the necessary communication and collaboration capabilities. The main feature of EVE training area is the 3D representation of a multi-user virtual classroom. The user interface of the training area is depicted in Figure 2. The participants in the virtual classroom can have two different roles: tutor (only one participant) and students. In that way EVE training area meets principle 8. The users that participate in the virtual classroom are represented by humanoid articulated avatars, which can support animations (such as walking and sitting down) and gestures for non-verbal interaction among the users. EVE’s avatars support functions not only for representing a user but also for visualizing his/her actions to other participants in the virtual space, which also satisfies principle 3. Available functions in EVE Training area are: Perception (the ability of a participant to see if anyone is around); Localization (the ability of a participant to see where the other person is located); Gestures (representation and visualization of others’ actions and feelings. Examples are: “Hi”, “Bye”, “Agree”, “Disagree”, and “Applause”); Bubble chat (when a user sends a text message, a bubble containing the message appears over his/her avatar). The virtual classroom is supported by various communication channels (principle 5) such as (a) audio chat, which is the main interaction channel, (b) 3D text/bubble chat, (c) non verbal communication using avatar gestures in order to provide a more realistic interaction among users, expressing, when needed, the emotion of each one to the others (Capin et al, 1999). Furthermore, EVE Training Area supports manipulation of users and shared objects by integrating two specific tools: (a) expel learner/participant and (b) lock / unlock objects. EVE Training Area integrates a “presentation table”, which is the central point in the virtual space, in order to provide specific collaboration tools. Using the functionality of this table the users can present their slides and ideas, can comment on slides, upload and view

32

learning material as well as view streaming video. The avatars of all participants in the virtual space can have a sit next to this table, viewing not only what is presented on the table but also the other participants. Furthermore, the user can change his/her viewpoint in order to zoom in and out on the presented material. The presentation table has the following functionality: •

•

•

•

3D Whiteboard: The 3D whiteboard supports slide projection, line, circle and ellipsis drawing in a wide range of colours and text input in many sizes and colours. It also offers “undo last action” capability as well the erasure of all previous actions on the whiteboard. Brainstorming Board: The brainstorming board can be used in a range of collaborative learning techniques for learners to present their ideas in a structured way. The users can create cards in three shapes (rectangle, circle and hexagon) and five colours attaching text on them. It should be mentioned that the shape and colour of the cards is attached to a defined argument. They can also move and delete a card. Video presenter: Video presenter is used in order the user to attend streaming video presentation/movies inside the 3D environment. The users have the capability to start and stop the movie. Supported formats are rm mpeg, and avi. Library with drag and drop support: The users have the capability to drag and drop learning material on the table. This material is represented as a small icon on the backside of the table. When the user clicks on the icon the corresponding file is opened either on the whiteboard (if the corresponding file is picture or VRML object), on the video presenter (if the corresponding file is of rm, mpeg or avi type) or on a new popup window (if the corresponding file is not supported by the VRML format).

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Figure 3. Creating and changing EVE training area

As described in Bouras & Tsiatsos (2006), after the user-evaluation, the usability of the user interface usability of the prototype has been rated positive. Concerning the support of multiple collaborative learning scenarios (i.e. principle 1) the teacher could design the EVE training area as s/ he wants (Bouras et. al, 2007) by: •

•

Using predefined classroom models and having the ability to reorganize the classroom. More specifically the tutor can create quickly a classroom setup and have the ability to move existing objects or to add new. Creating and setting up of a virtual classroom using object library (Figure 3).

This function supports the teacher to implement multiple learning scenarios by changing the organization of the classroom and by using different shared objects that can facilitate these scenarios.

For example, in order to apply the brainstorming/ roundtable scenario (which is described later on in this chapter) the tutor can re-organise the classroom area by creating a table with a brainstorming board and seats for the learners around the table, as depicted in Figure 4. Furthermore, EVE Training Area has been design in such a way to maximise the flexibility within a virtual space (in order to satisfy principle 2). As described before, the tutor can reorganise the EVE training area in order to support better the learning needs as well as to avoid misunderstandings in the usage by the students. In that way, the tutor can either create or re-use virtual rooms for formal classes, group work, etc. For example in the organisation depicted in Figure 4 the only action for the user, in order to participate in the brainstorming session, is to move his/her mouse aver chair and to click on it (Figure 5a). By following these actions the viewpoint of the user is changed and s/he can see the presentation table

33

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Figure 4. Organizing the EVE training area for brainstorming

and the other participants (Figure 5b and Figure 5c). After that the user can cooperate with the rest of participants in the brainstorming session by zooming in the brainstorming table (Figure 5d). In order to augment the user’s representation and awareness (and to satisfy principle 3), the usage of avatars along with gestures and additional icons attached to the avatar could be very helpful (Bouras & Tsiatsos, 2006). Examples of this functionality are the following: •

Bubble chat over the avatars head, which can be used in order to inform the

•

participants of a session about the text chat input of this user. Figure 6a depicts the implementation of a bubble chat. User representation and avatar gestures for expressing actions and feelings. In Figure 6b, we can see an avatar of a user to visualize a “Hi” action by a gesture in the EVE training area (Figure 6b). Good examples of other environments are the following: (i) An avatar presenting a theme and pointing on a specific point using its hand (Figure 6c) in I-maginer 3D virtual class (www.i-maginer.fr); (ii) An avatar whispering o another

Figure 5. Brainstorming session

(a)

34

(b)

(c)

(d)

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Figure 6. Examples of augmenting user’s representation and awareness

(a)

(b)

(c)

using text chat (Figure 6c) in INVITE project prototype (Laister & Kober, 2002). Concerning awareness of objects and the action on them, there are many solutions. An example is depicted in Figure 6c, where an avatar presenting a topic is shown. Another example is depicted in Figure 5, where users can share and see the cards attached in the brainstorming board by their participants. According to principle 4, in collaborative virtual environments the basic functionalities of the interface should be accessible in a graphical user interface fashion. Furthermore, in order to reduce the amount of extraneous load of the users EVE training area follows the following approach: •

Adopts avatars with gestures in order be possible for the user to see at once who is participating and who makes which contribution. An example is depicted in Figure 7a.

•

(d)

Separates the shared and not shared areas in order to avoid user’s misconception as depicted in Figure 7b. A different design that could maximise the amount of extraneous load of the users is depicted in Figure 7c. In that case there are many areas that contain information fully, partly or not shared. Thus, the user could be overloaded in order to discover what the other participants are doing, who is participating, etc.

As previously described e-learning systems supported by collaborative virtual environments should be based on three main categories: Content, Learning Context and Communication Media (principle 5). The approach adopted in EVE training area with the concepts of (a) presentation table for sharing information; (b) avatars, audio conferencing and text chat for supporting communication; (c) 3D classroom design along with shared library for integrating learning content has

Figure 7. Design examples to reduce the extraneous load of the users

(a)

(b)

(c)

35

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

been rated very positively as described in Bouras & Tsiatsos (2006). Thus such a design approach is proposed for supporting principle 5.

5.2 second life Validation against design principles Using SL, we created a 3D virtual environment which could be used for collaboration and carrying out online lectures. The design of the environment consisted of two interconnected rooms (Figure 8): a lecture hall where presentations by the teacher/students can be held, and a collaborative room where student teams can meet to collaborate. Our proposed evaluation methodology was applied through a group of students interacting within our educational environment design.

Some demonstrative tools for a virtual classroom or a collaboration space were designed and implemented on top of SL. These tools are equivalent to the real world ones, but in the virtual world context they may gain additional value, primarily because of the virtual world’s inherent lack of spatial and temporal limitations. These tools are the following (Figure 8): •

Presentation Board: This is a simple tool to support collaborative learning activities. A teacher inserts and arranges the steps of a structured team activity into the board. Afterwards, the students start the activity and update the board to indicate the step they are working on, whether they have finished the other steps or need help. This way the teacher can be aware of the teams’ progress with less effort.

Figure 8. The lecture hall and collaborative room of the environment we designed in Second Life

36

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

•

•

Progress display: The learners can use this tool to indicate whether they are working, have finished or are facing a problem. Also, the teacher is notified by the tool whenever the learners call for help. The progress display can also be attached to the door of a collaboration room. Group chat/Brainstorming: Though SL logs every chat activity in the respective window learners may want to log their brainstorming session separately. Using this tool, the students can chat in a separate chat channel, and display the conversation floating over the tool. Later, the text can be dumped into the public chat window.

Regarding the design adequacy of SL for online learning purposes, we validated the platform’s features, philosophy and policies against the design principles presented before. •

•

Principle 1 - Design to support multiple collaborative learning scenarios: Many collaborative learning scenarios can be supported in SL due to the fact that it supports text chat (private and public), voice chat, streaming video and audio, interaction with objects and group formation. Also, a variety of tools has been or can be developed. However, lack of application sharing is a definite drawback which needs to be addressed. Principle 2 - Design to maximize the flexibility within a virtual space: Space parameters like size, architecture, facilities and the physical environment affect the way learners socialize. In order foster educational value, virtual environments must fulfil the teacher’s expectations for spatial and temporal flexibility. Therefore, due to the need for multiple functions within a collaborative online synchronous session, it should be possible to quickly reorganize the virtual place for a particular activity or scenario. In SL there are limitless capabilities regarding

•

•

•

•

the organization of space. The instructor, using custom scripts and 3D objects, can allocate space instantly, satisfying the learners’ needs. Principle 3 - Augmenting user’s representation and awareness: Combining gestures, mimics, user representation, voice and text chat communication, users can share their views and show others what they are talking about. SL’s avatars are very flexible in customizing so that they not only look quite realistic, but also permit each user to display a unique style, enhancing user representation. Realistic walking and sitting animations, customizable gestures, typing animations and sounds, as well as head and eye movement, increase spatial and user awareness. Principle 4 - Design to reduce the amount of extraneous load of the users: SL is designed in a way that prevents user’s extraneous load. The built-in browser, the flexible preferences menu that allows the user to select the graphics quality and performance and the obvious distinction between shared and non-shared objects not only prevent extraneous load, but also make it possible for users with older computers to participate in the environment efficiently. Principle 5 - Design a media-learning centric virtual space: SL is by design a media-centric platform. Users can communicate through means such as text and voice. Even avatar live-video-mapping is possible. In addition, users can upload textures, or stream audio and video into the world. Support for viewing and manipulating documents may be added in the future if the platform can implement application sharing. Principle 6 - Ergonomic design of a virtual place accessible by a large audience: SL is indeed accessible since the in-world tutorials guide the user during his/her first actions.

37

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

•

•

Principle 7 - Design an inclusive, open and user-centred virtual place: SL membership is free, anyone above 18 years old can join (there is also a separate world for teenagers) and the virtual content of the world is created by its users. A significant drawback is the fact that organizations must pay monthly fees to the owners of the platform to be able to own and administrate land parcels in the virtual world. While this may be reasonable, as the company takes care of the maintenance and the expansion of the virtual world, some organizations would rather invest these resources in customizing the world for their own needs. Principle 8 - Design a place for many people with different roles: One very important in-world function included in SL is the creation of groups. This function permits the group creator (owner) to assign different roles to group members and to set access rights to each role.

Apart from the theoretical validation of SL’s capabilities to support collaborative learning scenarios, we have implemented and evaluated a jigsaw collaborative e-learning technique in SL. The aims of the case study, presented in the next section, are to:

5.3 discussion EVE Training Area supports almost all the previous defined design principles. Thus, even if the use of virtual reality technology is not a required feature a priori, it seems that the use of collaborative 3D virtual environments and humanoid avatars along with supportive communication channels fit well as a solution for virtual collaboration spaces. Concerning SL, it could be said that it stands out among similar web 2.0 immersive virtual environments mainly because it is easily customizable, able to support the creation of learning environ-

38

ments and experiences. The given functions cover the most important needs for communication, collaboration, awareness and administration, and at the same time enable the designers to benefit from them using the built-in scripting language. The demonstrative tools we developed seem to be useful, based on our summative evaluation of the platform, for educational use. Humanoid avatars are a unique solution that 3D-centered tools offer to group communication and learning. It is a fact that persons participating in the virtual learning experience with human like full-body avatars feel more comfortable than in chat or audio-communication (Bouras & Tsiatsos, 2006). The main benefit of the avatars is the psychological feeling of a sense of “presence”. The sense of “presence” results in a suspension of disbelief and an increase in motivation and productivity (Bouras & Tsiatsos, 2006). There is a number of important attributes to this experience. The ability to make basic gestures along with a voice or text message strengthens the understanding of the communication context (Redfern & Galway, 2002). Therefore, due to the fact that the user’s awareness of the spatial proximity and orientation of others has a strong impact on the dynamics of group communication (Redfern & Galway, 2002), we could say that 3D multi-user virtual spaces have a good potential for supporting learning communities and e-collaboration. In such an environment users feel as though they are working together as a group and tend to forget they are working independently.

6. collAborAtIVe e-leArnIng usAge scenArIos exploItIng multI-user VIrtuAl reAlItY enVIronments The aforementioned e-learning and collaboration tools could be used for supporting collaborative e-learning scenarios. As the comparison has

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

shown in the previous section, EVE Training Area could be a suitable solution for supporting these services. Some collaborative learning techniques used today are: brainstorming/roundtable (Millis & Cottell, 1998; Osborne, 1963), think pair share, jigsaw (Aronson et al, 1978), quickwrites / microthemes (Young, 1997), and structured academic controversies (Johnson et al, 1998). These techniques are not presented in this chapter due to space limitations. However, the processes for realizing these techniques using Collaborative Virtual Environments are presented. Before describing these processes, specific functionality, which is derived from the collaborative learning techniques, is described. First of all, we propose the tutors and learners to use a 3D virtual classroom (with functionality similar to EVE’s Training Area) and supportive break-out session rooms for dividing the users in sub-groups (in case required by the scenario). Both the specific functionality and the access rights on it depend on the e-learning scenario. The transformation and the basic processes are described in the following paragraphs.

6.1 brainstorming/roundtable The tutor and learners enter the classroom represented by avatars. The tutor asks a question using audio collaboration functionality (or alternatively text chat). Furthermore, the tutor can write the question and upload it to the presentation table as a document. The learners can answer to the questions using the audio collaboration functionality (or alternatively text chat). Furthermore the learners can use the brainstorming tool for writing and attach their ideas on it. When the brainstorming phase is completed, the learners can review and clarify their ideas on the text chat area or in the brainstorming tool.

6.2 think pair share The tutor poses a question (or a problem) as a file on the presentation table or using audio/text chat and introduces the collaboration technique. After a short pause for reflecting, the learners turn into the whisper-mode with their neighbour and discuss privately the problem. Preferable way for whispering would be a private audio-channel within the classroom (audio-whisper function). Alternatively a private text chat can be used. When the assigned discussion time is finished, the tutor gathers the attention of the learners by “ringing the bell” (sending a text message to all of the participants). Then, the learners exit the whispering mode and return to a group for discussion. For discussing in a larger group, the groups split up into separate corners of the learning environment (breakout session rooms). Each group should have a brainstorming tool available, though the equipment should be in the breakout room available only on demand and not by default. The default situation is a group with high visibility of all avatars, gestures and facial expressions. Again the tutor can send a text chat message to all learners in the different breakout areas (“ringing the bell”). Then the avatars, physically gather back in the virtual classroom place.

6.3 Jigsaw The whole Jigsaw procedure can be handled within the virtual classroom, which also supports 4 breakout session rooms (in case we have 4 groups of students). The tutor first introduces shortly the procedure and then asks for the number of learners (good numbers are any multiple of four). For 16 learners the tutor suggests study groups of 4 and 4 sections. Then the tutor needs to formulate the sections: s/he divides the users in the sections and attaches the necessary learning content to each section. The tutor then assigns the learners to their role (group number and section number). The learners will then receive an automated message about

39

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

the room they need to go to: there they find the section description on the presentation table and any study material the tutor might have assigned to the focus group. After that, the learners of each section participate all together in a section-shared place. The places can be virtual small classes (breakout session rooms) with audio collaboration, application, sharing, and text-chat functionality. Also the tutor can also assign documents to this section. These documents will be available to the learners in the breakout room. The learners can take material from the presentation table to their other session, by saving the materials into their local PC and upload it again.

6.4 quickwrites / microthemes The whole procedure for this technique can be handled within a 3D classroom, which has also 4 breakout session rooms. In the virtual classroom and the breakout out session rooms the users can use audio collaboration, application sharing and text chat functionality. The tutor presents to the learners the microthemes in the presentation table space. Also s/he uploads and presents supporting documents on the shared space. The learners can open for themselves a notepad or other text editor; focus on the proposed documents and after completion of the assignment, easily save their result on their local PC and upload it into the shared space. The tutor assigns groups to the themes that should be discussed (2-4 persons). The learners move to the breakout-rooms, pull their documents onto the presentation area in those rooms and discuss the outcomes. One person writes a protocol of the group discussion and saves the result back to his/her local PC and then upload it into the classrooms’ shared space. The tutor can visit the breakout out session rooms groups and discuss the status of the work. Furthermore the tutor has the capability to call the learners group to return back to the main classroom area, using text chat or by visiting the breakout session rooms. In the main classroom

40

area the groups present their results using application sharing and audio chat.

6.5 structured Academic controversies The whole procedure for this technique can be handled within a 3D classroom, which has also 4 breakout session rooms in case of 16 learners. In the virtual classroom and the breakout out session rooms the users can use audio collaboration, application sharing and text chat functionality. The tutor selects and uploads a topic with two different viewpoints on the presentation table. The learners form groups of 4 and divide into two pairs. Each pair goes to a breakout session room and the tutor uploads supportive documentation. Furthermore, the learners can upload their own content that think it could be supportive for formulating their assigned advocacy position. The pairs of learners have the possibility to visit breakout session rooms of the other pairs with the same positions. Each learner pair can prepare a short presentation using application sharing and collaboration on documents and to upload this presentation in the original groups of four learners. Each pair presents its position to the other pair in their group using application sharing and audio chat. In this case, no debate allowed and the tutor restricts the audio, application sharing, text chat, and gestures functionality from the opposite pair. Afterwards, the other pair presents its position, and then the learners debate and provide more evidence. Finally, learners drop their advocacy role and generate a consensus report addressing the original question posed using application sharing, collaboration on documents, and audio chat.

7. conclusIon & Future work Virtual reality technology could be used to support education in many areas, such as simulation of complex systems, macroscopic and microscopic

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

visualisation as well as fast and slow time simulation. Significant characteristics of VR that could be exploited to support education are the high levels of interactivity, the sense of immersion and the inherent flexibility/adaptability. For that reasons VR has been exploited in various projects in order to support education, training and/or collaboration. This chapter attempted to contribute on the current research on the design of collaborative e-learning virtual environments by investigating and defining design principles that educational designers could follow for designing effective virtual spaces for e-learning and e-collaboration. Thus, this chapter presented a list of design principles for virtual spaces that are focused on supporting collaborative e-learning. These design principles could be useful for software designers in order to enhance current CVEs by integrating supportive communication and collaboration tools and services, as well as tools for effective manipulation of both learning content and the users’ roles and rights. In addition this chapter presents a solution for supporting e-collaboration and multi-user communication in web-based learning communities. After the presentation and discussion of this solution we could say that 3D multi-user virtual spaces could be suitable for supporting learning communities and e-collaboration and for the effective realization of collaborative e-learning scenarios. Besides the basic principles presented in this chapter, it should be noted that when designing and implementing a system for supporting collaborative e-learning communities, there are some additional parameters that should be taken into account for achieving a higher degree of acceptance by the target users. These parameters are related to the profile of the users that the elearning system aims to support as well as to the domain area that e-learning processes will be applied. One of the key elements for the success and effectiveness of an e-learning system is the wide acceptance, in terms of use, of the users it targets at. Even though the profile of the users that will use an e-learning system may vary, as e-learning

communities could refer to a wide range of users, however, each application could be specialized based on some common characteristics of the majority of the users it aims to support. These characteristics involve, among others, the age of the target group, their IT skills, their educational level, their social and cultural background as well as their orientation to learning. Another basic consideration when designing and developing collaborative e-learning systems is the learning domain that these systems try to support and the processes that need to be simulated. As learning can refer to every aspect of the human activity it becomes clear that there is an extremely wide range of domains that could be explored by e-learning systems. Each of these domains, however, is characterized by special characteristics, which should be taken into account when designing the technological “texture” of an e-learning environment. In particular, the processes and content of a learning system could be social-focused, technical-focused, experiencefocused, etc. This particular focus should be a basic guideline when designing the functionality of the learning system as well as for the selection of the technology and style to be used. According to this discussion our next steps are to design and implement an adaptive CVE system that can be changed in various learning domains and user profiles. Further steps include research on investigating the relation between the use of the design principles presented in this chapter and learning outcomes.

8. reFerences Aronson, E., Blaney, N., Stephan, C., Sikes, J., & Snapp, M. (1978). The jigsaw classroom. Sage Publications.

41

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Bouras, C., Giannaka, E., & Tsiatsos, T. (2005). Designing Virtual Spaces to Support Learning Communities and e – Collaboration. 5th IEEE International Conference on Advanced Learning Technologies, Koahsiung, Taiwan, 5 - 8 July 2005 (pp. 328-332). Bouras, C., & Tsiatsos, T. (2002). Extending the Limits of CVEs to Support Collaborative e-Learning Scenarios. 2nd IEEE International Conference on Advanced Learning Technologies, Kazan, Russia, 9 - 12 September 2002 (pp. 420-424). Bouras, C., & Tsiatsos, T. (2006). Educational Virtual Environments: Design Rationale and Architecture. Multimedia Tools and Applications, 29(2), 153-173.Bouras, C., Tegos, C., Triglianos, V. & Tsiatsos, T. (2007). X3D multi-user virtual environment platform for collaborative spatial design. Paper presented at the 9th International Workshop on Multimedia Network Systems and Applications (MNSA-2007), Toronto, Canada, 25 - 29 June 2007. Calongne, C. (2007). A View from Second Life’s Trenches: Are You a Pioneer or a Settler? In Proceedings of the NMC Summer Conference, 2007 (pp. 111-119). Capin, T., Pandzic, I., Magnenat-Thalmann, N., & Thalmann, D. (1999). Avatars in Networked Virtual Environments. John Wiley & Sons Ltd. Cassell, J., Bickmore, T., Campbell, L., Vilhjálmsson, H., & Yan, H. (2000). Human conversation as a system framework: Designing embodied conversational agents. In J. Cassell, J. W. Sullivan, S. Prevost, & E. F. Churchill (Eds.), Embodied conversational agents (pp. 29-63). Cambridge, MA: MIT Press. Clark, S. & Maher, M.L. (2006). Collaborative Learning in a 3D Virtual Place: Investigating the Role of Place in a Virtual Learning Environment. doi: 10.2316/Journal.208.2006.4.208-0896.

42

Dillenbourg, P. (1999). What do you mean by collaborative learning? In P. Dillenbourg (Ed.), Collaborative-learning: Cognitive and Computational Approaches (pp. 1-19). Oxford: Elsevier. Dourish, P., & Harrison, S. (1996). Re-Placing Space: The Roles of Place and Space in Collaborative Systems. In Proceedings of the ACM CSCW’96 Conference on Computer Supported Cooperative Work (pp. 68-85). Dumas, C., Saugis, G., Degrande, S., Plénacoste, P., Chaillou, C., & Viaud, M. (1999). Spin: A 3D interface for cooperative work. Virtual Reality (Waltham Cross), 4(1), 15–25. doi:. doi:10.1007/ BF01434991 Hay, K. E., Elliot, D., & Kim, B. (2002). Collaborative network-based virtual reality: The past, the present, and the future of the virtual solar system. Paper presented at the CSCL conference, Boulder, CO. Retrieved July 6, 2006, from http:// newmedia.colorado.edu/cscl/151.pdf Hsi, S., & Hoadley, C. M. (1997). Productive discussion in science: gender equity through electronic discourse. Journal of Science Education and Technology, 6(1). doi:10.1023/A:1022564817713 Hutchins, M. A., Stevenson, D. R., Gunn, C., Krumpholz, A., Adriaansen, T., Pyman, B., & O’Leary, S. (2005). Communication in a networked haptic virtual environment for temporal bone surgery training. Virtual Reality (Waltham Cross). doi:.doi:10.1007/s10055-005-0015-1 Jackson, R. L., & Winn, W. (1999). Collaboration and learning in immersive virtual environments. In C. Hoadley & J. Roschelle (Eds.), Proceedings of the Computer Support for Collaborative Learning (CSCL) 1999 Conference. Mahwah, NJ: Lawrence Erlbaum. Jarczyk, A., Löffler, P., & Shipman, F. (1992). Design Rationale for Software Engineering: A Survey. 25th Hawaii International Conference on System Sciences (Vol. 2, pp. 577-586).

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Johnson, A., Roussos, M., Leigh, J., Barnes, C., Vasilakis, C., & Moher, T. (1998). The NICE Project: Learning Together in a Virtual World. In Proceedings of IEEE Virtual Reality Annual International Symposium (pp. 176-183). Johnson, D. W., Johnson, R. T., & Smith, K. A. (1998). Active learning: Cooperation in the college classroom. Edina, MN: Interaction Book Company. Kalawsky, R. S. (1998). Exploiting Virtual Reality Techniques in Education and Training: Technological Issues, A report prepared for AGOCG. Retrieved January 14, 2009 from http://www. agocg.ac.uk/reports/virtual/vrtech/title.htm Kirner, T. G., Kirner, C., Kawamoto, A. L. S., Cantão, J., Pinto, A., & Wazlawick, R. S. (2001). Development of a Collaborative Virtual Environment for Educational Applications. In Proceedings of the sixth international conference on 3D Web Technology (pp. 61-68). Koubek, A., & Müller; K. (2002). Collaborative Virtual Environments for Learning. ACM SIG Proceedings. Kreijns, K., Kirschner, P. A., & Jochems, W. (2002). The sociability of computer-supported collaborative learning environments. Educational Technology & Society, 5(1). Kuljis, J., & Lees, D. Y. (2002). Lessons from Industry in the Design of Virtual Collaborative Learning Environments. In Proceedings of International Conference Information Technology Interfaces - ITI 2002Cavtat, Croatia (pp. 31-36). Laister, J., & Kober, S. (2002). Social Aspects of Collaborative Learning in Virtual Learning Environments. In Proceedings of the Networked Learning 2002 Conference. Retrieved October 7, 2007 from http://www.networkedlearningconference.org.uk/past/nlc2002/proceedings/papers/19. htm

Langer, E. (1998). The Power of Mindful Learning. Perseus Books Group Liebregt, M. (2005). Collaborative Virtual Environments in education. Paper presented at 2nd Twente Student Conference on IT, Enschede 21 January. Lindeman, E. (1989).The Meaning of Adult Education. Norman, University of Oklahoma, USA. Lymna, F. (1981). The responsive classroom discussion. In A. S. Anderson (Ed.), Mainstreaming Digest. College Park, MD: University of Maryland College of Education. Macedonia, M. R., Zyda, M. J., Pratt, D., Brutzman, R., Donald, P., & Barham, P. T. (1995). Exploiting reality with multicast groups: A network architecture for large-scale virtual environments. In Proceedings IEEE Virtual Reality Annual International Symposium (VRAIS’95), North Carolina. Millis, B. J., & Cottell, P. G. (1998). Cooperative learning for higher education faculty. American Council on Education, Series on Higher Education. The Oryx Press, Phoenix, AZ. Moshman, D. (1982). Exogenous, Endogenous and Dialectical Constructivism. Developmental Review, 2, 371–384. doi:10.1016/02732297(82)90019-3 NMC. (2007). The Spring 2007 Survey: Educators in Second Life. Retrieved from http://www.nmc. org/pdf/2007-sl-survey-summary.pdf OECD. (2007). Participative Web and User-created Content. Web 2.0, Wikis and Social Networking. Retrieved from http://213.253.134.43/oecd/pdfs/ browseit/9307031E.PDF

43

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Okada, M., Tarumi, H., Yoshimura, T., & Moriya, K. (2001). Collaborative environmental education using distributed virtual environment accessible from real and virtual worlds. ACM SIGAPP Applied Computing Review, 9(1), 15–21. doi:10.1145/570142.570147 Okada, M., Yamada, A., Tarumi, H., Yoshida, M., & Moriya, K. (2003). DigitalEE II: RV-Augmented Interface Design for Networked Collaborative Environmental Learning. In Proceedings of the International Conference on Computer Support for Collaborative Learning (pp. 265-274). Osborne, A. (1963). Applied imagination (3rd ed.). New York: Scribner’s. Portugal, R. C., Guerrero, L. A., & Fuller, D. A. (2000). DeskTOP, a system based on virtual spaces to support and to promote collaborative learning. In Proceedings of the third international conference on Collaborative virtual environments (pp. 199-200). Prasolova-Førland, E., & Divitini, M. (2003). Collaborative virtual environments for supporting learning communities an experience of use. In Proceedings of the 2003 international ACM SIGGROUP conference on supporting group work (pp. 58-67). Redecker, C. (2008). Review of Learning 2.0, Practices, Deliverable 2 of the study: Learning 2.0 The Impact of Web 2.0 Innovations on Education and Training in Europe. Draft Working Paper, IPTS – IS Unit. Retrieved from http://is.jrc.ec.europa. eu/pages/documents/Learning2-0Review.pdf Redfern, S., & Galway, N. (2002). Collaborative Virtual Environments to Support Communication and Community in Internet-Based Distance Education. [JITE]. Journal of Information Technology Education, 1(3), 201–211.

44

Robinson, M., Pekkola, S., Korhonen, J., Hujala, S., Toivonen, T., & Saarinen, M.-J. (2001). Extending the Limits of Collaborative Virtual Environments. In E. Churchill, D. Snowdon, & A. Munro (Eds.), Collaborative Virtual Environments: Digital Places and Spaces for Interaction. Berlin: Springer-Verlag. Roussos, M., Johnson, A. E., Leigh, J., Vasilakis, C. A., Barnes, C. R., & Moher, T. G. (1997). NICE: combining constructionism, narrative and collaboration in a virtual learning environment. ACM SIGGRAPH Computer Graphics, 31(3), 62–63. doi:10.1145/262171.262264 Schneider, D. (1996). Virtual Environments for Education, Research and Life. WWW5 workshop on Virtual Environments and the WWW. Retrieved October 7, 2007 from http://tecfa.unige.ch/moo/ paris96/papers/daniel.html Singhal, S., & Zyda, M. (1999). Networked Virtual Environments: Design and Implementation. ACM Press. Spellmann, P., Mosier, J., Deus, L., & Carlson, J. (1997). Collaborative Virtual Workspace. In Proc. of GROUP’97, Phoenix Arizona, ACM (pp. 197-203). Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(29), 257–285. Tornincasa, S. (2001). Web3D Technology applications for distance training and learning: the Leonardo project WEBD. Paper presented at the XII International Conference on Design Tools and, methods in industrial engineering, Rimini. Weil, S., Hussain, T., Brunye, T., Sidman, J., & Spahr, L. (2005). The use of massive multiplayer gaming technology for military training: A preliminary evaluation. Paper presented at the 49th Annual Meeting of the Human Factors and Ergonomics Society (Sept 26-30, Orlando, FL).

Exploiting Virtual Environments and Web 2.0 Immersive Worlds

Winn, W., & Jackson, R. (1999). Fourteen Propositions About Educational Uses of Virtual Reality. Educational Technology, 39(4), 5–14.

Young, A. (1997). Mentoring, modeling, monitoring, motivating: Response to students’ ungraded writing as academic conversation. In M. D. Sorcinelli & P. Elbow (Eds.), Writing to learn: Strategies for assigning and responding to writing across the disciplines. New Directions for Teaching and Learning, 69.

45

46

Chapter 3

Web Technologies and E-Learning Strategies for New Teaching Paradigms Antonio Cartelli University of Cassino, Italy

AbstrAct The chapter aims at presenting some teaching experiences the author made for the introduction of the ICT and especially of Web technologies in teaching. In the introduction the need for the use of digital literacy strategies in education and the acquisition of digital competences in the knowledge society are discussed. Soon after, the problems teachers have to face in today society are analyzed and the recommendations of national and supra-national institutions for the continuous education of these professionals are reported (i.e., the European case is almost exclusively discussed). Subsequently a new teaching paradigm based on the implementation of practices is proposed and the features of the information system TETIS, which made possible the use of that paradigm in a master course for teachers, is explained. It is evidenced, among other things, how that system (born for making transparent teaching processes and implementing teachers’ practices) has been used to implement the changes induced by the new regulations in the Italian school. The results from the final questionnaire for the evaluation of the use of the TETIS platform, submitted to the teachers who attended the master course, are at last discussed and new proposals for an effective introduction of new technologies at school is proposed.

IntroductIon During last decade the importance of IT and ICT in lifelong learning and continuous education grew exponentially and new instruments and tools were developed to help individuals in the overcoming of DOI: 10.4018/978-1-60566-938-0.ch003

the complex problems of today society (Trentin 2003, Thorpe 2005, Dashti & Safar 2007). New technologies didn’t help only the subjects to improve the quality of their life, due to socio-technologies and social networking, in fact, they also improved the efficacy of communities of practice in corporate and organizations and supported professional communities in their growth and evolution (Biolghini 2001, Trentin 2004).

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Web Technologies and E-Learning Strategies for New Teaching Paradigms

At the same time the approach to IT and ICT introduction in teaching-learning activities has changed; first, teachers and educators thought it was important to teach technology (computing, algorithms, programming etc.), now there is more interest in the use of new technologies in everyday teaching and in the creation of suitable teaching-learning environments (i.e., constructivist environments, real or virtual they are). National and supra-national political institutions too, showed great interest for the changes induced in today society by the presence of IT and ICT. The European Commission (2005), for example, in its recommendation to member countries, on European citizens’ key competences for lifelong learning, included digital competence as the fourth key competence. The reading of the Commission recommendation clearly shows that digital competence involves the confident and critical use of Information Society Technology (IST) for work, leisure and communication and it is underpinned by basic skills in ICT: the use of computers to retrieve, assess, store, produce, present and exchange information, and to communicate and participate in collaborative networks via the Internet. The acquisition of the digital competences by European citizens mainly aims at: a)

b)

the understanding and knowledge of the nature, role and opportunities of IST in everyday contexts: in personal and social life as well as at work. It includes main computer applications, a sound use of the Internet and the communication via electronic media (e-mail, network tools) for leisure, information sharing and collaborative networking, learning and research, the understanding of the support that creativity and innovation can receive from IST, the development of sound understanding skills helping to state if information is valid, reliable and affordable enough and the knowledge of the ethical principles for the interactive use of IST.

The analysis of the literature on the definition of digital competence and on the many terms adopted today for the description of the knowledge and skills needed by the IT and the ICT use, like information literacy, computing literacy, digital literacy etc., show that digital competence is •

•

•

•

multidimensional, because it implies the integration of cognitive, relational and social abilities and skills, complex, because it cannot be completely measured by single tests and very difficultly can be verified in a short run; it requires more time and different contexts before becoming evident, interconnected; it is not independent from other key competences like reading, numeracy, problem solving, inferential skills etc. sensitive to the socio-cultural context, because its meaning could change over time, according to the context and to the different educational settings.

It can be easily deduced from the above statements that digital competences are very important in teachers’ everyday school work and, as a consequence, they are essential in their profession, both when looking at teachers’ first employ and at their in-service training (Borghi & others 2002, Rivoltella 2003, Galliani 2004). In the following section the special aspect of teachers’ education concerning the challenges of the knowledge society is discussed in a greater detail.

digital literacy, digital competence and teachers’ education and training The team of Eurydice, the European network for education, which collects scholars and researchers all over Europe, analyzed teaching profession in the different European countries and wrote many

47

Web Technologies and E-Learning Strategies for New Teaching Paradigms

reports on this topic (Eurydice 2002a, 2002b, 2003, 2004, 2005). The results of Eurydice’s study dwelt upon many aspects of teaching profession but, for the consequences they will have in this paper, only teachers’ initial training and lifelong learning will be discussed here. Among the many things analyzed in the Eurydice reports it has to be remarked the description of the changes in the skills needed from teachers, because of the influence on everyday life, and consequently on the school work, of the following aspects:

•

•

On a more general basis, the National council of the Italian Faculties for Educational Sciences by means of its president, professor L. Galliani, reported in December 2005 of the introduction of the same skills in the list of the competences that the “quality teacher” must develop in the university courses to be the professional who can help schools in facing today educational problems. Notwithstanding the substantial agreement between Eurydice reports and teacher perceptions there is great difference between theory and practice; a confirmation of this view comes from the analysis of what happens in the various European countries with respect to teacher education in pre-service and in-service phases. As regards the pre-service teachers’ education, the comparative analysis of the university courses all over Europe shows that only IT and ICT basic courses (mostly basic computer science courses) are explicitly included in all teaching curricula. They are mostly designed on the model of ECDL (European Computer Driving Licence) courses, and aim at letting teachers use office automation programs for data management. Other topics like school management, the integration of students with special needs, working with multicultural students’ groups and the management of students’ behaviors are not equally considered in the different countries and very often are not explicitly included among what has to be taught to future teachers (in Italian university courses for teachers

•

technology development, with the great role it plays on learning environments and on the way people build new knowledge (i.e., formal, non formal and informal contexts are continuously changing and are influenced from technology and its evolution), lifelong learning (a need in knowledge society) and the possible development of new ways of interacting between school and adults’ education, ◦ everyday school living, often influenced from multi-cultural phenomena and depending from the behavior of school staff and local regulations (i.e., local autonomy), ◦ the right for all citizens to the highest levels of education, with the well known problems of the integration of diversely able people and of the management of heterogeneous groups of students.

The statements from the Eurydice group are confirmed from teachers’ perception of the environment they live and work in. From a survey the author made with his colleagues, in some schools of the four Italian counties surrounding the University of Cassino (Cartelli 2006), emerged the following results:

48

•

almost all the interviewed teachers agreed with Eurydice statements in reporting the changes they perceived in the school, the teachers thought that the improvements in teaching profession could be attained with the development of the following skills helped from ICT integration in everyday work: organization planning and project making, management of interpersonal and social relationships, communication leading, working in a group and in a community.

Web Technologies and E-Learning Strategies for New Teaching Paradigms

some of the above features, mostly on a theoretical basis, are included in the study curricula and dedicated courses are planned for teachers explicitly working with diversely able students). With respect to in-service teachers’ training and education, the Eurydice’s reports register different situations depending not only on the countries but on the different training activities. First, all over Europe there is a very little institutional sensitivity for teachers’ in-service training, notwithstanding the frequent changes in regulations affecting national educational systems; in many countries the introduction of distance education strategies has been adopted to bring teachers up to date on the changes induced in their everyday work and to propose systematic training activities on special topics. In Italy, for example, the National Agency for School Autonomy Development (once called INDIRE), uses an online e-learning platform named puntoedu (which can be accessed at the URL: http://puntoedu.indire.it/) to make up teachers’ initial training and to offer them educational and training activities on special topics (i.e., teachers’ in-service training); the main problem with the Italian case is the lack of a real feedback (both for the institution and for single teachers), on teachers’ acquisition of knowledge and skills and on the transfer of the acquired skills to everyday school work. Furthermore Eurydice’s reports notice that in almost all European countries very little or no attention is devoted to teachers’ updating activities and to their evaluation; yes these activities highly impact on the career progression of teachers (i.e., in more than 50% of the European countries teachers’ career is influenced from updating activities). In Italy, public institutions act differently with respect to different updating experiences: a)

they do not assign any value to what teachers do autonomously, like the participation in conferences and seminars (Italian regulation only let teachers have special permissions for attending those events),

b)

c)

conversely, special projects on particular topics and experiments, either in single schools or groups of schools, are funded, special value to the participation of teachers at after graduation courses in the universities is given (i.e., at the end of each course teachers can have scores to be used in public competitions, in teachers’ mobility etc.).

By virtue of the issues just given, the offices of some Italian Ministries funded many special projects for the introduction of new technologies in teaching and almost all the Italian universities modified their educational offer and introduced after graduation courses in their curricula.

Ict, teAcHers’ trAInIng And new educAtIonAl pArAdIgms It has to be noted that in addition to what has been already reported on teachers education and training, in Italy further needs in teachers’ updating recently emerged from the new regulations affecting school organization and class work. In what follows a summary of the laws and regulations promulgated by the Italian Ministry of Education is reported; the changes which really occurred in Italian school after the confrontation with the social parties and trade unions are then discussed. This analysis is the starting point for the understanding of the structure of the master course named “Teacher and tutor in the renewed school”, planned and carried out by the author and his colleagues, where new technologies and a new educational paradigm played a relevant role.

the new regulations for the Italian school From the school year 2003-2004 to the school year 2005-2006 different laws and regulations concerning school management and more generally education, followed one another and introduced new

49

Web Technologies and E-Learning Strategies for New Teaching Paradigms

theoretical and practical elements in the organization of compulsory school and in teaching. The main innovating elements of the reform laws were: •

•

•

•

50

the definition of the “Educational, cultural and vocational profile” of the student, i.e. the set of things that he/she must know (discipline and inter-discipline knowledge) and be able to do (operating and vocational skills) at the end of his/her first school cycle, to be the man/woman and the citizen commonly meant in today’s society, the personalization of the student curriculum (now called study program), coherently with his/her educational profile. This process is influenced by: a) the organization and coordination of teaching according to national suggestions (aims), b) the acceptance of cultural and educational proposals from families, c) the integration of the school activities with the experiences made available from local institutions (consistent fractions of the school time have to be explicitly devoted to them), the definition of new features/skills for a teacher within the class council, to be submitted to a special training, and called tutor-master; he/she has in charge the following tasks: a) to be responsible for the continuous contact with families and environment, b) to have guidance functions for the choice of the activities to be included in the students’ personalized study program, c) to coordinate educational and didactical activities, d) to mind the connections with students’ families, e) to mind the documentation of the student’s curriculum, with the help of other teachers, the documentation of the students’ career in the “portfolio of personal skills”. The portfolio collects notes coming from teachers, parents and (eventually) the same student; these notes are selected among: a) works

describing the best skills of the student (made individually or in group), b) significant school tests, c) remarks on student’s learning coming from teachers and families, d) remarks on special works made by the student, e) examination papers that the student, the family or the school propose for the inclusion in the portfolio (showing relevant examples of the student’s skills and wishes), f) synthetic remarks coming from systematic observations, teachersfamily meetings, speech with the student and questionnaires or surveys concerning the student’s bents and interests. The portfolio is made of two parts: the former to be devoted to the student’s evaluation and assessment, the latter to guidance; the portfolio is compiled and brought up to date by the tutor-master teacher. The elements reported above only give a theoretical and static description of the changes in the Italian schools; the implementation of those aspects in everyday life and the changes they induced on school work could difficultly be regulated, as a consequence the Ministry of Education started a first investigation phase during which teachers, school councils and the whole schools worked under the new regulations on an experimental and voluntary basis. The procedures adopted to accomplish the reform law in the experimental schools led the Ministry of education to publish a guide where the best practices were collected. The diagram below drafts the process governing the planning and carrying out of the educational work by a general teacher for each of his/her students. While adopting the above scheme, teachers must look at the student’s “Personal, educational, cultural and vocational plan” and at the discipline’s general guidelines (national general aims), for the definition of the students’ learning targets. Otherwise stated, after having analyzed the students’ initial situation (pre-requisites etc.),

Web Technologies and E-Learning Strategies for New Teaching Paradigms

Figure 1. Elements influencing the definition of the learning units for each student (from the image in the report of the Ministry of Education, p. 166)

teachers draw the educational targets for their activity and design the learning units for every student. At the end of every learning unit the students’ assessed competences are included in the corresponding portfolios. The laws and regulations by the Italian Ministry of Education created bad mood in the teachers all over the nation and were followed by many protests; after some months the following amendments were made to the original proposals: a)

b)

the families would not play the prominent role that the reform law initially assigned to them in the choice of a relevant part of the students teaching-learning activities (i.e., all class work is planned and approved by the class council, mostly made by teachers), the tutor-master teacher and the student’s portfolio did not become effective.

It has to be noted that the above amendments must be considered as little changes with respect to the whole project and they did not undermine the reform because: a)

b)

in Italy the schools already have their own administrative autonomy and standing practice confirms the presence of a teacher as class-coordinator for every class in the school; his/her main aim is to help the headmaster in the management of the class council and of the class work, different level schools use different kind of student cards for data storage (these documents are not portfolios but they act as part of them).

51

Web Technologies and E-Learning Strategies for New Teaching Paradigms

the master course for teachers and the use of a new pedagogical paradigm The changes the reform law induced in the school and the great debate which accompanied its application led single teachers and whole schools to ask to different institutions for updating and training courses. The pedagogical community in the University of Cassino answered to the above requests with two different proposals: an after graduation special course and an annual master course titled “Teacher and tutor in the renewed school”. In the master course a theoretical analysis of the reform law and instruments and procedures to be adopted by teachers in their everyday work were proposed. The detailed discussion of the structure and of the features of the master course are behind the aims of this paper but the use of a new pedagogical paradigm in some modules of the course needs a description and an explanation; the teaching paradigm is the “implementation of practices by means of Information Systems” (Cartelli, 2008a) and in addition to the change in teaching-learning style obtained with the use of suitable information systems, it aims at making transparent the teaching-learning processes. The cited paradigm originated from previous experiences and in the special case of the master course it is based on the TETIS platform (TEaching Transparency Information System). It is a Management Information System (MIS) for the government of the processes occurring in everyday school teaching. The following issues are the basic pillars of its structure: a)

52

knowledge construction in human beings happens at three different levels (i.e. subjects know: 1. by interacting with the environment they are facing, 2. by participating in the activities of one or more communities they belong to, and at last, 3. by inheriting the culture/social knowledge of the society

b)

c)

they are immersed in) and each of them influences the others (Cartelli, 2008b), when a socio-technical approach for the creation of an information system is adopted all the levels described above must be considered; only the above conditions let practices be successfully implemented in the system and communities of interest, of learning or of practices be made up around the system and support the knowledge construction process, the information system and the community/ies working on it act like a learning organization; its knowledge evolves under the guidelines of a scheme similar to the SECI model by I. Nonaka and H. Takeuchi (1995). A new element, the “implementation of practices with the ICT” is now present between the Internalization and Socialization elements, but the rules governing knowledge transformation processes and the overall spiral process are the same.

The main features of the TETIS platform are: a)

users accessing the information system have different rights and powers: 1) the user with the less rights can only query the system to obtain general information on the schools which have their data stored in the platform; 2) the headmasters of the schools using the platform can access all school data only for reading; 3) at an upper level are the students and their families, who can access special web areas (by means of their ID and password); they can input and modify their personal data, input and modify all information concerning special or extra-school experiences to be included in the students’ cards, look at the evolution of the teaching work, at the carrying out of the students’ personal study programs, at their presence at school and at their assessments; 4) the

Web Technologies and E-Learning Strategies for New Teaching Paradigms

Figure 2. Data flow in the TETIS platform

b)

teachers can manage all data concerning teaching planning and evolution (with the assignment to the students of the learning units and the personal study programs) and the students’ assessment (i.e., these data can be accessed from students and families only when they are validated from the class coordinator, after the approval of the class council); 5) the class coordinator, among all teachers, has a special role because he/she validates students’ assessment and all data to be included in their cards (after the approval of the class council); 6) at the top of the access pyramid is the system administrator who can do all the operations allowed to teachers and coordinators and can access the certified data to modify or to delete them; students’ personal data can be accessed only from students and their families; global indices on those data can be accessed from authorized people (like educational researchers or social workers). Class information can

c)

be accessed from students/families in the class (i.e., teaching evolution and students’ global indices on evaluation and assessment). General school information (i.e., global indices on the teaching progression, the mean teaching processes’ evaluation and the students’ assessment) are available to everyone; the first step in system use is the input of the data concerning students, families, teachers, school and learning units; soon after teachers can record the data concerning the planning and carrying out of their work and the students’ personal study programs, presences and assessments. The coordinator (after the approval of the class council) validates the data and lets people access them for reading.

While implementing the teaching processes the TETIS platform lets people communicate by means of various instruments: a) one electronic

53

Web Technologies and E-Learning Strategies for New Teaching Paradigms

Figure 3. Steps of teachers’ work implemented in the TETIS platform

blackboard / forum for every class only devoted to teachers, b) one electronic blackboard / forum for every class within which teachers, students and family can interact, c) one electronic blackboard / forum for every discipline where teachers can discuss among themselves on the disciplinary topics. The scheme in Figure 3 summarizes the main features of the TETIS platform (Cartelli, 2005). The structure and the functions of the TETIS platform are general enough to be used in everyday class work management, independently from any reform law. In the case of the master course the above features have been used in two didactic modules, concerning respectively a) the role of

54

the ICT in teaching management and b) Teaching programming and carrying out as provided by the reform law. The scheme in Figure 3 summarizes the way by which teachers’ work has been implemented in the TETIS platform.

results from the teachers Attending the master course At the end of the master course an anonymous online survey has been proposed to the 54 people (nearly 50% of the people attending the master course), who chose to intensively use the TETIS platform for simulating everyday school work and made a final thesis on the description of

Web Technologies and E-Learning Strategies for New Teaching Paradigms

the experience they had with the platform. The survey analyzed teachers’ perceptions of the ICT influence on the topics in the course and on their application to everyday work, while considering the transformations induced from the reform laws. In the following tables the distribution of teachers’ answers are reported, with a special attention to the following three areas: a)

the quantity of the everyday work teachers managed with the TETIS platform (table 1),

b)

c)

the perception of the support that the TETIS platform could give to teaching work (table 2), the analysis of the possible consequences the TETIS platform can have on the interactions among the actors of the educational processes (table 3).

The main conclusion from the above data is that people who used the TETIS platform found it useful enough to better understand the innovation

Table 1. Activity implemented in the TETIS platform Abs. values Yes

Percentages

No

Yes

No

Did you compile all the sections in the teaching module you created?

49

5

90,7

9,3

Did you apply the activities reported in the planned modules to all students?

51

3

94,4

5,6

Did you collect the students’ answers for all the activities in the modules?

48

6

88,9

11,1

Table 2. Support given to teaching work from TETIS Abs. values

Percentage

Yes

Yes

No

No

Is the planning of the teaching work as supported from TETIS coherent and complete?

53

1

98,1

1,2

Is the work with the platform TETIS easy enough to manage?

51

3

94,4

5,6

Does the information in the platform completely describe teachers’ work?

47

7

87,0

13,0

Does the information in the platform adequately describe students’ behaviors and performances?

41

13

75,9

24,1

Does TETIS platform lead teachers to better programming their work and to obtain better results?

51

3

94,4

5,6

Does TETIS platform make easier for teachers the personalization of student’s teaching-learning phenomena?

50

4

92,6

7,4

Table 3. Impact of TETIS platform on social relations Abs. values Yes

Percentage

No

Yes

No

Can TETIS platform improve the dialogue between the teacher and the students?

38

16

70,4

29,6

Can TETIS platform improve the dialogue among the teachers in the class?

49

5

90,7

9,3

Can TETIS platform improve the dialogue between the teacher and the student’s relations?

47

7

87,0

13,0

Can TETIS platform induce the creation of communities of practices?

49

5

90,7

9,3

Can TETIS platform improve the dialogue between teachers and principal in a school?

47

7

87,0

13,0

Can TETIS platform induce better cooperation between school and social services?

45

9

83,3

16,7

55

Web Technologies and E-Learning Strategies for New Teaching Paradigms

introduced from the reform law, they also thought that the platform could be improved to give more support to everyday teaching work. Furthermore the teaching-learning paradigm “implementation of practices with Information Systems” has appeared successful enough in helping teachers to learn and to apply the changes induced by the reform law, to let them accept the use of an information system for having support in their work and to persuade them that they could make communities of practices working on the information system.

conclusIon And Future trends At the end of the master course all the schools surrounding the university of Cassino were invited to use the TETIS platform for the management of everyday teaching; only High Schools accepted the proposal and decided to use the system on an experimental basis. The greatest difficulty in the generalized application of the TETIS platform in the school seems to be the lack of motivation by teachers, who explicitly said they had no reason for making an effort which had no counterpart. Notwithstanding the platform has been proposed for free only a few class councils accepted to use it systematically, and no school decided to use it fully. Further support to the possible use of the TETIS platform in everyday school came quite occasionally from a different context. The Laboratory for teaching and learning technologies managed by the author was in fact involved in a national competition for the production of learning objects in the primary school. While helping the schools in the planning of the projects for the national competition the use of the TETIS platform was suggested for: a)

56

the construction of a virtual environment within which to manage teaching-learning processes,

b)

the inclusion of the learning objects use in the more general teaching-learning process management.

In school year 2008-2009 started the project called “open education” involving the schools which placed in position for funding in the ranking of the public competition. The site http://elf.let. unicas.it/ was made up to support the project and in it the TETIS platform and different e-learning platforms were installed. The project is not yet arrived at the end and it is too early to say if and how much the systems will influence teachers’ work and students’ learning when it will be used for everyday school work. As a conclusion it is evident that there is much work to do in the future not only to complete and verify what has been done until now, but also to merge the results coming from the different interventions and especially the management of educational objects and processes. At least a few questions need to be answered: •

•

if the TETIS and the e-learning platforms can be helpful in governing teaching-learning processes can they help in the development of digital competences in teacher, students and families? is there any framework to be considered for the possible integration of the above instruments in a perspective of objects and processes management?

Further experiments and analyses are needed to describe the impact that the instruments described above can have on individual and social environments. The way is open for new experiences and new research.

Web Technologies and E-Learning Strategies for New Teaching Paradigms

reFerences Biolghini, D. (2001). Comunità in rete e Net learning. Milan: RCS libri. Borghi, L., De Ambrosis, A., & Mascheretti, P. (2002). Formazione in rete degli insegnanti di fisica: un esempio. Tecnologie Didattiche, 26, 58–61. Cartelli, A. (2005). Towards an Information System Making Transparent Teaching Processes and Applying Informing Science to Education. Journal of Issues in Informing Science and Information Technology, 2, 369–381. Cartelli, A. (2006). Un esempio di progettazione di attività formativa mirata. Quaderno n. 2 del Centro di Facoltà per le T.I.C. e la didattica on line. Cassino (Italy): Idea Stampa. Cartelli, A. (2008a). The Implementation of Practices with ICT as a New Teaching-Learning Paradigm. In A. Cartelli & M. Palma (Eds.), Encyclopedia of Information Communication Technology (pp. 413-418). Hershey, PA: Information Science Reference. Cartelli, A. (2008b). Towards a new model for knowledge construction and evolution. In A. Cartelli & M. Palma (Eds.), Encyclopedia of Information Communication Technology (pp. 767-774). Hershey, PA: Information Science Reference. Commission of the European Communities. The (2005). Recommendation of the European Parliament and of the Commission on key competences for lifelong learning. Retrieved January 9, 2009 from http://ec.europa.eu/education/policies/2010/ doc/keyrec_en.pdf Dashti, A., & Safar, M. (2007). Streaming of Continuous Media for Distance Education Systems. International Journal of Distance Education Technologies, 5(3), 42–66.

Eurydice, The information network on education (2002a). Initial training and transition to working life, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education, vol. 1. Retrieved January 9, 2009 from http://eacea.ec.europa.eu/ressources/eurydice/ pdf/0_integral/037EN.pdf Eurydice, The information network on education (2002b). Supply and demand, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education, vol. 2. Retrieved January 9, 2009 from http://eacea.ec.europa.eu/ ressources/eurydice/pdf/0_integral/034EN.pdf Eurydice, The information network on education (2003). Working conditions and pay, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education, vol. 3. A Retrieved January 9, 2009 from http://eacea.ec.europa.eu/ressources/eurydice/ pdf/0_integral/040EN.pdf Eurydice, The information network on education (2004). Keeping teaching attractive for the 21st century / Volume 4, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education, vol. 4. Retrieved January 9, 2009 from http://eacea.ec.europa.eu/ ressources/eurydice/pdf/0_integral/043EN.pdf Eurydice, The information network on education (2005). Reforms of the teaching profession: a historical survey (1975-2002) / Supplementary report, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education.Retrieved January 9, 2009 from http://eacea.ec.europa.eu/ressources/eurydice/ pdf/0_integral/067EN.pdf Galliani, L. (2004). La scuola in rete. Bari: Laterza.

57

Web Technologies and E-Learning Strategies for New Teaching Paradigms

Ministry of Education (2003), Piani di Studio Personalizzati e Unità di Apprendimento, Annali dell’istruzione (National report: R.I.So.R.S.E.. project), 49(3-4), 164-169. Nonaka, I., & Takeuchi, H. (1995). The knowledgecreating company: how Japanese companies create the dynamics of innovation. New York: Oxford University Press. Rivoltella, P. C. (2003). Scuole in Rete e Reti di Scuole. Milan: RCS libri.

58

Thorpe, M. (2005). The Impact of ICT on Lifelong Learning. In C. McIntosh & Z. Varoglou (Eds.), Perspectives on Distance Education: Lifelong Learning and Distance Higher Education (pp. 23-32). Paris: Unesco. Trentin, G. (2003). Dalla formazione a distanza all’apprendimento in rete. Milan: Franco Angeli. Trentin, G. (2004). Apprendimento in rete e condivisione delle conoscenze. Milan: Franco Angeli.

59

Chapter 4

Teaching Dimension in WebBased Learning Communities Francesca Pozzi Istituto Tecnologie Didattiche – CNR, Italy

AbstrAct The article tackles the issue of the teaching dimension in computer-supported collaborative learning (CSCL) contexts. In particular, it describes two Web-based courses that were held in 2006—one by the Istituto Tecnologie Didattiche – CNR and one by the University of Genoa, which, while sharing the socioconstructivist theoretical framework, adopt different approaches as far as the teaching dimension is concerned: While in the former course tutors were asked to cover all the functions typically required by e-tutors, in the latter, experience functions were distributed across a variety of actors. The aim of the work is to foster reflections about strong points and weaknesses of the two approaches, thus leading to considerations concerning the applicability of the models even in contexts different from the original ones.

IntroductIon As it is well known, the use of telematics in the learning processes brings about radical changes in the educational context in that it allows students to interact with peers independently from any spatial or temporal constraint. CSCL (computersupported collaborative learning) is a very challenging research field investigating how collaborative processes carried out in online contexts may lead to the construction of new knowledge

and thus to learning (Cognition and Technology Group at Vanderbilt, 1991; Dillenbourg, 1999; Kanuka & Anderson, 1999; Scardamalia & Bereiter, 1994). In this context, several studies have been devoted to the tutor’s role, which is seen as a key factor for fostering an effective collaborative learning process. In this article, we address the teaching dimension, a more general concept concerning not only the tutoring function, but rather all the initiatives and actions undertaken during a

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Teaching Dimension in Web-Based Learning Communities

course with the aim of supporting collaboration and learning processes of the learning community. The concept is wide in that it may refer to actions and behaviours by any actor involved in the learning community, including students. In particular, the work aims at comparing two courses while sharing the socioconstructivist theoretical framework, adopted different approaches as far as the teaching dimension is concerned. Thus, the article describes the different choices and reflects on strong points and weaknesses of the two approaches.

tHeoretIcAl bAckground In CSCL contexts, one of the basic assumptions is that discussion and negotiation among students may play a key role in the learning process because, while interacting and sharing their points of view, students develop their critical thinking and thus gain a better understanding of things. Thus, in order to allow and enhance interactions among students and between students and tutors, the actors of the learning experience should not act just as individuals, but rather they should feel like part of a community. Shaffer and Anundsen (1993) define a community as a dynamic whole that emerges when a group of people share common practices, are interdependent, make decisions jointly, identify themselves with something larger then the sum of their individual relationships, and make a long-term commitment to well-being (their own, one another’s, and the group’s). From this perspective, a learning community, that is to say, a community that has been set up with learning purposes, makes no exception, and it is up to designers and tutors to set up the learning environment in such a way that the community can “form, storm, norm, perform and finally adjourn” (Tuckman, 1965). A Web-based learning community is characterized by its size (the number of people involved), the general features of learners (background,

60

technical skills, age, gender, etc.), the roles members play, and the social structures employed in the various learning phases, which are usually coupled with the learning strategies adopted (Pozzi & Persico, 2006). All these characteristics have to be defined by designers according to the context and the learning goals, and are afterward managed by tutors and teachers during the course in such a way that students achieve the learning objectives. As a matter of fact, within CSCL contexts, the role of the tutor has always been recognized as being determinant. Even if nowadays it is more and more frequent to talk about human support—a general expression stressing the fact that support in CSCL may come from the various members of the learning community (other students, experts, technicians, etc.; Lund, 2004)—it is undeniable that tutors have a primary role in supporting the learning process in all its dimensions. In 1995, Berge enlighteningly identified four main functions of the online tutor: the social, the pedagogical, the managerial, and the technical functions. Similarly, Paulsen (1995) and Mason (1991) spoke about organizational, social, and intellectual roles. More recently, Anderson, Rourke, Garrison, and Archer (2001) defined what they call “teaching presence” in terms of “the design, facilitation, and direction of cognitive and social processes for the purpose of realizing personally meaningful and educationally worthwhile learning outcomes” (p. 5). The teaching dimension thus entails design and organizational aspects, discourse facilitation, and direct instruction (Anderson et al.). A tutor taking care of design and organizational matters creates working groups, assigns roles for carrying out the activities, identifies leaders, suggests the time schedule, and so forth. In order to carry out such functions, the tutor should be able to evaluate students’ individual competences, behavioural characteristics, and their personal interests to form groups accordingly. Furthermore, since during an online course

Teaching Dimension in Web-Based Learning Communities

the platform (usually a computer conferencing system) is the prime (and often the only) communication channel, it is very important that the tutor provides support in making the technology as transparent as possible for students. As far as facilitating discourse is concerned, the tutor is in charge of fostering the learning process by sustaining a generative debate, focusing discussion on key points, providing stimuli, and enhancing interactions while being flexible and nondirective. Besides these, he or she helps students in recognizing others’ contributions as meaningful and is able to attribute value to every participant by pointing out their specificities. Furthermore, the tutor has to make sure that written communication does not create inhibition. As a matter of fact, if computer-mediated communication (CMC) may reveal to be effective for those people who do not like face-to-face interactions, perhaps because they may feel more protected during a virtual exchange, handwritten communication may represent an obstacle for someone else because of a number of reasons, including the lack of immediate feedback, the impossibility to associate any gesture or facial expression to written text, the fact that one may perceive written communication to be too definitive, or even because one may feel alone when writing. For all these reasons, the tutor is in charge of making interactions fluent and guaranteeing effective and rich communication within the learning community. Finally, direct instruction entails proposing content or activities, monitoring the process, and assessing results. The three aspects pointed out by Anderson et al. (2001) as constituting the teaching presence cover the variety of responsibilities tutors are typically in charge of during an online collaborative learning experience. Nonetheless, depending on the context, tutors may share responsibilities with others (teachers, technicians, etc.); moreover, as time goes by and the learning community grows, responsibilities may sometimes be undertaken

by students, either on a voluntary basis or as an assigned task. In this article we describe the case of two Webbased learning communities the teaching dimension was interpreted in two different ways.

two exAmples oF web-bAsed leArnIng communItIes The present article describes two online courses carried out in 2006, that is, TD-SSIS, a teacher training course, and a Master in Specialized Legal Translation.

td-ssIs TD-SSIS was a blended course designed and run by the Istituto Tecnologie Didattiche - CNR in the context of the Italian system for teacher training (SSIS is the acronym for Specialization Schools for Secondary Teaching, the institutions that in Italy are responsible for teacher training.) The course, which in 2006 was in its seventh edition, had the goal “to promote the development of instructional design competence, with special focus on the evaluation and selection of learning strategies, techniques and tools and on the implementation of educational technology in the school context” (Delfino & Persico, in press). The course envisaged an alternation between face-to-face lectures aiming at presenting topics and online sessions aiming at carrying out collaborative activities. The TD-SSIS 2006 community was composed of 108 students of all disciplines, six tutors, and one technician. The course lasted 8 weeks involving 6 face-to-face lectures and 4 online modules (only the online component of the course is addressed here). The first module was devoted to socialization among the members of the community and familiarization with the communication system. During this phase, both students and tutors were asked to introduce themselves. Moreover, students, working in groups, were asked to collab-

61

Teaching Dimension in Web-Based Learning Communities

oratively choose a slogan for identifying their own groups. This activity served the double purpose of introducing collaboration as the prime working strategy from the very beginning of the course and of creating a common sense of identity within the groups. During the second module, that is, Educational Technology for School, learners worked in groups and were asked to analyse some online educational resources and collaboratively define a list of criteria for evaluating such resources. Within the third module, which was focused on the issue of educational technology and design principles, each group was asked to analyse an existing pedagogical plan, identify its weaknesses, and collaboratively propose possible changes. The peculiarity of the task was that it was carried out through role-play, where each learner was asked to act out a predefined profile. Role-play is a very useful collaborative technique allowing responsibility to be distributed between the participants and taking advantage of individual attitudes whilst promoting reciprocal interdependence (Renner, 1997). As a result, in TD-SSIS the artefacts produced by the groups should encompass the different viewpoints of all the members performing their roles. Finally, during the last module of the course, that is, Conclusions, an individual metareflection was required addressing the overall learning process. The platform used for carrying out the activities was Centrinity FirstClass® (http://www.centrinity. com), a computer conferencing system that can be easily configured in conferences and subconferences to allow easy communication among a large number of students working on different activities. In particular, since the structure of the course envisaged four modules, the FirstClass learning environment was organized into four main conferences (one for each module), which in turn contained subconferences for each working group. In addition to the conferences devoted to the learning activities, other conferences were aimed at supporting participants. In particular, the Meta-Reflection area was devoted to gather

62

considerations and reflections about the learning process at hand, the Café conference was intended to be an asynchronous chat room for socialization, the Help conference gathered all the messages concerning any technical problems learners may face while using FirstClass, News contained any new useful information, and Library was used as a repository for all the learning materials to be used during the course (Figure 1). As far as the social structure of the learning community is concerned, a great constraint was represented by the large number of students that, as it was, did not allow the adoption of collaborative strategies. Thus, the idea underpinning the design of the course was that of creating smaller groups working in parallel to allow collaboration. Groups were not fixed for the whole duration of the course: During the first phase of the course (first and second modules), the community was divided into 6 interdisciplinary groups (18 to 20 persons each), whilst during the second phase (third and fourth modules), groups were rearranged, and 14 disciplinary groups were created (7 to 9 persons each). Such change was due to both logistical and didactical reasons and caused a turnover of tutors as well, who tutored one group during the first stage and managed different groups during the second stage of the course.

master F@rum The Master in Specialized Legal Translation (simply Master F@rum in the following) is a Webbased postdegree specialization master’s course designed and run by the Foreign Language Faculty (University of Genoa) within the F@rum project (http://www.farum.unige.it). In 2006 Master F@ rum carried out its fourth edition. The main aim of Master F@rum was to develop competences and skills in the specialized legal translation field for translators who work in local and international organizations, companies, and translation agencies. Furthermore, the course aimed at developing both skills in the usage of computers for translating

Teaching Dimension in Web-Based Learning Communities

Figure 1. The TD-SSIS main conferences in FirstClass

and collaborative attitudes within the professional context (Rossi & Sarti, 2004). The course was run completely online and only the final exam was carried out face to face. The 2006 edition lasted 25 weeks and included both assisted theoretical modules and practical ones. As far as the theoretical modules are concerned (i.e., terminology, Italian law, English law, French law, German law, informatics), students were provided with learning materials and then were asked to carry out individual tests for verifying their understanding of the studied materials. As for the practical modules (i.e., translations), they included both individual and collaborative translations (we focus exclusively on the collaborative modules). During the collaborative activities, groups were asked to work on a text and produce the translation into the required language. In parallel with didactical activities, students took part in a simulation “where they face authentic (i.e. resembling real worlds complexity) problem situations. To this aim learners are grouped to form virtual translation agencies, where they work as if employed in a real company, meet in dedicated virtual spaces, fight external competition, etc.” (Rossi & Sarti, 2004). As we will see in the fol-

lowing, the simulation covered the whole duration of Master F@rum and entailed both didactical activities and social games. The learning community of the course was composed of 41 students, 8 tutors, 1 technician, and 12 teachers. Each teacher was responsible for her or his module and thus was in charge of preparing and launching activities according to the course calendar, offering expertise on content, and assessing learning outcomes. Students were subdivided into 8 groups (5 to 6 persons each) whose composition was determined by their specialization languages. Groups were fixed and each of them had its own reference tutor, who was in charge of supporting the group mainly in organizational aspects (deadlines, problems with technicalities, etc.). The communication system used within the course (called deneb.pro) is a Web-based application that was specifically created for Master F@ rum and is organized into virtual rooms; rooms are devoted to the specific subject matters (one module to one room) where students may find learning materials and have access to forums for contacting teachers. In addition, there are rooms for groups (one room to one group) where

63

Teaching Dimension in Web-Based Learning Communities

collaborative activities are carried out; within such rooms it is possible both to interact through asynchronous forums and to share documents (Figure 2). Finally, there is a common room where students may address general logistical or technical questions; this room is guarded in turns by all the tutors. The Café area of deneb.pro is a synchronous chat room.

socIAl/cognItIVe processes And teAcHIng dImensIon As we have seen, in TD-SSIS, during the Socialization module, tutors acted mainly as social promoters, whilst during the following didactical modules (Modules 2 and 3, and Conclusions), they took charge in all the aspects of the teaching dimension, that is, design and organizational aspects, discourse facilitation, and direct instruction. In particular, during the didactical activities, tutors launched the learning activities, provided

feedback, moderated discussions, and organized and managed working groups. Besides these, it is worthwhile noting that after the initial strong socialization input, the schedule of the course did not envisage any other activity directly aimed at fostering the social presence (Rourke et al., 2001): The Café and the Meta-Reflection conferences, which could be used for social purposes, were optional and free. This implied that it was up to the tutor and to his or her personal attitudes to take care of the social dimension of groups. From this perspective, the already mentioned rearrangement of groups that occurred after the second module certainly did not help in that at the beginning of the third module, tutors have activate personal initiatives for getting around the fact that people did not know each other. Altogether, in the TDSSIS experience, tutors were completely in charge of all the aspects of the teaching dimension. The only exception was represented by Module 3, where within the role-play, two students per group were each assigned either the role of school di-

Figure 2. A group room in the Master F@rum communication system

64

Teaching Dimension in Web-Based Learning Communities

rector or rapporteur, and so they were entailed to coordinate and facilitate discussion, thus partially undermining the tutor’s role. Within Master F@rum, the approach was quite different. As far as the start-up of the course, an initial socialization phase was not envisaged and a personal introduction either by the students or the tutors was never explicitly required; rather, the learners, who worked in groups from the very beginning, were required to collaboratively set up a virtual translation agency and to elaborate a document presenting it. As already mentioned, the peculiarity of the course consisted of the simulation, which acted as the real catalyst of the social presence by fostering interaction, collaboration, and a positive attitude toward distance learning (Bricco & Rossi, 2004). According to the simulation, the virtual agencies had been selected by the European Union (EU) for participating in a tender, and thus during the course, a certain number of collaborative activities were proposed and, according to the results obtained by the groups, a classification was created. The simulation was managed by two ad hoc tutors (EU consultants) who had the specific role of lunching the simulation activities. Such activities involved either didactical, collaborative tasks (aiming at fostering the collaboration process within the groups and a certain competition among groups) or social

games (thus aiming at simply creating a friendly climate). The simulation, which started from the beginning of Master F@rum and ended with it, contributed to the creation of relationships among members of the community. Thus organized, Master relied on different actors as the main catalysts of the teaching dimension: teachers, who were in charge of individual activities and student assessment; tutors, whose responsibilities were shared between EU consultants, in charge of collaborative processes and in general at supporting the social presence of the community; and other tutors acting in the proper sense of the word, who never took directly the responsibility of facilitating the discourse, but rather were asked to provide logistical information, remind students of deadlines, provide technical support, and so forth. In Figure 3, the two different approaches are graphically represented.

dIscussIon When looking at the two experiences, one may easily infer that within Master F@rum, functions were distributed among the various actors (teachers, tutors, EU consultants) while in TD-SSIS, each tutor was required to act at different levels.

Figure 3. The two different approaches to the teaching dimension

65

Teaching Dimension in Web-Based Learning Communities

In particular, it is interesting to notice that within Master F@rum ad hoc tutors were expressly appointed to take care of collaborative and social processes and had been definitely differentiated from the other tutors (in charge of the managerial and technical functions). In other words, actions aimed at fostering the social processes came from external entities (i.e., the EU consultants) who did not participate in the everyday life of groups, while tutors who did follow group life were assigned to a “cooler” role. Consequently, neither EU consultants nor tutors were considered part of groups and, apart from seldom exceptions, no strong relationships between tutors and students emerged from the course. On the contrary, within TD-SSIS, stimuli aimed at enhancing collaboration always came from inside the group. Tutors were integral parts of groups and, despite the new arrangement of groups that took place in the middle of the course, tutors often made friends with students. Furthermore, while the external action provided by the EU consultants in Master F@rum for fostering collaboration (i.e. the simulation activities) was structured in activities and spread throughout the course, the stimuli provided by TD-SSIS tutors were unstructured and, with the exception of the initial socialization activity, they largely depended on the single tutor’s attitudes and initiatives. This latter element seems to indicate that while the Master F@rum approach could be adopted even in contexts where there are novice tutors (because tutors’ actions are in a sense limited and strictly guided), the TD-SSIS approach should be taken by professionals, whose competences and sensibility toward students allow them to appropriately decide whether, to what extent, and how to manage situations. It is also worthwhile noting that the workload for tutors turned out to be very different in the two experiences: While tutors of Master F@ rum could dedicate a few hours per week to the course, a daily presence was required of tutors in TD-SSIS, often with a nontrivial amount of

66

hours per day. This suggests that while the latter approach is adoptable mainly in research contexts, where it is possible to dedicate full-time tutors to the activity, the former may turn out to be more transferable even to nonacademic contexts. Another interesting factor emerging from the comparison of the experiences is the difference in effort for design and coordination. In Master F@rum, teachers were considered responsible for designing their own modules, so managers of Master F@rum were in charge of the overall planning and coordinating of all the actors involved in the teaching dimension (teachers, tutors, EU consultants) during the whole duration of the course; on the contrary, the TD-SSIS staff devoted considerable time and work to the initial design phase, and once the course had started up, they left tutors quite free to manage things autonomously. As a last remark, it is important to say that, despite differences, both the courses reported good results in terms of participant assessment (marks were quite high), dropouts (which were very few in both cases), and students’ overall satisfaction.

conclusIon The present article contains the description of two examples of Web-based learning communities based on a socioconstructivist theoretical framework. Despite the theoretical similarities, the two experiences present differences in the strategies adopted as far as the teaching dimension is concerned. This has lead to reflections and considerations concerning the applicability of these models even in contexts different from the original ones. The research study is in its embryonic stage. Further investigations are to be carried out on the two experiences in order to systematically analyse and evaluate the teaching dimension. To this aim, an evaluation model recently elaborated

Teaching Dimension in Web-Based Learning Communities

(Pozzi, Manca, Persico, & Sarti, 2007) is being used for capturing the existing relationships among the participative, social, cognitive, and teaching dimensions. These dimensions will also be compared to responses obtained from students and data from other sources (e.g., data on dropouts and data from the students’ final questionnaires). Preliminary results concerning Master F@rum have already been published (Lupi, Pozzi, & Torsani, 2008). Further results may inform and improve existing approaches concerning the teaching dimension.

reFerences Anderson, T., Rourke, L., Garrison, D. R., & Archer, W. (2001). Assessing social presence in asynchronous text-based computer conferencing. Journal of Distance Education, 14(2). Berge, Z. L. (1995). Facilitating computer conferencing: Recommendations from the field. Educational Technology, 35(1), 22-30. Bricco, E., & Rossi, M. (2004). La simulation globale à l’épreuve de la formation à distance: Un fil d’Ariane nécessaire. Information, Savoirs, Décisions & Médiations: Information Sciences for Decision Making, 18. Retrieved January 14, 2008, from http://isdm.univ-tln.fr/PDF/isdm18/53-rossibricco.pdf Cognition and Technology Group at Vanderbilt. (1991). Some thoughts about constructivism and instructional design. Educational Technology, 31(10), 16-18. Davie, L. E. (1989). Facilitation techniques for the on-line tutor. In R. Mason & A. R. Kaye (Eds.), Mindweave: Communication, computers and distance education. Oxford, United Kingdom: Pergamon Press. Delfino, M., & Persico, D. (in press). Online or face-to-face? Experimenting with different tech-

niques in teacher training. Journal of Computer Assisted Learning. Dillenbourg, P. (Ed.). (1999). Collaborative learning: Cognitive and computational approaches. Pergamon. Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105. Kanuka, H., & Anderson, T. (1999). Using constructivism in technology-mediated learning: Constructing order out of the chaos in the literature. Radical Pedagogy, 1(2). Lund, K. (2004). Human support in CSCL: What, for whom, and by whom? In J.-W. Strijbos, P. Kirschner, & R. Martens (Eds.), What we know about CSCL and implementing it in higher education (pp. 167-198). Dordrecht, The Netherlands: Kluwer Academic Publishers. Lupi, V., Pozzi, F., & Torsani, S. (2008). La dimension sociale dans un master à distance, postuniversitaire et de traduction juridique. Apprentissage des Langues et Systèmes d’Information et de Communication (ALSIC), 11. Mason, R. (1991). Moderating educational computer conferencing. DEOSNEWS, 1(19). Retrieved January 14, 2008, from http://pchfstud1.hsh.no/ hfag/litteratur/jenssen/deosnews/mason.htm Paulsen, M. P. (1995). Moderating educational computer conferences. In Z. L. Berge & M. P. Collins (Eds.), Computer-mediated communication and the on-line classroom in distance education. Cresskill, NJ: Hampton Press. Pozzi, F., Manca, S., Persico, D., & Sarti, L. (2007). A general framework for tracking and analysing learning processes in CSCL environments. Innovations in Education & Teaching International (IETI) Journal, 44(2), 169-179.

67

Teaching Dimension in Web-Based Learning Communities

Pozzi, F., & Persico, D. (2006, June 14-17). Evaluation in CSCL: Tracking and analysing the learning community. In A. Szücs & I. Bø (Eds.), E-Competences for Life, Employment and Innovation: Proceedings of the EDEN 2006 Annual Conference, Vienna (pp. 502-507). Renner, P. (1997). The art of teaching adults: How to become an exceptional instructor and facilitator. Vancouver, Canada: The Training Associates. Rossi, M., & Sarti, L. (2004). Playing touch and tender: Tutoring strategies in a university master. Information, Savoirs, Décisions & Médiations: Information Sciences for Decision Making, 18. Retrieved January 14, 2008, from http://isdm. univ-tln.fr/PDF/isdm18/56-sarti-rossi.pdf

Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. The Journal of the Learning Sciences, 3(3), 265283. Shaffer, C., & Anundsen, K. (1993). Creating community anywhere. New York: Jeremy P. Tarcher, Perigee Books. Stacey, E. (2002). Social presence online: Networking learners at a distance. Education and Information Technologies, 7(4), 287-294. Tu, C.-H. (2002). The measurement of social presence in an online learning environment. International Journal on E-Learning, 1(2), 34-45. Tuckman, B. W. (1965). Developmental sequence in small groups. Psychological Bulletin, 63, 384399.

This work was previously published in the International Journal of Web-Based Learning and Teaching Technologies, Vol. 3, Issue 3, edited by L. Esnault, pp. 34-43, copyright 2008 by IGI Publishing (an imprint of IGI Global).

68

69

Chapter 5

Management of Lecture Time: Using the Web to Manipulate Extrinsic Cognitive Load Michael A. Chilton Kansas State University, USA Anil Gurung Neumann College, USA

AbstrAct The variety of new technologies available for classroom use requires a choice not just between the technological options, but among them as well, since an educator may choose a single option or include a mix of media. In this study we investigate a particular mix of advanced technology and its effect on student learning outcomes. Our experimental design compares outcomes from a traditional teaching format with those of a more advanced web-based format. Our model is based on cognitive load theory, is developed from perceptions of the students, and is analyzed using factor analysis. The results based on this qualitative model show promise for delving further into the assessment of learning. This would provide researchers with additional tools to help evaluate their results and educators with a basis on which to make decisions regarding which advanced technologies to use.

IntroductIon The traditional method of teaching, in which instructors, considered to be subject matter experts, present material to a classroom of students who then supplement their in-class material with other forms of reinforcement (readings,

textbooks, homework, etc.) is argued to be both the best and the worst form of instruction (cf. e.g., Kirschner, Sweller, & Clark, 2006; Michael, 2006). The overwhelming volume of research devoted to teaching and learning is evidence that most researchers believe that something better is out there. As new technology is introduced

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Management of Lecture Time

into society, it is quickly adopted for classroom use and often claimed to be a better method. Yet recent research seems to indicate that most new technologies (multimedia classrooms, distance learning with audio and video feeds and computerbased training) has not lived up to its potential to achieve better learning outcomes (cf. e.g., Lou, Bernard, & Abrami, 2006; Weston, 2005). Is there a better method that utilizes advanced technology? Although the answer to this question will likely remain elusive for some time, the literature has identified several key dimensions that any ap-proach to improving learning outcomes must address. Mayer and Moreno (2003) present a theory of multimedia learning that is based on how the human mind processes information. In their presentation they cite five different scenarios which might result in cognitive overload, but also offer nine methods based on 12 years of research to alleviate induced overload. Their methods are based primarily on presentation of the material, but also include focusing the material only on information relevant to the material needed to learn (i.e., excluding extraneous material), and avoiding presenting redundant material. Cognitive load theory emphasizes that the major objective of instructionis toassistthelearnerinforming and automating schema (Paas, Tuovinen, Tabbers, & Van Gerven, 2003). Schema are elements of information that are held together in long term memory, and upon automation, can help the individual by bypassing short term memory, which is limited in its ability to process infor mation (Paas, et al., 2003). Clearly then, any approach to improving learning outcomes must include these dimensions of presentation, clarity or focus, and practice which helps to induce the development and automation of schema. In that light, we investigate a method of teaching that utilizes advanced technology in its attempt to improve learning outcomes, but must be managed properly in order to obtain a greater benefit than traditional methods. We suggest that traditional lectures, which can be viewed ahead of time by students using Web-

70

based technology with which they are very familiar, coupled with classroom time that is devoted to addressing issues of confusion or ambiguity in the students’ comprehension of lecture material, along with time devoted to solving problems or investigating salient examples, can provide this greater benefit. Advanced technology allows the student to view or listen to the recorded lecture at any time prior to the scheduled class time at just about any place. Lecture audio and video can be prerecorded along with instructor notes and other visual aids and stored on a Web site to which the students are given access. The student can either visit the site with a Web browser and view the lecture immediately or can download it and view it at a later time, even to the point of downloading it to a handheld device and viewing it while engaged in other activities, such as during exercise periods or while having lunch. Once viewed, the student is then prepared to explore the topics presented in the lecture in more detail. Classroom time can then be devoted to answering student questions about the lecture or to working problems and examples to further reinforce the concepts presented in the lecture. The purpose of this article is to present the results of an evaluation of such a system of learning by comparing it to a strictly traditional method. In so doing, we also offer a model of extrinsic cognitive load based on perceptions of the students using factor analysis.

tHeorY And model Research into human cognition and learning has focused on its architecture, that is, working and long term memory, how these structures interact, and the processing of elements in working memory into schemas that can be shifted into long term memory for later use (Paas & Kester, 2006). Working memory is limited in its ability to process a number of elements simultaneously. It is theorized to hold only seven items simultaneously (Miller, 1956) and this number is further

Management of Lecture Time

restricted when processing interacting items (Sweller, VanMerrienboer, & Paas, 1998). This limitation of working memory inhibits learning (Ayres, 2006). Long term memory represents the subconscious storage of items, which are usually organized into schema and are accessed through the filtering mechanism of working memory (Swelleretal.,1998). This portion of the human cognitive architecture is what allows people to overcome the limitations of working memory (Kalyuga, 2006). By storing integrated, related and categorized facts in long term memory (schema), a person can utilize this knowledge quickly and easily without getting bogged down by the limitations of working memory. An expert in any particular field can rely on these schemata to assist in the processing of tasks in a way far different from the novice. The novice requires more elements in order to process the same tasks and can become overloaded due to the constraints of working memory. Consider playing the piano, for example. The student must translate notes on a page to keys on the piano, taking timing of each note into account. The skilled pianist, however, has already learned these basics and has been able to integrate them into schema that have become part of long term, subconscious memory. This allows the skilled pianist to focus working memory on processing other characteristics of the music, such as the harmonious blending of the piano with the rest of the orchestra. Schema construction is the ability to shift large chunks of knowledge into long term memory (Kalyuga, 2006) so that it can be accessed in working memory as a single unit (Sweller et al., 1998). There is no apparent limit to the amount of information that can be processed from constructed schema (Sweller et al., 1998). Even more important is that schema can be used automatically, bypassing working memory altogether and allow a person to complete a task with minimal conscious effort (Sweller et al., 1998). The pro-

cess of schema construction and its subsequent automation is a central tenet to cognitive load theory because it is focused on how instruction, instructional aids and instructional formats can be designed to enhance the ability of working memory to construct and automate schema (Paas, Renkl, & Sweller, 2004). Although the processes used in long term memory are addressed by cognitive load theory, the research has dealt mostly with the conscious portions of the cognitive architecture, as this is where schema construction begins. Cognitive load theory posits that learning is dependent upon the student’s ability to deal with two types of cognitive pressure or loading, intrinsic and extrinsic (Paas & Kester, 2006). Intrinsic cognitive loading is a function of the complexity of the material to be learned with the student’s ability to process the material. Extrinsic cognitive loading is a function of the presentation of the learning material and the learning activities required of the student. Intrinsic cognitive loading represents the interaction of elements that must be processed by the student in order to learn the new material and the student’s level of expertise already achieved within the domain of content (VanMerrienboer & Ayres, 2005). As the level of expertise increases, the number of elements to be processed can also increase and still achieve learning in an efficient manner. In situations in which the student has little expertise in the area of study, however, the number of elements requiring processing increases intrinsic cognitive loading. As intrinsic load increases, extrinsic load is reduced. While this may appear to be the desirable result because it is caused by a greater amount of element interactivity, the effect is actually to hinder learning efficiency. It occurs because of the limited capacity in short term working memory, and the additive nature of intrinsic and extrinsic load (Paas & Kester, 2006; Paas et al., 2003). Thus, if intrinsic load increases, less working memory is available to process the demands of extrinsic load. Extrinsic cognitive load is further subdivided into two

71

Management of Lecture Time

parts: extraneous load, or load that is affected by the learning environment, and germane load, or load that is affected by the integration of learning elements into schema. Expertise results from the effective formation of these schemata into long term memory such that they can be utilized in an automatic fashion without drawing upon precious working memory resources (Van Merrienboer & Ayres, 2005). Given the components of extrinsic cognitive load, the literature recommends certain actions on the part of instructional designers. The first is that controlling the cognitive load within an instructional environment is critical to achieve meaningful and efficient learning outcomes (Paas & Kester, 2006). The second is that instructional formats should only be adjusted as they relate to complicated tasks, or those that present high intrinsic load. The concept here is that with simple tasks, the stuÂ�dent is able to devote some of the “freed-up” intrinsic load to understanding the instructional format and compensate for a poor instructional design. As the complexity of the task increases, intrinsic load also increases and instructional format therefore takes on a greater role in the learning process. Thirdly, for complex learning environments where a reduction in extraneous load can and should be applied, improving the instructional technique may provide this reduction in extraneous load and thereby free up working memory to be devoted to germane load. Germane load is associated with processes that are directly relevant to learning (Van Merrienboer & Ayres, 2005). Taking these recommendations into account, our mission is to devise a learning format aimed at learning complex tasks so as to reduce extraneous load and increase germane load to facilitate schema formation and automation (Paas & Kester, 2006). An additional method by which schema formation is enhanced is to increase the variability in the problem space (Van Gerven, Paas, van Merreinboer, & Schmidt, 2006). Variability is measured by the extent to which examples,

72

problems or learning situations may differ. When variability is high, students are forced to look for similarities among the various situations they are presented, and in so doing, will begin to form schema that yields better learning. This process of thoughtful engagement is the natural result when a student tries to categorize, classify, allocate, systematize, order, or otherwise make sense of new material. Our learning format design must therefore at�tempt to increase the variability of the elements involved and reduce the requirement that the student spend time on understanding the format instead of learning the material. Given this discussion of extrinsic cognitive load, we might formulate the model depicted in Figure 1. The three dimensions of extrinsic load that we cite from the literature become those factors on which we focus in order to design a suitable learning format and on which we can rely to ensure that our design actually does af� fect extrinsic cognitive load and not some other confounding variable(s).

APPLICATION AND EXPERIMENTAL DESIGN In consonance with the objectives recommended in the literature and our resultant model pre�sented above, we designed a learning environ�ment that made use of specialized technology with the intent of reducing extraneous load, and increasing germane load by presenting maximal variability in the problem domain. By utilizing a technology that students were familiar with and could utilize on their own time, we were able to better utilize class time toward schema creation and automation, that is, increasing germane load. In addition, class time was then used for answering questions that students had and to demonstrate how to work examples that related directly to the problem domain. Utilizing worked examples has been shown to reduce extraneous load (Sweller et al., 1998) over that of requiring students to

Management of Lecture Time

Figure 1. Perceptual model of extrinsic cognitive load

Extrinsic Load

Variability

Instructional Design

work through examples on their own. We wanted to determine whether lectures presented in a recorded format, which would allow the freeing up of class time for questions and problem demonstration, would improve learning by reÂ�ducing extraneous load and increasing germane load. To this end, we prerecorded all lectures for three different courses within the Management and MIS curricula: Operations Management, Computer Networking and Database Concepts and Design. These lectures were posted on a Web site that utilized a subscription service so that students were notified automatically when new content was available. Lectures could be viewed immediately at the student’s workstation or downloaded and viewed later, including to personal devices (e.g., ® an iPod ). Lectures were to be viewed prior to class time so that students could attend class fully prepared to discuss issues they may have had with the lecture material. Lectures were not repeated during class time, and students were quizzed to help motivate them to viewing the lectures ahead of time. They were also expected to be prepared to work on specific problems and examples that would help to provide the variability necessary to cause the student to seek similarities in the

Student Engagement

problems and examples and to assist them in the creation of new schema. This step would help to increase germane load. Students from six sections of the three classes were involved in this experiment over a period of two semesters at a major Midwestern university. One section in each class was sub�jected to the teaching format described above (n = 95) and a second section was used as a control group (n = 100) by presenting them with the live version of the recorded lectures during their regularly scheduled class time. The control groups were also provided lecture notes ahead of time and were asked to read relevant sections in the textbook prior to class so as to control for the amount of time spent on course material. Assessments were taken of learning outcomes at equal points throughout the semesters and a comparison of results was performed. In addition, the treatment groups were asked to complete a questionnaire that allowed them to report their own perceptions of the effectiveness and satisfaction with the recorded lecture format. It is our belief that if cognitive load theory holds and that our treatment groups had their extraneous load reduced and germane load increased through

73

Management of Lecture Time

the use of our instructional format, then these groups would achieve bet�ter learning outcomes than the control groups. Stated in hypothesis format, this is: H: Better learning outcomes can be achieved through recorded lectures. There are two factors affected by the instructional format used in this experiment that would help to determine the outcome. The first is the reduction of extraneous load. Our experimental design allows the student to focus more on material presented prior to class and to utilize these new concepts in classroom exercises designed to strengthen and reinforce these concepts. Extraneous cognitive load is thus reduced because the student is not distracted by other students or classroom disturbances during the initial presentation and may replay any portion if he or she felt it to be unclear. These students were also not expected to teach themselves, but were provided guided instruction prior to class time. Secondly, we increase germane load. Because the learning process could now focus on examples to reinforce the underlying principles instead of merely explaining them, content variability is increased within each of the treatment groups. Control groups were presented information in class that they had previously reviewed before class, but treatment groups were assumed to have had this exposure and could then make more productive use of class time by exploring the problem space further through the use of examples, more in depth class discussion and by addressing questions formed in advance.

METHOD In order to test the hypothesis, we used metrics to assess learning outcomes that consisted of both quantitative and qualitative measures. Our quantitative measure consisted of the compari-

74

son of test scores between the treatment and the control groups. Identical tests were given on the same days to both control and treatment groups. For these quantitative measures, we would be able to sustain our research hypothesis if we found a significant performance difference between treatment and control groups reflected in their scores on the same exam. Our qualitative measure consisted of a comparison of the amount of material covered in each course between treatment and control groups. We felt that if the classes had covered more material within the same amount of time, the class with the greatest coverage would have achieved a better learning outcome. In terms of cognitive load theory, greater schema construction would have occurred. We could sustain our research hypothesis on this basis if the treatment groups had covered considerably more material than the control groups. A second qualitative measure was also administered to students within the treatment group to help us assess the perceptions of the students regarding their learning experience and express either satisfaction or dissatisfacÂ�tion with the learning format. We developed a survey instrument to obtain these perceptions and used it for two purposes. First, it served as a manipulation check for our experimental design, that is, had we effectively isolated variables that we had postulated would affect extrinsic load? Secondly, we felt that if student satisfaction was high for the recorded lecture format, this would be an indication that the students had not felt overloaded with extraneous information. While a self-assessment such as this may not seem appropriate, rating scales have been shown to be effective quantitative measures of mental burden (Paas et al., 2003). In order to gauge the suitability of the survey instrument, we submitted the results to factor analysis and checked it for reliability (using coefficient alpha) and for validity usÂ�ing exploratory techniques. The items used in the instrument were designed to assess the students’ perceptions of

Management of Lecture Time

this decrease was not statistically significant due to the high variÂ�ance in both sets of scores. For the operations management class, scores were about the same in both control and treatment groups for both tests, and for the database class, the scores on the first exam were about the same, but there was a statistically significant improvement for the control group in exam 2. The quantitative data, with the exception of exam 2 for the database class, do not seem to conclusively support the hypotheses, except in one case. Qualitative assessments show that although one of the treatment classes was unable to proÂ�ceed farther into the material than the control class, two other treatment classes were able to do so. One was the database concepts and design class in which material that covered stored procedures was introduced and mastered by the students. Stored procedures are not norÂ�mally covered in such a course and students’ mastery was made evident by their ability to include five multipart stored procedures in their semester database design projects. These stored procedures provided answers to nontrivial business questions that were a part of the overall project. They were written in both procedural and nonprocedural (SQL) code ® and successfully executed on an IBM DB2™ database server. This is a significant result not just because of its magnitude, but because this class also significantly outperformed the control group on exam 2. Exam 2 was administered after

their own learning achieved and whether they felt that their time was well allocated. Our reasoning is that if the items show good convergence on factors that are associated with the components of cognitive load theory, then the students themselves can tell us whether or not we have achieved what we sought to achieve and whether this result was brought about by these components. That is, if a group of items which was designed to elicit responses regarding the effectiveness of the teaching format showed good reliability and validity, this would provide us with support that what we measured actually resulted from a reduction in extraneous cognitive load or from good use of germane load. Measurement of the reduction of extraneous load was linked to items that focused on class time utilization and student assessment of the recorded lectures. Measure�ment of germane load utilization was reflected by items that focused on student involvement, interest and level of understanding. The instru�ment itself is provided in the appendix.

RESULTS Quantitative comparisons between the classes are tabulated in Table 1. The table depicts the aggregated results of the two exams given within each semester and are listed by class. For the networking class, the exam scores actually decreased, but

Table 1. Exam results for control and treatment groups Control group

Treatment group

n

Exam 1

Exam 2

n

Exam 1

Exam 2

Networking

25

73

75

22

68

69

Database

28

74

65

16

76

78**

Ops Mgt.

47

76

81

57

75

82

Total

100

95

** p < 0.01 n total = 195 Scores are out of 100 possible points

75

Management of Lecture Time

the class had been exposed to the material on stored procedures. The other class was computer networking concepts, in which students were able to accomplish two additional labs that required using software to simulate several large networks and analyze the effects of adding more bandwidth capability. Measurements during the simulation included four services: file transfer, Web site acquisition, e-mail and database application processing. The measurements were taken one at a time and results were plotted in color coded graphs for easy viewing and interpretation. Students were required to submit written reports that documented their activities for each lab and their observations (i.e., learning achieved). These labs were also nontrivial in nature and required the application of knowledge gained during the semester. For these two classes (the database concepts class and the computer networking class), the qualitative assessment of the progress of the treatment groups was significantly higher than that for the control groups. A second qualitative assessment was performed using the attitudinal instrument discussed

earlier. Our statistical analysis of the student survey responses demonstrated a reliability using coefficient alpha of 0.81, thus showing support for a reliable instrument. We then subjected the instrument to factor analysis and these results are presented in table 2. A three factor solution was determined to be best by evaluating the amount of variance explained as the number of factors was increased. A three factor solution accounts for 85% of the variÂ�ance and provides the best factor pattern. Items were placed in the table descending order by factor. Items with less than a 0.4 loading were removed. The gist of each item is shown in the last column to provide clarity. Factor 1 seems to include those items that are affected by the variability of the problem space. Items that reflect such variability would indicate the thoughtful engagement on the part of the student, and this appears to be what these items are showing. Such variability is reflecÂ�tive of germane load. This factor seems to be reflecting the variability construct depicted in Figure 1. Factor 2 includes items that reflect the student’s ability to utilize class time. One would surmise that the instructional format would affect this

Table 2. Factor analysis of student responses Factor 1

Factor 2

Factor 3

item 1

0.8385

.

.

Increase level of understanding

item 6

0.6825

.

.

Learned more

item 4

0.6695

.

.

Interested in topics

item 7

0.6432

.

.

Improve study habits

item 12

0.6406

.

.

Ask more questions

item 8

0.6124

.

.

Better instructor aid

item 9

.

0.6735

.

Better utilized class time

item 11

.

0.6095

.

More interesting class time

item 10

.

0.5129

.

More participation in class

item 2

.

.

0.8199

Bored

item 5

.

.

0.7783

Waste of my time

n = 95 (items with loadings less than 0.4 are excluded)

76

Management of Lecture Time

result because a good format would reduce the need to devote portions of working memory to extraneous load, whereas a poor format would not. This factor seems to be reflecting the instructional design construct depicted in Figure 1. Factor 3 includes items that reflect the degree to which each student feels comfortable with the amount of time required by the instructional format. If a student were bored or considered it a waste of time, then thoughtful engagement might not occur; conversely, the lack of boredom would result in greater engagement. Such engagement is an indicator of germane load. This factor seems to reflect the student engagement construct of Figure 1. Commonly reported fit indices for the instrument are shown in Table 3. Values indicate a good model fit. This indicates that the data support the hypothesized model depicted in Figure 1. Although we cannot use this as a basis for sustaining our hypothesis, we can say that the perceptions of the subjects support our belief that the experimental design has isolated the factors that would affect extraneous load in our learning environment. The three factors of our model provide indications of measurement of the two types of extrinsic load, extraneous load and germane load. Germane load is indicated by two of the factors, variability and student use of study time, while extraneous load is directly indicated by the instructional format. The results of our data collection effort Table 3. Fit indices for the survey instrument Fit Index

Value

Goodness of Fit Index (GFI)

0.96

seem to support the model depicted in Figure 1, which is taken from cognitive load theory. As a check of construct and discriminant validity, we see no significant cross loadings in the data and each of the items loads on a single factor and appear to share a common meaning. Convergent validity is also supported because the test for the t-statistic for each item loading is significant (cf. Anderson & Gerbing, 1988). As a measure of student satisfaction with the recorded lectures, an additional four-item survey was completed by those who were exposed to the recorded lecture format (the treatment groups). Each item was scored electronically and converted to a percentage. The results are tabulated in Table 4. Each metric listed in Table 4 was a single item on the survey and represents only the proportion of students who expressed satisfaction with the recorded lecture format. Overall, the students indicated a 58% satisfaction rate. The purpose for the satisfaction survey was to gauge interest level in the instructional format. If the students are generally not satisfied with the method, this would suggest that their motivation might be lower, which would affect their learning outcomes. It might also adversely affect their perceptions toward variability, instructional design and engagement, which would skew the results of the factor analysis performed above. Because this was not the case, we feel that the students were generally satisfied with the recorded lecture technique and that they were sufficiently motivated toward using it to their best advantage. Table 4. Student satisfaction levels

Adjusted Goodness of Fit Index (AGFI)

0.85

Normed Fit Index (NFI)

0.93

Non-normed Fit Index (NNFI)

0.87 0.95

Recorded Lectures

Class Time

Course Material

Overall

Comparative Fit Index (CFI) Relative Fit Index (RFI)

0.81

57

59

54

58

n = 95

Percentage of Students Expressing Satisfaction with the Recorded Format

n = 95

77

Management of Lecture Time

DISCUSSION This study has attempted to link the precepts of cognitive load theory with a specific classroom instructional technique. Using a treatmentcontrol, post measurement only experimental design and both quantitative and qualitative metrics, we seem to show limited support for our hypotheses. Because our quantitative results are significant in only one class, repÂ�resenting only 17% of the possible tests, we cannot sustain either hypothesis on this basis. Our qualitative results show promise, but little statistical support for the hypotheses as well. By constructing a survey instrument designed to provide students with a self-evaluation of the instructional technique, we feel that we have gone farther into investigating the phenomenon and perhaps have uncovered another way to assess the learning outcomes. Though self-reporting surveys are often criticized for their inability to measure phenomena objectively, they have been found to possess high face validity and that subjects are quite capable of assessing their own mental effort and results (Paas, 1992; Paas et al., 2003). We therefore conclude that although we cannot sustain our hypotheses based on hard statistical numbers, we can suggest that the instructional technique works well. We can base this result on the ability of a significant portion of our treatment groups to master additional material over and above what is normally required and on the students’ own interpretation of their learning environment and skills. The survey we introduce suggests that students are aware of their own mental effort and achievement, and that their assessment is in line with cogniÂ�tive load theory. Their responses indicate that there is strong correlation between variability of the problem space, as suggested by the first factor; that expending mental energy in learnÂ�ing the instructional format is correlated with distraction which could alter student learning outcomes; and that the amount of boredom felt or the amount of time that was wasted can provide

78

an indirect measure of their mental engagement. Based on the results of the factor analysis (i.e., the data fit the theoretical model and in the correct direction), we can conclude that the instructional technique does lower extraneous load and engages the student more effectively (improves the use of germane load) and therefore helps to improve student learning outcomes. To further underscore our qualitative results, we provide some comments from both students and instructors regarding the recorded lecture format and technique. First, some student comments: I like the use of recorded lectures because it gives another aspect of the material and class. I like the separation of lecture and discussion teaching method. It allows me to watch lectures multiple times if needed, and then get more in­ formation in class about the online lecture. The in class discussions seem to get all the students more involved and thinking, rather than just listening to the lecture material. I think future students will like this method better. It was fun and I learned a lot. I like how I can watch the recorded lectures when I want. I also like how I can pause them and take notes as necessary. I think the recorded lectures help me learn the material better. I don’t like how I have to watch a lecture on top of going to class, but I think that we get more out of the class this way. And the instructor comments: Although the time requirement seems slightly higher using recorded lectures, the class periods became much more interesting. Students seem to interact more favorably and more often during the class time. It has the added advantage of provid­ ing you with feedback of how well the students are able to understand the material.

Management of Lecture Time

I believe this is a more efficient use of class time. Students and instructors seemed to prefer this type of presentation as evidenced by these comments. The study, of course, has certain limita�tions. First, we did not stratify the student samples based on ability or intelligence as this data was not available. This may mask these effects and result in a type II error. However, students involved in these experiments are all second or third year management students and as such have demonstrated abilities toward assimilating the information and concepts presented in these classes. We do not believe that there were sizable differences in ability or intelligence among these students that would skew our results. Second, instructors measured the amount of material covered in class while fully aware of the objectives of this study and so the results may be somewhat biased. However, a seasoned instructor should be able to gauge the progress of students and adapt the amount of covered material based on this assessment. The instructors involved in this study were all well seasoned and we feel that this should not have much of an effect on our results. Third, the survey instrument was tested using exploratory techniques and additional experimentation is required in order to validate the instrument. Only after additional research is conducted with the instrument can we use confirmatory analysis techniques. The classes chosen represent an on probabil�ity sample, which limits our ability to general�ize. In addition, we focused on specific classes required of management and MIS majors, which further limits our ability to generalize. Further research using different disciplines is required to allow much of our findings to be in general use; however, we feel that this is an important first look at this type of instructional format and that our findings are significant for the groups for which the measurement was taken.

Although we subjectively arrived at a decision that the amount of material covered in one class was significantly higher than another, this is based on expert opinion obtained after 12 years of teaching experience. This high face validity provides a good alternative for a measurement that would be difficult to quantify. Future research should be geared toward investigating more specific aspects of the prerecorded lecture format. The current study was general in nature because we looked at the entire process. There is much in the literature that can be tested at a finer granularity using the same format. For example, we find that some students have greater amounts of prerequisite knowledge in each class. For these students, learning may actually be inhibited because of the “expertise reversal affect” (Kalyuga, Ayres, Chandler, & Sweller, 2003). This effect occurs when an advanced learner is cognitively distracted by the presentation of elementary information. Students with greater requisite knowledge may fare even better using a recorded lecture technique if more advanced topics are available. Another example explored by Olina and colleagues (2006) is that of determining if the format of the problem or the order in which problems are presented reduce extraneous load. Applying this to the recorded lecture format might investigate whether students themselves are better able to pick a format and whether concepts can be learned more effectively in a specific order. Additionally, Sweller et al. (1998) and van Merrienboer and Ayres (2005) discuss problems that are presented without solutions, that is, with a nonspecific goal. Their research suggests that this is a more powerful method of reducing extraneous load because the learner need not consume limited short term memory resources with the goal state and the relation between the goal state and the current state, but is only required to process the current state using some operator that can be applied to that state (van Merrienboer & Ayres, 2005).

79

Management of Lecture Time

conclusIon This study is the first we know of to explore the use of recording technology to investigate the specific effects on learning outcomes bases on the theory of cognitive loading. The recorded lectures included in this study are placed in a format that is familiar to the student and can be accessed and viewed when the student wants. Because the study has certain limitations, it should not be taken as archetypical; however, it does represent a step toward refining the analysis of newer teaching methods. We have attempted to analyze the results of an innovative teaching technique that utilizes advanced technology in its presentation using the theoretical lens of cognitive load theory. We feel that although the results do not provide clear statistical evidence of its success, the qualitative measures do provide a strong indication that the technique can be put to good use in teaching environments in which the domain of content is complex. We caution, however, that the technique needs to be closely managed to ensure that students are using it properly and can come to class prepared to be fully challenged and engaged in the learning process. By doing so, it is our feeling that students will have more time for learning and have to devote less time to the distraction of the teaching technique itself. This balance of extraneous and germane load forms part of the essence of cognitive load theory.

reFerences

Hatcher, L. (1994). Astep-by-step approach to ® using the SAS system for factor analysis. Cary, ® NC: The SAS Institute. Kalyuga, S. (2006). Assessment of learners’ organized knowledge structures in adaptive learning environments. Applied Cognitive Psychology, 20(3), 333-342. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23-31. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instructions does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based learning. Educational Psychologist, 41(2), 75-86. Lou, Y., Bernard, R., & Abrami, P. (2006). Media and pedagogy in undergraduate distance education: A theory-based meta-analysis of empirical literature. Educational Technology Research & Development, 54(2), 141-176. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43-52. Michael, J. (2006). Where’s the evidence that active learning works? Advances in Physiology Education, 30(4), 159-167. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.

Paas, F. G. W. C. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429-434.

Ayres, P. (2006). The impact of reducing intrinsic cognitive load on learning in a mathematical domain. Applied Cognitive Psychology, 20(3), 287-298.

Paas F., & Kester, L. (2006). Learner and information characteristics in the design of powerful learning environments. Applied Cognitive Psychology, 20(3), 281-285.

80

Management of Lecture Time

Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advanced cognitive load theory. Educational Psychologist, 38(1), 63-71. Sweller, J., Van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296. Van Gerven, P. W. M., Paas, F., Van Merrienboer, J. J. G., & Schmidt, H. G. (2006). Modality and

variability as factors in training the elderly. Applied Cognitive Psychology, 20(3), 311-320. VanMerrienboer, J. J. G., & Ayres, P. (2005). Research on cognitive load theory and its design implications fore-learning. Educational Technology Research and Development, 53(3), 5-13. Weston, T. J. (2005). Why faculty did—and did not—integrate instructional software in their undergraduate classrooms. Innovative Higher Education, 30(2), 99-115.

81

Management of Lecture Time

AppendIx Survey items are listed below. Items 1 through 12 represent the items used in building the model depicted in Figure 1; items 13 through 16 are general satisfaction items. The student was asked to respond to each item using a semantic differential format which ranged from “completely” to “not at all.” Responses were electronically obtained and converted into a Likert type scale. Items marked with an asterisk were reverse-scored. When my instructor used a recorded lecture format, … 1. *2. 3. 4. *5. 6. 7. 8. 9. 10. 11. 12.

I was able to increase my level of understanding. I was bored. I was better able to utilize class time. I was more interested in the topics we studied. I wasted a lot of my personal time. I learned more about the topics we studied. I was able to improve my study habits. My instructor was better able to help me. Class time was better utilized. I participated more in class discussions. Class time was more interesting. I was able to ask more questions in class.

How satisfied are you with the following items? 1. 2. 3. 4.

The recorded lectures used in this class. Class time when recorded lectures are used. Course material when recorded lectures are used. Overall satisfaction with the course.

This work was previously published in the International Journal of Web-Based Learning and Teaching Technologies, Vol. 3, Issue 2, edited by L. Esnault, pp. 35-47, copyright 2008 by IGI Publishing (an imprint of IGI Global).

82

83

Chapter 6

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education Gérard Fillion University of Moncton, Canada Moez Limayem University of Arkansas, USA Thérèse Laferrière Laval University, Canada Robert Mantha Laval University, Canada

AbstrAct For the past two decades, information and communication technologies (ICT) have transformed the ways professors teach and students learn. This study aims to investigate the perceptions of onsite and online students and professors. It was conducted into ICT-supported or technology-rich environments at a Faculty of Administration of a large Canadian university. To conduct the study, a moderator-type theoretical research model was developed, out of which nine hypotheses were formulated. The authors used a multimethod approach to collect data, that is, a Web survey involving open- and closed-ended questions, as well as a structured interview. The sample was composed of 313 students who completed an electronic survey on a Web site and 16 professors teaching to these students who participated in a structured interview. The quantitative data analysis was performed using a structural equation modeling software, that is, Partial Least Squares (PLS); the qualitative data were analyzed following a thematic structure using QSR NVivo software.

IntroductIon For the past two decades, information and communication technologies (ICT) have transformed DOI: 10.4018/978-1-60566-938-0.ch006

the ways professors teach and students learn. Some professors have actively shifted the information flow of a face-to-face mode (student listening, onsite presence) to an entirely online mode (student reading, onsite non presence); that is, they have designed courses and curricula that are offered completely

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

online using the Internet and the Web. Others have developed the hybrid or blended mode (a combination of face-to-face and online activities; less student onsite presence, ongoing use of ICT both inside and outside the classroom). Hence, knowledge acquisition and dissemination have been transformed, and new methods developed in order to satisfy the rapidly evolving needs of a population of individuals in search of more knowledge, heterogeneous, and geographically distributed. In today’s global economy, organizations (including universities) who want to survive and strive to stay highly competitive must continually innovate at the human, material, and technological levels. Alavi and Leidner (2001) pointed out that, during the past decade, universities and corporate training facilities have at an increasing rate invested in ICT to improve education and training. Marshall (2002) added that actual classrooms are now more and more enriched by technology. Recent studies by the National Center for Education Statistics (Waits & Lewis, 2003), the Sloan Consortium (Allen & Seaman, 2004, 2005, 2006, 2007, 2008), Aggarwal and Legon (2006), Borstorff and Lowe (2007), Martz and Shepherd (2007), as well as Kinuthia and Dagada (2008) showed a growing appeal and acceptance of online learning. Other recent studies by Gomez et al. (2007), Eynon (2008), Young and Ku (2008), and Steele (2008) showed the growth of blended learning. Numerous Web tools have also emerged promoting the growth of both online and blended learning, for example: Web-based platforms, such as WebCT, Blackboard and Desire2Learn, designed to assist schools, colleges, universities and corporations in the delivery of online learning; Course Management Systems (CMS) software packages designed to help professors create online learning communities (Liu & Cheng, 2008); Moodle, an online course management course designed to encourage participation by students (Steele, 2008); TETIS (Teaching Transparency Information System), an information system implementing the practices

84

of everyday teaching and designed to be used together with a traditional e-learning platform (Cartelli, 2008); FirstClass, an asynchronous written conferencing package (Knight, 2007); VUSIL (Virtual University Server in Lebanon), a prototype designed to be used as a basis for the creation of a digital campus in Lebanon (van Schaik et al., 2005); Talk 2 Learn, an online community reflecting the thinking of Wenger and Vygotsky (Allen, 2005); Learning Objects from online repositories facilitating courses production (Wilhelm & Wilde, 2005); and the new Web 2.0 tools such as Blog, Cloud Computing, Mash Up, Podcast, RSS Feed, Social Networking, and Wiki. Finally, Giddens (1999) stated that one of the more important functions of the university is to allow people to play a significant role in today’s new economy. Thus, universities, faculties, and professors are currently looking for ways to improve teaching and curricula, as well as develop new modes capable of satisfying the actual and future needs of organizations and societies. Out of their recursive attempts, the four fundamental questions often revisited are the following: (1) What are we teaching? (2) What should we be teaching? (3) What is the best way to teach it (pedagogy)? and (4) What are the impacts on students? This study aims at helping universities to stay highly competitive in the current global shift in higher education. It uses an innovative approach to explore new directions regarding the last two questions above. We examine the relation between students’ learning outcomes (undergraduate and graduate students) and learning environments all integrating ICT. Specific relations between student onsite presence and student online presence are examined as to identify their effect on the basic relation between learning environments and students’ learning outcomes. In fact, this study compares onsite technology-rich hybrid or blended learning environments and online learning environments. Moreover, this study brings to the foreground several moderator variables related to students’ characteristics (psychology) and

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

professors’ pedagogy in order to better understand the relation between learning environments and students’ learning outcomes. Therefore, the objective of this study is twofold: (1) to verify whether there are effectiveness-, performance-, and satisfaction-related differences between learning outcomes of onsite students and of those taking the same courses online; and (2) to verify whether students’ characteristics and professors’ pedagogy are important factors to consider when examining the relation between learning environments and students’ learning outcomes. Building on questions 3 and 4 raised previously (professor’s pedagogy and impacts on students), this study focuses on the following research questions: Are there differences between learning outcomes of onsite students and of those taking the same courses online? If so, which ones? Do students’ characteristics have an influence on the relation between learning environments and students’ learning outcomes, and are there differences in this influence between onsite and online students? If so, which ones? Does professors’ pedagogy have an influence on the relation between learning environments and students’ learning outcomes, and are there differences in this influence between onsite and online students? If so, which ones? This chapter presenting the study builds on a framework suggested by Fillion (2004) in the conduct of hypothetico-deductive scientific research in organizational sciences, and it is structured as follows. First, the theoretical background supporting the study is examined; second, the methodology followed to conduct the study is described; third, the results of the study are reported (we present a summary of the quantitative results got from students which are supported and enriched by the qualitative results got from students and their professors); and the chapter ends with a discussion of the results and some recommendations.

tHeoretIcAl bAckground First, whether in-class presence is required or not, the new ICT-supported or technology-rich learning environments bring several changes for all of the actors involved: for universities: the development of curricula adapted to these new learning environments; for professors: new roles with students (facilitator, guide, etc.) as well as more open, flexible, and dynamic teaching methods; and for students: new ways of learning in which active participation and discovery are becoming key factors in their success. Numerous authors (e.g., Breuleux et al., 1996; Dede, 1998; Hiltz, 1994, Holmberg, 1995, Lockwood, 1995; quoted in Linn, 1996 p. 826; Hennessy et al., 2005; Keegan, 1995, 1996; Magambo, 2007; UNESCO, 2005) argue that the new ICT-supported or technology-rich learning environments have the power to dramatically improve students’ learning outcomes. Others think that advanced and emerging technologies have very important impacts on our societies (Barker & Hall, 1998; Magambo, 2007; UNESCO, 2005). According to Alavi and Leidner (2001), during the past decade, universities and corporate training facilities have at an increasing rate invested in ICT to improve education and training. And several emerging companies have the objective to provide tools and services improving the conception of ICT-based learning solutions. However, Dede (1998) stresses the fact that there is still much work to do to improve the conception of learning tools which are already available. Piccoli et al. (2001) point out that some investigation of the role of different communication technologies supporting learning is needed, while several questions remain without responses. Moreover, according to Brindle and Levesque (2000), infatuation regarding the actual technological capabilities must be combined to an equal sum of attention on the part of the academic researchers concerning its educational implications. The real question here, in fact, is that there is a

85

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

gap between our view of the technology and those of the pedagogy used into ICT-supported learning environments, add Brindle and Levesque. In other words, technological advances are frequently overtaking our willingness to estimate the actual learning outcomes. Brindle and Levesque therefore strongly agree that a realistic estimation and understanding of the impacts on students of ICT-based learning environments is needed. Alavi and Leidner (2001) support this position while they mention that, although ICT-based learning has increased in the past years (for example, we can see more conferences on the subject as well as several journals discussing special questions about ICT and management education), it still stands far behind the practical developments. It is very clear in their mind that there is a lack of rigorous and theoretically-based research. Alavi and Leidner wrote their paper with the aim to invite researchers to seriously think about research on ICT-based learning. More specifically, these authors put an emphasis on the absence of research taking into account the interactions between technology, professors’ pedagogy, and students’ psychological processes. Numerous other researchers are in accordance on the fact that theoretically-based research on pedagogy used into ICT-supported learning environments is needed. Indeed, according to Phipps and Merisotis (1999), the irony is that the bulk of research on technology comes to point an academic fundamental activity: pedagogy- the art of teaching. Palloff and Pratt (2001) indicate that it is pedagogy and not technology which is critical to the success of an ICT-based course. Mayes (2001) argues that, before to adopt a particular technology supporting students’ learning, we should clarify the pedagogical approach which we want to use. Carr (1999) supports the Mayes’ position while he indicates that, without an appropriate pedagogy, the use of high performance communication media cannot lead to significant improvements in students’ learning outcomes (quoted in Jackson & Anagnostopoulou, 2001 p. 53). In fact, according

86

to Carr, it is generally pedagogy which is on the basis of learning and not technology alone. In addition, Bonk et al. (2001) are identifying four key elements on which a professor using ICT must pay a particular attention: (1) pedagogy; (2) social interaction; (3) course management; and (4) technology. Finally, in the Schlais and Davis’ (2001) view, educational networks create new learning opportunities asking a new pedagogy to support them. Thus, we can easily see, in which is discussed above, that the researchers within the information systems (IS) and education fields (the IS field is extremely well positioned to contribute to the development of ICT-based learning for several reasons (see Alavi & Leidner (2001) for more details)) must not only put the emphasis on the research concerning the impacts of the technology, but also on professors’ pedagogy and students’ characteristics (psychology) which will allow to use the technology in an appropriate way to provide students with effective and satisfying learning experiences. So we put the emphasis on these two specific points in the present study. The study is theoretically-based on Leidner and Jarvenpaa’s, and Phipps and Merisotis’ key research works. On the basis of three case studies, Leidner and Jarvenpaa (1993) developed a theoretical research model for other researchers to test in future studies. And, in a literature review, Leidner and Jarvenpaa (1995) inventoried numerous educational variables to be examined in future studies according to different scenarios using ICT. Several of the variables suggested by these authors are used in this study. In their literature review on distance learning effectiveness in the 1990’s, Phipps and Merisotis (1999) pointed out that the studies which compare distance ICT-based learning environments with conventional learning environments (face-toface without ICT use) fall into three categories: (1) students’ results (performance); (2) students’ attitude toward learning in these two types of environments; and (3) students’ general satisfac-

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Figure 1. Theoretical research model

tion. We use these three categories as dependent variables in this study. Of the 8,110 papers published over a period of 15 years in the journals and reviews examined, Chin et al. (2003) found only 74 that contained moderator variables. Moreover, several IS dominant theories (e.g., Davis’ 1989Technology Acceptance Model (TAM) and Doll and Torkzadeh’s 1991 user participation/involvement model; quoted in Chin et al., 2003 p. 192) as well as the streams of research that have extended these models (e.g., Barki et al., 2007; Bhattacherjee & Sanford, 2006; Carswell & Venkatesh, 2002; Chin et al., 2008; Davis & Venkatesh, 2004; Devaraj et al., 2008; Hartwick & Barki, 1994; Karahanna et al., 2006; Limayem et al., 2007; Venkatesh & Davis, 2000; Venkatesh & Speier, 1999; Venkatesh & Speier, 2000; Venkatesh & Johnson, 2002; Venkatesh et al., 2003; Venkatesh et al., 2008) suggest that moderator variables are an important avenue of future development. Furthermore, numerous researchers within the IS field have suggested that models using moderator variables be tested (Anderson, 1985, Doll & Torkzadeh, 1989, Ives & Olson, 1984, McKeen et al., 1994, Sambamurthy & Zmud, 1999, Tait & Vessey, 1988; quoted in Chin et al., 2003 p. 192; Barki et al., 2007) as have researchers in other

fields (Chin et al., 2003). Finally, while there is an increasing body of literature on the effectiveness of ICT-based learning environments on students’ learning outcomes, studies on the effects of factors predicting Web-based course success over time are very limited (Arbaugh, 2005; Santhanam et al., 2008). Therefore, researchers must now closely examine whether other factors may have an indirect influence on students’ learning outcomes or, in other words, on the relation between ICTsupported learning environments and students’ learning outcomes. Hence, most of the variables identified by Leidner and Jarvenpaa (1993, 1995) are used as moderator variables in this study. The resulting theoretical research model is shown in Figure 1. Figure 1 shows that the theoretical research model that guides the present study is articulated around an independent construct, learning environments, a dependent construct, student learning outcomes, as well as two moderator constructs (a moderator variable is a variable that affects the direction and/or the strength of the relation between an independent variable and a dependent variable (Baron & Kenny, 1986)), student characteristics and professor pedagogy. On the basis of this theoretical research model, nine research hypotheses are formulated.

87

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Hypothesis 1. Students whose onsite presence is required to take courses (blended mode) find learning more effective than those whose onsite presence is not required (online mode). Hypothesis 2. Students whose onsite presence is required to take courses (blended mode) perform better than those whose onsite presence is not required (online mode). Hypothesis 3. Students whose onsite presence is required to take courses (blended mode) are more satisfied than those whose onsite presence is not required (online mode). Hypothesis 4. Students’ autonomy has an influence on the relation between learning environments (students’ onsite presence and non presence) and their learning outcomes ((a) learning effectiveness; (b) performance; and (c) satisfaction), and (d) this influence is more pronounced for students whose onsite presence is not required. Hypothesis 5. Students’ anxiety has an influence on the relation between learning environments (students’ onsite presence and non presence) and their learning outcomes ((a) learning effectiveness; (b) performance; and (c) satisfaction), and (d) this influence is more pronounced for students whose onsite presence is not required. Hypothesis 6. Students’ motivation has an influence on the relation between learning environments (students’ onsite presence and non presence) and their learning outcomes ((a) learning effectiveness; (b) performance; and (c) satisfaction), and (d) this influence is more pronounced for students whose onsite presence is not required. Hypothesis 7. Students’ participation has an influence on the relation between learning environments (students’ onsite presence and non presence) and their learning outcomes ((a) learning effectiveness; (b) performance; and (c) satisfaction), and (d) this influence is more pronounced for students whose onsite presence is not required. Hypothesis 8. Type of professor has an influence on the relation between learning environments (students’ onsite presence and non presence) and students’ learning outcomes ((a) learning

88

effectiveness; (b) performance; and (c) satisfaction), and (d) this influence is more pronounced for students whose onsite presence is required. Hypothesis 9. Teaching practice has an influence on the relation between learning environments (students’ onsite presence and non presence) and students’ learning outcomes ((a) learning effectiveness; (b) performance; and (c) satisfaction), and (d) this influence is more pronounced for students whose onsite presence is required. In the next section, the methodology followed to conduct the study is described.

metHodologY research design The theoretical research model depicted in Figure 1 was tested in a field experiment at the Faculty of Administration of a large Canadian university. Pragmatically, we used the experimental research design “Static-Group Comparison” proposed by Campbell and Stanley (1966) to conduct the study. “This is a design in which a group which has experienced X [here students’ onsite non presence] is compared with one which has not for the purpose of establishing the effect of X.” (Campbell & Stanley, 1966 p. 12) And the participants are not randomly assigned. Campbell and Stanley argued that, to be of a real value, a scientific research study must at least establish a comparison between two groups; this might be a group submitted to a treatment compared to a control group or two groups submitted to a treatment compared together.

sample and data collection The sample was composed of students of five undergraduate and three graduate courses, which were offered at the same university in the two modes taken into account in this study: blended mode and online mode. Students were not ran-

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

domly assigned, that is, for each course selected, all the students were asked to participate in the study. The study was spread over two semesters, fall and winter, and for each semester, four courses were studied. Each course had to meet the four following criteria: (1) to use a similar set of ICT in the two modes; (2) to be taught by a different professor in the two modes (in order to see whether there is a difference in the moderating effect of the variables type of professor and teaching practice when a same course is taught onsite and online); (3) to have the same course content in the two modes; and (4) to have, as much as possible, a similar group size in the two modes. In addition, each course was selected so that groups of students in the two modes were relatively homogeneous in terms of age and ICT experience. Finally, the course selection was made in order to cover a large area of disciplines offered at the Faculty of Administration of the university chosen for the study. Thus, the sample of the study consisted of 841 students, 438 (242 in fall and 196 in winter) in blended mode courses and 403 (198 in fall and 205 in winter) in online mode courses. Three weeks before the end of each semester of the data collection, students were asked to answer an electronic survey on a Web site. To that end, an e-mail, including a URL and a password allowing access to the electronic survey, was sent to students. And one week and a half after having asked students to answer the electronic survey on the Web site, an e-mail relating the importance of answering the electronic survey for the advancement of scientific knowledge on integration of ICT into higher education was sent to students. Finally, a few days after this first recall, all the professors were asked to make a second recall to students during class or in the discussion forums of the online courses. In the fall semester, 174 students (113, blended mode; 61, online mode) out of 440 completed the electronic survey for a response rate of 40%; in the winter semester, 139 students (70, blended mode; 69, online mode) out of 401 completed

the electronic survey for a response rate of 35%. Overall, 313 students (183, blended mode; 130, online mode) out of 841 completed the electronic survey on the Web site for a global response rate of 37%. And, of these 313 students, 262 (156, blended mode; 106, online mode) completed the qualitative section (open-ended questions) of the Web survey for a response rate of 84%. As for professors, for each course selected, both the onsite and online professors were asked to participate in the study. A total of 18 professors were asked to participate in a structured interview. Eighteen professors were selected for eight courses because one of the onsite courses was taught by three professors, each of them teaching a third of the semester. From these 18 professors, 16 (nine teaching onsite and seven teaching online) agreed to take part in the study for a response rate of 88.9%. Professors were relatively homogeneous in terms of age, gender, degree, ICT used, and computer experience. From time to time during the winter semester two or three professors were asked by e-mail to participate in a structured interview and to provide us with their schedule. According to their free time, an appointment was set up with each professor in a room of the faculty. Thus, 16 structured interviews were conducted with professors.All of the interviews were conducted using the French language in a relaxed atmosphere by the primary author of the study. The interview duration ranged from 1h15 to 2h35. Each structured interview as such was organized in the following way. First, some questions were asked to professors in order to complete the demographic data section of the structured interview. Then, with regard to the open-ended questions, permission was requested from professors to record their responses in order to ease the data input and analysis. The professors were then informed that they could express themselves freely given that their names would not be revealed in the results of the study in order to ensure them anonymity. All the professors that participated in the study agreed to the recording of their interview without any reservation.

89

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Table 1. Measures of variables Variable

Measure and (# of items)

Learning effectiveness

Alavi (1994); Centra (1979); Hiltz (1988) (12 items)

Student performance

Objective measure (students’ grades)

Student satisfaction

Hobbs and Osburn (1989) (15 items)

Student autonomy

Hackman and Oldham (1975) (2 items); Wilson (1990) (4 items)

Student anxiety

Spielberger et al. (1970) (20 items)

Student motivation

Hackman and Oldham (1975) (10 items)

Student participation

Green and Taber (1980) (5 items)

Type of professor

Hiltz (1990) (11 items); Thach and Murphy (1995) (2 items)

Teaching practicec

Chickering and Gamson (1987) (7 items)

a

b

b

The measure of this variable was formed using 12 items developed by Alavi (1994) and derived from Hiltz’s (1988) survey on the basis of Centra’s (1979) theoretical summary. b The measure of this variable was formed using items of two instruments. Following Keller and Dansereau (2001), which showed the importance of retesting the validity and reliability of an instrument after the addition or removal of some items, we pre-tested the modified instrument with a sample of 63 students. c To our knowledge, the seven principles of a good teaching practice proposed by Chickering and Gamson (1987) were never used as a measurement tool in any study until now. We then pre-tested the new instrument with a sample of 63 students. This one showed a high level of validity and reliability. a

constructs measurements The learning environments construct was dummy coded with students’ onsite presence (blended mode) as “1” and students’ onsite non presence (online mode) as “2”. And to measure all the variables that made up the other constructs, with the exception of teaching practice, we used various measures validated by other authors in past studies. They are presented in Table 1.

data Analysis The quantitative data analysis of the study was performed using a structural equation modeling software, that is, Partial Least Squares (PLS-Graph 3.0). PLS can easily deal with small samples and data have no need to follow a normal distribution. In addition, PLS is appropriate when the objective is a causal predictive test instead of the test of a whole theory (Barclay et al., 1995; Chin, 1998) as it is the case here. To ensure the stability of each model developed in order to test the research hypotheses, we used the PLS bootstrap resampling

90

procedure (the interested reader is referred to a more detailed exposition of bootstrapping (see Chin, 1998; Efron & Tibshirani, 1993)) with an iteration of 100 sub-samples extracted from the initial sample (313 students). Some analyses were also performed using the Statistical Package for the Social Sciences software (SPSS 11.5). As for the qualitative data analysis, it was carried out using the Qualitative Solutions & Research NVivo software (QSR NVivo 2.0). We performed thematic analyses on the qualitative data of both students and professors. In the next section, we present a synthesis of the results.

results constructs reliability To ensure the reliability of a construct using PLS, we must verify the three following properties: individual item reliability, internal consistency, and discriminant validity (see Yoo & Alavi, 2001 for more details).

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Table 2. Means, standard deviations, composite reliability indexes, correlations and average variance extracted of variables Variablea

M /5

SD

Reliability index

Correlations and average variance extractedb (1)

(2)

(3)

(4)

(5)

(6)

(7)

(1) Type of professor

3.72

1.08

0.91

0.75

(2) Teaching practice

3.49

1.03

0.86

0.73

0.76

(3) Student autonomy

4.15

0.82

0.85

0.12

0.12

0.70

(4) Student anxiety

2.22

1.10

0.92

-0.36

-0.23

-0.45

0.64

(5) Student motivation

4.05

0.91

0.81

0.30

0.34

0.35

-0.19

0.65

(6) Student participation

3.33

1.15

0.87

0.27

0.26

0.30

-0.37

0.38

0.76

(7) Learning effectiveness

3.50

1.05

0.92

0.66

0.66

0.09

-0.21

0.39

0.27

0.77

(8) Student satisfaction

3.72

1.06

0.93

0.74

0.72

0.20

-0.38

0.37

0.24

0.69

(8)

0.76

Student performance is not included in this PLS reliability analysis since it is measured by students’ grades. Boldfaced elements on the diagonal of the correlation matrix represent the square root of the average variance extracted (AVE). For an adequate discriminant validity, the elements in each row and column should be smaller than the boldfaced element in that row or column. a

b

To verify individual item reliability, a confirmatory factor analysis (CFA) was performed on eight of the nine variables of the moderator and dependent constructs (the other variable of the dependent construct, student performance, was measured by students’ grades). After the first iteration of the CFA, only some items were retired of the following variables given their loadings were inferior to 0.50: type of professor (1 item), student anxiety (4 items), student motivation (4 items), learning effectiveness (2 items), and student satisfaction (1 item). And after the second iteration of the CFA, no item was withdrawn. In the whole, items had high loadings, that is, between 0.65 and 0.87, indicating a high level of internal consistency of their corresponding variables. In addition, loadings of each variable were superior to cross-loadings with other variables of the model. The first criterion of discriminant validity was therefore satisfied. And to get composite reliability indexes and average variance extracted (AVE) in order to satisfy the second criterion of discriminant validity as well as to verify internal consistency of the variables, we used PLS bootstrap resampling procedure with an iteration of 100 sub-samples

extracted from the initial sample (313 students). The results are presented in Table 2. As shown in Table 2, PLS analysis indicates that all square roots of AVE (boldfaced elements on the diagonal of the correlation matrix) are higher than the correlations with other variables of the model. In other words, each variable shares more variance with its measures than it shares with other variables of the model. Thus, discriminant validity is verified. Finally, as it can be seen in Table 2, PLS analysis shows high composite reliability indexes for all variables of the theoretical research model. The variables have therefore a high internal consistency, with composite reliability indexes ranging from 0.81 to 0.93.

test of Hypotheses First, to perform descriptive statistics of the variables of our theoretical research model, we used SPSS. The results of the analysis are summarized in Table 3. According to the means in Table 3, students who participated in the study were very autonomous (means of 4.07 and 4.27 for onsite and online students, respectively) and motivated (means of

91

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Table 3. Descriptive statistics of variables Variable

Type of professor

Blended mode (n = 183)

Online mode (n = 130)

M /5

SD

M /5

SD

3.68

1.13

3.77

1.01

Teaching practice

3.44

1.05

3.55

0.99

Student learning effectiveness

3.48

1.08

3.54

1.02

Student satisfaction

3.64

1.12

3.84

0.97

Student anxiety

2.33

1.13

2.07

1.03

Student participation

3.37

1.16

3.27

1.13

Student autonomy

4.07

0.84

4.27

0.77

Student motivation

4.11

0.86

3.97

0.97

Student performance a Exam grades /50

36.19

2.22

35.85

2.70

Assignment grades /50

42.02

2.97

40.26

3.26

Total grades /100

78.21

4.50

76.11

5.11

Student performance was measured using students’ assignment and exam grades. Given the weights of exams of the whole courses studied were falling into the interval [40, 70] points out of 100 and those of assignments into the interval [30, 60] points out of 100, and following a normal distribution, weights of 50% for exams and 50% for assignments were used to standardize assignment and exam grades for the whole courses studied and thus get two items allowing to integrate student performance as dependent variable into all the PLS structural equation models developed to test research hypotheses. a

4.11 and 3.97 for onsite and online students, respectively), and not very anxious (means of 2.33 and 2.07 for onsite and online students, respectively). We can also see in Table 3 that onsite students achieved higher grades than their online peers both in assignments and exams (global means of 78.21 and 76.11 for onsite and online students, respectively). And standard deviations of the whole variables indicate a relative homogeneity between groups of students. To test hypotheses involving independent and dependent variables (H1-H3), we developed a PLS model similar to those of Limayem and DeSanctis (2000), Limayem et al. (2002), and Yoo and Alavi (2001). And the PLS bootstrap resampling procedure was used with an iteration of 100 sub-sample extracted from the initial sample (313 students) to ensure the stability of the model. This model is depicted in Figure 2. As we can see in Figure 2, the low t-value (0.546) and beta coefficient (0.022) got in the PLS

92

structural equation model indicate that the path from learning environments to student learning effectiveness is not significant. In short, contrary to our expectations, in this study, learning environments had not a more significant effect on learning effectiveness of onsite students than on those of their peers taking the courses online. As a result, hypothesis 1 is not supported. As for performance, Figure 2 shows that the path from learning environments to student performance is significant (t = 4.396, p < 0.001). In other words, in this study, learning environments had a more significant effect on performance of onsite students than on those of online students, as we anticipated. Thus, hypothesis 2 is supported. Finally, we can see in Figure 2 that the path from learning environments to student satisfaction is significant (t = 2.330, p < 0.01). But, contrary to our expectations, online students were more satisfied than those onsite. Consequently, hypothesis 3 is not supported.

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Figure 2. PLS structural equation model to test hypotheses 1, 2, and 3

And to test hypotheses involving moderator variables (H4-H9), we developed several PLS models according to the Chin et al.’s (2003) and Carte and Russell’s (2003) procedures. As for the test of hypotheses 1 to 3 previously, for each PLS model developed, we used the bootstrap resampling procedure with an iteration of 100 sub-sample extracted from the initial sample (313 students) to ensure the stability of the model. For a matter of space, given that the test of hypotheses 4 to 9 related to moderator variables required the development of several PLS structural equation models (six models per variable, that is, 36 models), we summarize PLS analyses to test these hypotheses in Table 4. We can see in Table 4 that the path from the latent variable environments*autonomy to the dependent variable student learning effectiveness is significant (t = 1.895, p < 0.05). And there is a change in R2 (^R2 = 0.008). Consequently, student autonomy had an influence on the relation between learning environments and student learning effectiveness, and sub-hypothesis 4 (a) is then supported. As for student performance and satisfaction, there are some changes in their respective R2, but the paths from latent to dependent variables are not significant. Thus, sub-

hypotheses 4 (b) and 4 (c) are not supported. To verify whether there was a difference in influence of autonomy between onsite and online students, we ran SPSS Box’s M test. The test showed that the influence of student autonomy on the relation between learning environments and student learning effectiveness was more pronounced for students whose onsite presence was not required, supporting sub-hypothesis 4 (d). Table 4 indicates that the path from the latent variable environments*anxiety to the dependent variable student performance is significant (t = 2.468, p < 0.01). And there is a change in R2 (^R2 = 0.014). Thus, as we expected, student anxiety had an influence on the relation between learning environments and student performance. And sub-hypothesis 5 (b) is then supported. As for the two other dependent variables, there is a change in R2 for learning effectiveness and no change in R2 for satisfaction, and the paths from latent to dependent variables are not significant. So sub-hypotheses 5 (a) and 5 (c) are not supported. To verify whether there was a difference in influence of anxiety between onsite and online students, we ran SPSS Box’s M test. Contrary to our expectations, we found that the influence of student anxiety on the relation between learning

93

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Table 4. Summary of the PLS structural equation models to test hypotheses 4 to 9 Variable

Interaction effecta

t

R2 (1)

R2 (2)

^R2b

Student autonomy Student learning effectiveness

-0.113*c

1.895

0.020

0.028

0.008

Student performance

0.056

0.998

0.075

0.078

0.003

Student satisfaction

-0.030

0.455

0.060

0.061

0.001

Student anxiety Student learning effectiveness

0.049

0.827

0.061

0.064

0.003

Student performance

0.121**

2.468

0.074

0.088

0.014

Student satisfaction

0.000

0.000

0.157

0.157

0.000

-0.038

0.634

0.159

0.160

0.001

Student motivation Student learning effectiveness Student performance

0.085*

1.708

0.078

0.085

0.007

Student satisfaction

-0.049+

1.289

0.173

0.176

0.003

Student learning effectiveness

-0.155****

2.899

0.082

0.103

0.021

Student performance

0.092**

2.339

0.065

0.073

0.008

Student satisfaction

-0.116**

2.374

0.098

0.111

0.013

-0.045

1.125

0.445

0.447

0.002

Student participation

Type of professor Student learning effectiveness Student performance

0.054

1.024

0.062

0.065

0.003

Student satisfaction

-0.037+

1.334

0.680

0.681

0.001

Teaching practice Student learning effectiveness

0.034

0.799

0.433

0.434

0.001

Student performance

0.037

0.531

0.080

0.082

0.002

Student satisfaction

-0.034+

1.298

0.535

0.537

0.002

Interaction effect is formed by the multiplication of items of the independent variable by those of the moderator variable (ex.: for student autonomy, the interaction effect on student learning effectiveness is formed by learning environments*student autonomy). b ^R2represents change in R2, that is, the difference between the R2 calculated before the addition of interaction effect and those calculated after the addition of interaction effect (R2 (2) - R2 (1)). c Path coefficients (beta). +p < 0.10; *p < 0.05; **p < 0.01; ****p < 0.001. a

environments and student performance was more pronounced for students whose onsite presence was required. As a result, sub-hypothesis 5 (d) is not supported. If we look at Table 4, we can see that the path from the latent variable environments*motivation to the dependent variable student performance is significant (t = 1.708, p < 0.05). There is also a change in R2 (^R2 = 0.007). As we expected, student motivation had an influence on the rela-

94

tion between learning environments and student performance, supporting sub-hypothesis 6 (b). Table 4 also shows that the path from the latent variable environments*motivation to the dependent variable student satisfaction is significant (t = 1.289, p < 0.10). And there is a change in R2 (^R2 = 0.003). But, given the level of significance to support hypotheses in this study is p < 0.05, subhypothesis 6 (c) is not supported. As for student learning effectiveness, there is a change in R2,

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

but the path from latent to dependent variables is not significant. As a result, sub-hypothesis 6 (a) is not supported. To verify whether there was a difference in influence of motivation between onsite and online students, we ran SPSS Box’s M test. Contrary to our expectations, we found that the influence of student motivation on the relation between learning environments and student performance was more pronounced for students whose onsite presence was required. As a result, sub-hypothesis 6 (d) is not supported. As shown in Table 4, the paths from the latent variable environments*participation to the dependent variables student learning effectiveness, student performance, and student satisfaction are significant (t = 2.899, p < 0.001; t = 2.339, p < 0.01; and t = 2.374, p < 0.01, respectively). And there are changes in R2 (^R2 = 0.021; 2 = 0.008; 2 = 0.013, respectively). Consequently, as we expected, student participation had an influence on the relations between learning environments and student learning effectiveness, student performance, and student satisfaction. Therefore, subhypotheses 7 (a), 7 (b), and 7 (c) are supported. To verify whether there were differences in influence of participation between onsite and online students for learning effectiveness, performance, and satisfaction, we performed three SPSS Box’s M tests. All three tests showed to be significant but, contrary to our expectations, we found that the influence of student participation on the relations between learning environments and student learning effectiveness, student performance, and student satisfaction was more pronounced for students whose onsite presence was required. As a result, sub-hypothesis 7 (d) is not supported. We can see in Table 4 that the path from the latent variable environments*typeprof to the dependent variable student satisfaction is significant (t = 1.334, p < 0.10). And there is a change in R2 (^R2 = 0.001). However, given the level of significance to support hypotheses in this study is p < 0.05, sub-hypothesis 8 (c) is not supported. As for learning effectiveness and performance,

there are some changes in their respective R2, but the paths from latent to dependent variables are not significant. Consequently, sub-hypotheses 8 (a) and 8 (b) are not supported. And, as we have not found a significant interaction effect, there is no need to test sub-hypothesis 8 (d) and it is, of course, not supported. In short, contrary to our expectations, type of professor had not a significant influence on the relations between learning environments and student learning effectiveness, student performance, and student satisfaction. Table 4 indicates that the path from the latent variable environments*teachpra to the dependent variable student satisfaction is significant (t = 1.298, p < 0.10). And there is a change in R2 (^R2 = 0.002). Nevertheless, given the level of significance to support hypotheses in this study is p < 0.05, sub-hypothesis 9 (c) is not supported. As for learning effectiveness and performance, there are some changes in their respective R2, but the paths from latent to dependent variables are not significant. Thus, sub-hypotheses 9 (a) and 9 (b) are not supported. And, similarly to the previous hypothesis, as we have not found a significant interaction effect, there is no need to test sub-hypothesis 9 (d) and it is, of course, not supported. In short, contrary to our expectations, teaching practice had not a significant influence on the relations between learning environments and student learning effectiveness, student performance, and student satisfaction. Finally, in order that the reader can have a global view of the quantitative results of the study, Table 5 presents a summary of the test of hypotheses. As shown in Table 5, onsite students performed better than those online (p < 0.001). On the other hand, online students were more satisfied than those onsite (p < 0.01). As for the moderator variables, autonomy had an influence on the relation between learning environments and student learning effectiveness (p < 0.05), and this influence was more pronounced for online students than for those onsite (p < 0.001). Anxiety and motivation

95

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Table 5. Summary of the test of hypotheses Hypothesis

Results

Software (Sig.)

H1

Not supported

PLS (0.022)

H2

Supported

PLS (0.228****)

H3

Not supporteda

PLS (0.059**)

H4

(a) Supported (b) Not supported (c) Not supported (d) Supported

PLS (-0.113*) PLS (0.056) PLS (-0.030) SPSS (0.000****)

H5

(a) Not supported (b) Supported (c) Not supported (d) Not supporteda

PLS (0.049) PLS (0.121**) PLS (0.000) SPSS (0.000****)

H6

(a) Not supported (b) Supported (c) Not supportedb (d) Not supporteda

PLS (-0.038) PLS (0.085*) PLS (-0.049+) SPSS (0.000****)

H7

(a) Supported (b) Supported (c) Supported (d) Not supporteda

PLS (-0.155****) PLS (0.092**) PLS (-0.116**) SPSS (0.000****)

H8

(a) Not supported (b) Not supported (c) Not supportedb (d) Not supporteda

PLS (-0.045) PLS (0.054) PLS (-0.037+) SPSS (0.000****)

H9

(a) Not supported (b) Not supported (c) Not supportedb (d) Not supporteda

PLS (0.034) PLS (0.037) PLS (-0.034+) SPSS (0.000****)

The test is significant, but the result is in opposition with which is formulated in the hypothesis. The hypothesis is not supported given the level of significance of the test is too low (p < 0.10). +p < 0.10; *p < 0.05; **p < 0.01; ****p < 0.001. a

b

had an influence on the relation between learning environments and student performance (p < 0.01 and p < 0.05, respectively). Participation had an influence on the relation between learning environments and student learning effectiveness (p < 0.001), performance (p < 0.01), and satisfaction (p < 0.01). Finally, it is very surprising that professor pedagogy (type of professor and teaching practice) had not a significant influence (at least to a level of significance of 0.05) on the relation between learning environments and student learning outcomes (learning effectiveness, performance, and satisfaction). To summarize, the quantitative data analysis of the study provided very interesting and somewhat surprising results, particularly

96

regarding students’ performance and satisfaction, as well as professors’ pedagogy. The results of the qualitative data analysis follow.

open-ended questions Involving students Students’ Preferences in the Course When Using ICT In the first open-ended question of the Web survey students were asked to indicate what they appreciated the most in the course. The elements most appreciated by both onsite and online students (in order of priority) are professor, course usefulness,

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

course material, ICT use, assignments, access to the course material on the Web site, discussion forums, prompt feedback, student/student and student/professor interaction, course structure, evaluations, nothing, participation, and collaboration. Thus, we can conclude that whether or not students come to class to take courses, when the same set of ICT is used, they appreciate the same elements related to these courses. And, among the elements they appreciate most, professor and course usefulness in everyday life and for their career are by far in the lead. Clearly, professors still take a predominant place in the formation of students at the beginning of the 21st century. For example, some onsite students mentioned that their professor was competent, careful, dynamic, supportive, and motivating. And some online students said that their professor was competent, available, flexible, and supportive. These results provide support to the fact that professors and their teaching practices had a very significant effect on students’ learning outcomes (learning effectiveness, performance, and satisfaction) while measured as independent variables (direct effect) in the test of hypotheses 8 and 9, and, inversely, they did not have a significant effect while measured as moderator variables (indirect effect) (see Table 5).

Students’ Suggestions for Improving the Course When Using ICT In the second open-ended question students were asked to suggest ways of improving the course. The results show that the elements the students want improve in the course (in order of priority) are professor, presentation of the material, course material, assignments, amount of work, course content, nothing, evaluations, student/student and student/professor interaction, discussion forums, and WebCT use. Thus, we can conclude that whether or not the students come to class to take courses, if the same set of ICT is used, generally

both sets of students suggest improving the same elements related to these courses. And, of the elements proposed, professor and presentation of the material are by far in the lead. As a result, whether the students take courses onsite or online, they put crucial importance on the professor and his/her teaching practice, as much to appreciate them when they are satisfied (as we have seen in the analysis of the first question previously) as to criticize them when they are dissatisfied (as we can see here). For example, some onsite students said that their professor should be more dynamic, supportive, and competent. And some online students indicated that their professor should be more interested in the course, competent, dynamic, supportive, and providing more real life examples. As for the first open-ended question above, these results support the fact that the professors and their teaching practices had a very significant effect on students’ learning outcomes (learning effectiveness, performance, and satisfaction) while measured as independent variables (direct effect) in the test of hypotheses 8 and 9, and, inversely, they did not have a significant effect while measured as moderator variables (indirect effect) (see Table 5).

Benefits of Students’ Onsite Presence When Using ICT The third open-ended question of the Web survey asked students whether the onsite presence provided benefits to them with the integration of ICT into higher education, and why? Students’ responses to this question are regrouped in three categories: advantageous, non advantageous, and more or less advantageous. In the first category, the two themes that are by far in the lead are that onsite presence allows a better understanding of the material and promotes student/student and student/professor interaction. For example, some onsite students indicated that they prefer having a professor in front of the class which is explain-

97

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

ing the course material and with which they can interact when needed. And some online students answered that students taking courses in the classroom have several advantages comparatively to those taking courses on the Internet because the professors’ explanations are critical to the integration of the course material. As for the second category, the two themes that are most evident are that the students can learn as well at home with a book and that ICT allow students to take courses at a distance without onsite presence. As seen earlier, even when using ICT in the courses, professors are still taking a predominant place in the formation of students, and when professors cannot bring this value-added desired by students in class, they are dissatisfied. These results provide support to the fact that hypothesis 3 has been rejected. In contrast to what we postulated in the hypothesis, online students were more satisfied than those onsite (see Table 5). The great flexibility (time and place) offered by the distance courses on the Internet is certainly another important factor leading to these results. For the third category, there is no interrelation between onsite and online students’ responses.

Impacts of Using ICT on Students’ Characteristics In the fourth open-ended question students were asked to indicate the impacts of using ICT on their characteristics (autonomy, anxiety, motivation, and participation). The three impacts that have been by far the most important for students are that ICT use at the university increases the level of autonomy and motivation, and that the students’ characteristics (autonomy, anxiety, motivation, and participation) taken into account in this study have an influence on their learning outcomes. The two next most important impacts for students of the two modes are that ICT use at the university increases their level of anxiety and participation. For example, several onsite and

98

online students said that the integration of ICT in the courses definitely allows students to be more autonomous, motivated, and participating in the courses, but some students are more anxious when using ICT. These qualitative results support the quantitative ones (see the descriptive statistics in Table 3 as well as the test of hypotheses 4, 5, 6, and 7 in Table 5).

Impacts of Using ICT on Professors’ Pedagogy Finally, in the fifth and last open-ended question of the Web survey students were asked to indicate the impacts of using ICT on professors’ pedagogy (type of professor and teaching practice). The four impacts that have been by far the most important for students are: when they are using ICT, professors must be dynamic to keep students’ interest, make good use of ICT to bring motivation to students, use active learning techniques, and be there for students. We can see here that these impacts related to professors and their teaching practices (the two variables taken into account in this study to assess the quality of professors’ pedagogy) are of crucial importance to students. And the next most important impacts for students of the two modes are: when professors are using ICT, they must have a well-organized course and be more familiar with ICT. For example, several onsite and online students answered that professors must adapt or change their teaching methods not only in order to integrate ICT in the courses, but to use them in an appropriate way. These results provide support for the first three open-ended questions asked of students and for the rejection of hypotheses 8 and 9, while we can see a direct influence of professors and their teaching practices on students’ learning outcomes and not an indirect one (see Table 5). The results of the structured interviews involving professors will now be examined.

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

structured Interviews Involving professors

Using ICT to Improve Students’ Learning

Using ICT to Improve Professors’ Teaching

In the second question of the interview professors were asked whether using ICT in the course improved students’ learning. As for the first question above, professors’responses to the second question are regrouped in three categories: ICT improved students’ learning, I am not sure that ICT improved students’ learning, and ICT did not improve students’ learning. In the first category, both onsite and online professors were in accordance that ICT improved students’ learning. And the themes that have been the most important to support this assertion are that ICT increase access to information, increase/measure information retention, promote student/student and student/professor interaction, establish links between theory and real life, and give access to numerous computer applications. For example, some onsite professors said that ICT opened a lot of ways which may allow best students to learn better (but ICT may also allow students which are not providing much efforts to cheat), ICT will allow to illustrate concepts dynamically and in real time in the future, ICT use into courses improves the operational side of students with their computer, ICT (particularly the computer and the Internet) provide students with the sensation to have an “excellent book” in their hands, and ICT should change the “game”, that is, when the students have an in-class lesson with a professor, they should have no need to see all the theory that they have already read. And some online professors answered that ICT provide students with a lot of possibilities to access information, to discuss about it and to have a doubt about it when necessary, as well as to be placed in front of numerous “real life” examples that make such they can establish links between theory and practice, ICT allow students to provide much more sustained assignments (indicating a much more pronounced search of information), ICT provide students with a level of interaction which was not there before in conventional courses, and ICT allow students

In the first question of the structured interview professors were asked whether using ICT in the course improved their teaching. Professors’ responses to the first question of the interview are regrouped in three categories: ICT improved my teaching, ICT deteriorated my teaching, and ICT did not improve my teaching. In the first category, both onsite and online professors were in accordance that ICT improved their teaching. And the three themes that have been in the lead supporting this assertion are that ICT promote student/student and student/professor interaction, improve presentation of the material and allow to extensively use the Internet. For example, some onsite professors indicated that ICT definitely improved their teaching given they are opening new horizons to them, they are a precious help to present and improve the course material, and they provide a greater flexibility of communication at a distance. And some online professors answered that ICT provide them with their own library on their office desk, allow them to suggest to students a lot of exercises and to present the course material more dynamically, allow them to answer students’ questions more rapidly, and allow students to work more at their own pace and following their own schedule. There is no interrelation between onsite and online professors’ responses regarding the second and third categories. On the other hand, it is interesting to see that two times more onsite professors than online ones mentioned that ICT improved their teaching. Thus, we think that in online courses ICT are taken as an integral part of the course compared to onsite courses where ICT are rather viewed as a complement to conventional teaching methods.

99

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

to be more open to ask questions to professors than in face-to-face. In the second category, one professor of each mode mentioned that they were not sure that ICT improved students’ learning. And no theme supporting this assertion is interrelated. As for the third category, there is no interrelation between onsite and online professors’ responses. Similar to the first question, it is interesting to see that two times more onsite professors than online ones indicated that ICT improved students’ learning. This might be explained by a rationale similar to those provided in the first question, that is, in online courses ICT are taken as an integral part of the course compared to onsite courses where ICT are rather viewed as a complement to conventional teaching methods.

Professors’ Satisfaction with Their Use of ICT in Their Teaching In the third question professors were asked whether they were satisfied with their use of ICT in their teaching. Professors’ responses to the third question are regrouped in two categories: satisfied enough and never satisfied. In the first category, both onsite and online professors were satisfied enough with their use of ICT in their teaching. And the two interrelated themes supporting this assertion are that ICT bring very interesting results (in students’ learning and professors’ teaching) and they could do more with ICT. For example, some onsite professors indicated that they are satisfied with their use of ICT, but they are sure that they are using them only at the minimum of their potential and they can do much more than this with ICT. On the other hand, these onsite professors also mentioned that it is a matter of time to learn using a lot of ICT in their teaching; if they want to integrate ICT at their full potential in their teaching, they must take this time on those personally reserved to them and their families, it is a big problem! And some online professors answered that they are satisfied with their use of ICT and the results got until now, and that they

100

could not use them more. With regard to the second category, one professor of each mode indicated that they were never satisfied with their use of ICT in their teaching. And no theme supporting this assertion is interrelated. On the other hand, it is very surprising to see that about half of the professors of the two modes mentioned that they were not exploiting ICT to their full potential and that they could do more with ICT. We can conclude that professors that are not completely involved with ICT are then fully aware. But it seems that they often have constraints hindering them, for example the lack of time to be more familiar with ICT or to explore other ICT as well as the lack of adaptation of the classrooms so that students can work with ICT. We can also see that unlike online professors who said that they lack time to explore other ICT, onsite professors indicated a lack of time to become more familiarized with ICT. Therefore, it seems that online professors were more comfortable with ICT than onsite professors in this study and, consequently, they have probably made a better use of ICT in the courses. These results help to explain the rejection of hypothesis 3 in which we postulated that onsite students would be more satisfied than their peers taking the courses online; however, the test of hypothesis showed the opposite (see Table 5).

Benefits of Students’ Onsite Presence When Using ICT In the fourth question of the interview professors were asked whether students’ onsite presence is advantageous when using ICT. We can see that professors’ responses to the fourth question are regrouped in three categories: advantageous, non advantageous, and less and less advantageous. In the first category, both onsite and online professors were in accordance that students’ onsite presence is advantageous when using ICT. Nevertheless, the majority of professors of the two modes were also in accordance that students’ onsite presence is advantageous, but only if professors are performing

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

and bring value-added to students in the classroom. The two other interrelated themes that have been by far in the lead to support this assertion are that some students need onsite presence to succeed and onsite presence allows social contact. For example, some onsite professors indicated that when the professor is of high quality, a “high-performance” professor, in-class presence makes a great difference but, inversely, if the professor is just reading the textbook and the PowerPoint slides in front of the class, then in-class presence brings no advantage. When students come to class, professors must bring to them a value-added compared with those which are not coming to class. ICT make such that professors should rethink in-class activities. And some online professors shared the same points of view than their peers teaching onsite. In the second category, two professors of the online mode were ambivalent concerning their responses, that is, they said that onsite presence is advantageous for some reasons, but they also indicated that onsite presence is not advantageous because some students are autonomous and succeed very well online, and all the course material is on the Web site. But these themes are not interrelated with those extracted from onsite professors’ responses. As for the third category, there is no interrelation between onsite and online professors’responses. On the other hand, it is interesting to see that there was a consensus between onsite and online professors regarding the advantage of students’ onsite presence when using ICT. What is still more interesting is that professors of the two modes were in accordance concerning the condition that onsite professors must be performing and capable of bringing value-added to students whether they want that students come to class to take their courses now we have ICT to make “the job” at a distance. Thus, we can see that professors have a similar point of view as students on this condition. Finally, it is interesting to see that several themes related to the benefits of students’ onsite presence when using ICT are interrelated with those derived from students’ responses to the same question.

Impacts of Using ICT on Students’ Characteristics In the fifth question of the structured interview professors were asked to indicate the impacts of using ICT on students’ characteristics (autonomy, anxiety, motivation, and participation). Professors’ responses to the fifth question are regrouped in five categories: the first four are related to students’ characteristics taken into account in the study, that is, autonomy, anxiety, motivation, and participation, and a fifth category includes other responses. The four impacts that have been by far the most important for professors are that ICT use increases the level of autonomy, motivation, participation, and anxiety in students. For example, regarding autonomy, some onsite professors commented that using ICT students become less dependent on the professor given there are much information on the course Web site and that it is very easy for them to communicate with their peers either to ask more explanations or to discuss on some points of the course material misunderstood. And some online professors answered that ICT allow increase students’ autonomy because they provide a lot of possibilities that were not accessible before, for example students can learn rapidly how to search information on the Web and to analyse it in order to see whether this information is useful or not in their courses and/or in their everyday life. It is interesting to see that students’ responses to the same question are very similar to those of professors. On the other hand, practically as many professors who said that ICT use increases the level of anxiety in students also reported a decrease in the level of anxiety. Some professors were therefore quite ambivalent on this point. With regard to the last category, there is no interrelation between onsite and online professors’ responses.

101

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Impacts of Using ICT on Professors’ Pedagogy In the sixth question professors were asked to indicate the impacts of using ICT on professors’ pedagogy (type of professor and teaching practice). Professors’ responses to the sixth question are regrouped in two categories related to professors’ pedagogy taken into account in the study, that is, type of professor and teaching practice. The three impacts that have been by far in the lead are that professors can use ICT to organize themselves and organize their courses in a more effective way, to promote student/student and student/professor interaction, and to provide prompt feedback to students. For example, with regard to provide prompt feedback to students, some onsite professors said that ICT are powerful tools to help professors in this task, for example when they are at work, on the lunch time, if they have a few minutes, they can answer some e-mails of students. And some online professors mentioned that feedback is faster online than onsite because it is more a part of the course objectives to interact effectively using electronic media, while in the classroom the major part of the communication is carried out during the three hours in-class time and sometimes within personal meetings with professor. If we take a look at students’ responses to the same question earlier, we can see that students’ priorities regarding professors’ pedagogy when using ICT are quite different of professors’ priorities. Indeed, for students, the three themes by far in the lead are that, when using ICT, professors must be dynamic to keep their interest in the course, make a good use of ICT to motivate them, and use active learning techniques. Thus, on the basis of these observations, we can conclude that students put greater importance on the dynamics of presentation of the material by professor when using ICT, while professors show greater importance for the course dynamics as such and interaction between participants. And the next three impacts that have been the most important for professors

102

of the two modes are that ICT are not changing anything with regard to respect students, and that professors can use ICT to develop active learning techniques and increase their open-mindedness. If we establish a comparison with students’ responses to the same question earlier, we can see that students and professors have at least three similar priorities in a course using ICT, but they are not in the same order. On the other hand, it is interesting and somewhat surprising to see that all the themes extracted from onsite and online professors’ responses are interrelated. Clearly, both onsite and online professors saw the same impacts on pedagogy when using ICT. In fact, this question was more structured than the previous one involving students’ characteristics and much more structured than all the others. So professors of the two modes answered this question in a more systematic way, hence the interrelation between all the themes derived from their responses.

Technical and/or Pedagogical Formation Offered When Using ICT In the seventh question of the structured interview professors were asked whether some technical and/or pedagogical formations were offered to them regarding their use of ICT in their teaching. Professors’ responses to the seventh question of the interview are regrouped in three categories: formations offered, no formation offered, and other responses. In the first category, 14 out of 16 professors participating in the interview agreed that some technical and/or pedagogical formations concerning the use of ICT in their teaching were offered to them. And the theme that has been by far in the lead is that these formations should be offered more often and particularly at the level of teaching pedagogy using ICT. For example, some onsite and online professors indicated that a great part of these formations should be oriented on the ways to improve teaching pedagogy using ICT, how to use all these tools more effectively, how to manage large groups of students when using

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

ICT in the courses, and so on. In the other interrelated theme, some professors indicated that they have some difficulties to find time to attend these formations. As for the second and third categories, there is no interrelation between onsite and online professors’ responses. On the other hand, it is very interesting to see that five onsite professors suggested that such formations should be offered in more flexible hours, while no online professor mentioned this. We think that the determining factor is probably the great flexibility (time and place) of the courses offered on the Internet. It is also very interesting to see that five out of seven professors of the online mode said that they were relatively autonomous with ICT, while no onsite professor indicated this. According to us, the determining factor is that professors teaching online courses are “constrained” to use ICT; hence they become much more comfortable using ICT. So this factor helps to explain the rejection of hypothesis 3, while online students were more satisfied than those onsite (see Table 5).

students are asking to improve in the course and what professors are asking as material resources to teach courses using ICT. Indeed, the two aspects that students are asking to improve the more are course material and professor, and those at which professors put more emphasis is that more computer software and material is needed to teach better courses. In fact, what students and professors are requesting is complementary. On the other hand, it is quite surprising that, with the exception of the three interrelated themes, all the other themes come from unique request on the part of onsite and online professors. This data analysis and results interpretation will now be followed by a discussion of the research findings and their implications.

Material Resources Lacking When Using ICT

First, regarding student learning effectiveness, our findings provide support for the conclusions drawn by Allen and Seaman (2004, 2006), Knight (2007), Lionarakis and Papademetriou (2003), Newlin et al. (2005), Phipps and Merisotis (1999), Russell (1999, 2001) and Tucker (2001). In fact, the results of our study suggest that, even with the addition of the permanent use of ICT into conventional environments, students’ learning is as effective online as in the classroom (“the no significant difference phenomenon”). On the other hand, our findings are in opposition to those reported by Carnevale (2002), that is, students who took an economics course online did not do as well as students who took the same course in a conventional classroom. Indeed, Carnevale found a “significant difference”. Concerning student performance, our findings are in opposition to those of Ahmed (2000; quoted in Alavi & Leidner, 2001 p. 5), Allen and Seaman (2004, 2006), Courte (2007), Fallah and

Finally, in the eighth and last question of the structured interview professors were asked what material resources were lacking when they are using ICT in their teaching. Professors’ responses to this last question are quite diversified. The theme that has been by far in the lead is that more software is needed to teach courses using ICT. For example, some onsite professors said that they need software into computers of their teaching desk to present their material to students in an appropriate way. And some online professors answered that they need more computer software and material to improve their teaching using ICT. The two other interrelated themes that were the most important for professors of the two modes are to improve the WebCT platform and all visual and visual interactive material. It is interesting here to observe that there is a link between what

dIscussIon comparison of the research Findings with previous studies

103

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Ubell (2000), Knight (2007), Phipps and Merisotis (1999), Russell (1999, 2001), Shen et al. (2007), Ury (2004), van Schaik et al. (2003), and Waschull (2001) who concluded that students’ performance is as good at a distance as in conventional education (“the no significant difference phenomenon”). Moreover, our results are in opposition to those of Matthews (2000), Ricketts et al. (2000), and Vigilante (2000) indicating an improvement in students’ performance in the Internet and the Web environment compared to the conventional environment. In short, according to the results of our study, with the addition of the permanent use of ICT into conventional learning environments, onsite students performed better than their peers taking the courses online. And assignment grades made all the difference. So our findings provide support to the results found by Carnevale (2002) indicating that students who took an economics course online did not do as well as students who took the same course in a conventional classroom. Indeed, Carnevale noted a “significant difference”. Clearly, this is a very surprising result, one which will require further investigation in future studies. With regard to student satisfaction, similar to student performance above, our findings are in opposition to those of Allen and Seaman (2004, 2006), Borstorff and Lowe (2007), Phipps and Merisotis (1999), Russell (1999, 2001), Washull (2001), and Young and Ku (2008) who concluded that students taking the courses at a distance are as satisfied as those in conventional education (“the no significant difference phenomenon”). In fact, in our study, even with the addition of the permanent use of ICT into conventional learning environments, online students were more satisfied than those onsite. This result has been also noted in Gomez et al.’s (2007) and Newlin et al.’s (2005) studies. So here is another very surprising result which will require further investigation. Let us now examine the results of the verification of hypotheses involving moderator variables. Our findings show a significant influence of stu-

104

dent autonomy on the relation between learning environments and student learning effectiveness, and this influence is more pronounced for online students than for those onsite. Thus, if we add the permanent use of ICT into conventional learning environments, as in our study, given that online students were more autonomous than those onsite, our results provide support for what Bilodeau (1995) stressed, that is, students at a distance are less dependent on their professor and then become more autonomous. Our findings also provide support to the results reported by Blackmore et al. (2008), that is, online students had higher levels of computer ownership, frequency of Internet use, and IT skills than onsite students. Finally, our results provide support to those found by Wang et al. (2008) indicating that a relationship exists between students’ psychological characteristics and learning outcomes of distance learners. On the other hand, our results seem not to be in accordance with the conclusion drawn by Hiltz and Turoff (1997), as well as Urban-Lurain and Weinshank (2000) who indicated an increase in students’ autonomy in the networked classroom and the wired classroom environments, respectively, compared to the conventional environment. Past research has shown that students experience moderate to high levels of anxiety in courses, as much in conventional environments as in online ones. In this study, we found that the level of anxiety was very low both for onsite and online students. Here is another very surprising result which will require further investigation in future studies. In addition, our findings suggest that anxiety has a significant influence on the relation between learning environments and student performance, and that this influence is more pronounced for students taking the courses in the classrooms than for those online. So if we add the permanent use of ICT into conventional learning environments, our research results provide support for what Harasim (1987a, 1987b; quoted in Harasim et al., 1995 p. 15) and Hiltz and Turoff (1997) said, that is, the networked classroom

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

might bring anxiety into communication. On the other hand, they are in opposition to those observed by Cowan and Piepgrass (1997), and Hembree (1988; quoted in Cowan & Piepgrass, 1997 p. 105) into conventional environments, while the researchers suggested that anxiety has harmful effects on students’ performance (the more anxious students performed better than the others here), and also in opposition to those of the study carried out by Jegede and Kirkwood (1992) in a distance learning environment who indicated that students experienced a high level of anxiety and were more anxious at the end of the semester than at the beginning. In our study, we found that student motivation has a significant influence on the relation between learning environments and student performance, and that this influence is more pronounced for onsite students than for those online. Thus, when adding the permanent use of ICT into conventional learning environments, and given that onsite students were more motivated than their peers taking the courses online, our results provide support for the conclusion drawn by Riel (1993), Harasim et al. (1995), as well as Hiltz and Wellman (1997) indicating an increase in students’ motivation in the networked classroom environment compared to the conventional environment. Our results also provide support to those noted by Wang et al. (2008), that is, a relationship exists between students’ psychological characteristics and learning outcomes of distance learners. And our findings provide support to those observed by Kinuthia and Dagada (2008) showing that learners are enthusiastic in engaging in online activities. On the other hand, our findings seem to be in opposition with the conclusion of the studies conducted by Barron and Orwig (1997; quoted in Jankowski, 1997 p. 1), Blyth (2000), and Gomez et al. (2007) which pointed to an increase in students’ motivation in the Internet and the Web environment compared to the conventional environment. Previous studies have argued that participation is crucial in distance learning environments

(e.g., Alavi et al., 1995; Barkley, 2000; Kruh & Murphy, 1990; Leidner & Jarvenpaa, 1993; Webster & Hackley, 1997). Our findings somewhat challenge these results as we found that student participation is crucial into both onsite and online environments. Indeed, although we noted relatively weak levels of student participation, participation had a strong influence on the relations between learning environments and student learning effectiveness, student performance, and student satisfaction. And, surprisingly, this influence is more pronounced for students taking courses onsite rather than online. Thus, if we add the permanent use of ICT into conventional learning environments, and given that onsite students participated more than those online, our results provide support for the conclusion of the studies conducted by Hiltz (1990) and Hiltz and Wellman (1997) indicating an increase in students’ participation in the networked classroom environment. A part of our findings is also directed in the same direction as those observed by Wang (2007), while he noted that, among others, the structure of the online discussion, group size and group cohesion, strictly enforced deadlines, and direct link of interactive learning activities to the assessment are some of the important factors that influence participation and contribute to sustained online interaction and collaboration. And our results provide support to those observed by Steele (2008) indicating that students are somewhat reluctant to embrace online technology without adequate support and preparation. On the other hand, our findings are somewhat in opposition to those of Karp and Yoels (1976) who, while following the observation of 10 undergraduate courses, noted that even in small classrooms, only few students participated in the discussions. Clearly, in our study, student participation is the moderator variable that showed having the greater influence on the relations between learning environments and the three dependent variables of our theoretical research model. Consequently, it is an extremely important variable to take into account in future

105

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

development of courses and curricula, and in future studies. Finally, the results of our study suggest that type of professor and teaching practice did not have a significant influence on the relations between learning environments and student learning effectiveness, student performance, and student satisfaction. Here again, these are very surprising results. In our view, a fact that may explain these surprising results is that type of professor and teaching practice had such a strong direct (independent) influence on the dependent variables that they did not have a significant indirect one (moderator), at least to a level of significance p = 0.05. These variables require further investigation in future studies.

limitations First, the experimental research design (a field experiment) of this study inherits the limits of this research approach: a weak level of control on independent variables and a weak level of internal validity compared to the laboratory experiment. But, inversely, it provides a higher level of external validity as it was conducted in a real life environment instead of a laboratory. In addition, this study was carried out at only one faculty of a higher education institution instead of several faculties. If it would have been conducted in several faculties of several universities, the external validity would have been even higher. Second, as this study tested a new moderatortype theoretical research model which, to our knowledge, had never been used before, it was necessary to interpret the findings using different perspectives that make sense of one or several independent variables influencing one or several dependent variables. In this study, we used moderating variables that cannot have a direct influence on dependent variables, but rather an indirect one. As a result, we needed to use a different approach to compare the findings with existing theories.

106

Finally, to compare the results of this study with existing theories, we stress that both onsite and online students were using a similar set of ICT. In other words, both learning environments were ICT-supported or technology-rich.

theoretical and practical contributions From a theoretical point of view, this study provides academic and organizational communities with theoretical foundations which are innovative, interesting, useful to strategic decisionmakers to anticipate the future with a greater certainty, and generalized to other faculties and universities regarding the impacts of students’ onsite presence and non presence on their learning outcomes, as well as the influence of numerous moderating variables on the relation between highly technological learning environments and students’ learning outcomes. This study is also opening the door to the comparison of different ICT-supported or technology-rich learning environments, whereas until now researchers have always compared an ICT-supported learning environment with the conventional learning environment (face-to-face without ICT use). In addition, to our knowledge, this study is the first to explore the impact of several important moderating variables related to students’ characteristics (psychology) and professors’ pedagogy in order to better understand the relation between learning environments and students’ learning outcomes. Hence, it sheds some light on the role of students’ characteristics and professors’ pedagogy in the students’ learning process, while they are into ICT-based learning environments. Finally, our new and creative moderator-type theoretical research model might be tested by other researchers in other universities and/or other situations since many researchers of the IS and education fields, among others, have requested the development and test of such models since a long time.

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

From a practical point of view, this study will help educational institutions to develop curricula better adapted to ICT-supported or technology-rich learning environments so that students take full advantage of their learning activities into these new environments. It will also allow decision-makers of educational institutions to target professors likely to be “the best” in these highly technological learning environments or at least to make such that those already teaching in these environments become more aware of the importance of adapting their pedagogy to these new environments and to continually be innovative in the ways of presenting their material to students. Moreover, this study will allow ICT providers to be more proactive in the design of these new technologyrich learning environments in choosing “the best” technologies to support them. Finally, the findings of this study allow us to suggest a series of recommendations hoping that they will be valuable for students, professors, educational institutions, and ICT providers. So we propose: (1) (in the case of blended mode only) to ensure that the classrooms are designed and organized so that students can easily use their laptop computer in class both for individual work and teamwork; (2) to develop courses more adapted to ICT use in learning and teaching; (3) to provide more opportunities of formations to professors which “really want” using ICT in their teaching; (4) to involve each actor concerned in the implementation process of either a new technology or working environment (this recommendation is oriented in the same direction as those formulated by Berge & Lenora (2008) which indicates that strategic planning in terms of the organizational elements and the e-learning program requirements are necessary to build a framework in order to institutionalize and sustain e-learning as a core business process); (5) to target only professors which are “eating” technologies to teach into highly technological environments; (6) (in the case of blended mode only) to provide students with a 10 minutes break per each course hour (and more particularly when professor is

teaching theory) so that they can change their mind and thus remain motivated and interested in the course; (7) to organize courses so that students might participate the more possible (even if they do not talk much about it, in general, students like participating in the courses); (8) to answer students’ questions the more rapidly possible, and more particularly for online students who are not meeting professor in face-to-face; (9) (in the case of blended mode only) to avoid reading textbook and/or PowerPoint slides in front of the class (if professor is reading textbook and/or PowerPoint slides in front of the class, students have the feeling to loose their time to come to class; professor must try to bring value-added to students who are coming to class to take courses); (10) (more particularly in the case of online mode) to know that professor must be part of the group; and (11) do not forget to HAVE FUN!

Future research directions Although we have provided some future research directions throughout the discussion until now, here are some additional research avenues. First, the testing of the new moderator-type theoretical research model developed for this study in other faculties of other universities throughout the world in order to see whether the results are persistent. Second, the conduct of the same study at the same university or other universities, but in the form of a longitudinal study so that the same students could be observed during two, three, or four semesters. Would the results remain the same? Third, the study of the possible influence of other moderator variables on the relation between ICT-supported learning environments and students’ learning outcomes, for example student self-efficacy, student attention, student learning style, student work habits, student leadership as well as student/student and student/professor interaction.

107

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

conclusIon The objective of this study was twofold: (1) to verify whether there were effectiveness-, performance-, and satisfaction-related differences between learning outcomes of onsite students and of those taking the same courses online; and (2) to observe whether students’ characteristics (psychology) and professors’ pedagogy were important factors to consider when examining the relation between learning environments and students’ learning outcomes. To reach this objective, a moderator-type theoretical research model was developed, out of which a series of hypotheses was formulated. The model was tested in a field experiment at the Faculty of Administration of a large Canadian university. The “Static-Group Comparison” research design proposed by Campbell and Stanley (1966) was used to conduct the study. The final sample was formed of 313 students who completed an electronic survey on a Web site. Also, 16 professors teaching these students participated in a structured interview. The quantitative data analysis was performed using a structural equation modeling software, that is, PLS. And the qualitative data analysis was carried out following a thematic structure using QSR NVivo. The quantitative results indicate that onsite students have not found learning to be more effective than their peers taking the same courses online. Onsite students performed better than those online. Online students were more satisfied than onsite students. Concerning students’ characteristics, students’ autonomy had an influence on the relation between learning environments (blended mode and online mode) and the effectiveness of their learning, and this influence was more pronounced for online students. Students’ anxiety and motivation had an influence on the relation between learning environments and their performance, and this influence was more pronounced for onsite students. And students’ participation had an influence on the relation between learn-

108

ing environments and the effectiveness of their learning, their performance, and their satisfaction, and this influence was more pronounced for onsite students. The qualitative results related to students are, grosso modo, the following: the elements the most appreciated by students are professor and course usefulness; the elements that the students suggest improving are professor and presentation of the material; students’ onsite presence is still advantageous when using ICT as it allows a better understanding of the material and promotes student/student and student/professor interaction; ICT use increases the level of autonomy and motivation in students, and students’ characteristics (autonomy, anxiety, motivation, and participation) have an influence on their learning outcomes; and when using ICT professors must be dynamic to keep students’ interest, make a good use of ICT to bring motivation to students, use active learning techniques, and be there for students. As for the qualitative results related to professors, grosso modo, they are the following: ICT improve professors’ teaching and students’ learning; professors are satisfied enough with their use of ICT in their teaching; students’ onsite presence is still advantageous when using ICT, but according to some conditions, among others, onsite professors must be performing and capable of bringing value-added to students in the classroom; ICT use increases the level of autonomy, motivation, participation, and anxiety in students (the level of anxiety can also decrease according to the circumstances in which the student is placed to take the course); professors can use ICT to organize themselves and organize their courses, to promote student/student and student/professor interaction, and to provide prompt feedback to students; and some technical and/or pedagogical formations were offered to professors regarding the use of ICT in their teaching. Finally, much more research will be needed as technology-rich environments unfold. Better understanding of their impacts on students, profes-

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

sors, and educational institutions will be required in order to improve them or design new ones still better adapted to higher education students. We will continue to inquire into this exciting innovative field!

Allen, I. E., & Seaman, J. (2005). Growing by degrees: Online education in the United States, 2005. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http://www. sloan-c.org/publications/survey/pdf/ growing_by_ degrees.pdf

Aggarwal, A. K., & Legon, R. (2006). Case studyWeb-based education diffusion. International Journal of Web-Based Learning and Teaching Technologies, 1(1), 49–72.

Allen, I. E., & Seaman, J. (2006). Making the grade: Online education in the United States, 2006. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http:// www.sloan-c.org/publications/survey/pdf/ Making_the_ Grade.pdf

Ahmed, R. (2000). Effectiveness of Web-based virtual learning environments in business education: Focusing on basic skills training for information technology. Unpublished Doctoral Dissertation, Louisiana State University.

Allen, I. E., & Seaman, J. (2007). Online nation: Five years of growth in online learning. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http://www.sloan-c. org/publications/survey/index.asp

Alavi, M. (1994). Computer-mediated collaborative learning: An empirical evaluation. MIS Quarterly, 18(2), 159–174. doi:10.2307/249763

Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States, 2008. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http:// www.sloan-c.org/publications/survey/pdf/ staying_the_course.pdf

reFerences

Alavi, M., & Leidner, D. E. (2001). Research commentary: Technology-mediated learning- A call for greater depth and breadth of research. Information Systems Research, 12(1), 1–10. doi:10.1287/isre.12.1.1.9720 Alavi, M., Wheeler, B. C., & Valacich, J. S. (1995). Using IT to reengineer business education: An exploratory investigation of collaborative telelearning. MIS Quarterly, 19(3), 293–312. doi:10.2307/249597 Allen, I. E., & Seaman, J. (2004). Entering the mainstream: The quality and extent of online education in the United States, 2003 and 2004. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http://www. sloan-c.org

Allen, K. (2005). Online learning: Constructivism and conversation as an approach to learning. Innovations in Education and Teaching International, 42(3), 247–256. doi:10.1080/01587910500167985 Anderson, E. E. (1985). Managerial considerations in participative design of MIS/DSS. Information & Management, 9(4), 201–208. doi:10.1016/03787206(85)90057-6 Arbaugh, J. B. (2005). Is there an optimal design for on-line MBA courses? Academy of Management Learning & Education, 4(2), 135–149. Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling, personal computer adoption and use as an illustration. Technology Studies, 2(2), 285–309.

109

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Barker, B. O., & Hall, R. F. (1998). Planning for technology implementation in rural schools. Rural Educator, 19(3), 1–6. Barki, H., Titah, R., & Boffo, C. (2007). Information system use-related activity: An expanded behavioral conceptualization of individuallevel information system use. Information Systems Research, 18(2), 173–192. doi:10.1287/ isre.1070.0122 Barkley, A. P. (2000). Unanticipated benefits from the use of computer technology in a large introductory course. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 147-149). Bolton, MA: Anker Publishing Company Inc. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. doi:10.1037/0022-3514.51.6.1173 Barron, A. E., & Orwig, G. W. (1997). New technologies for education: A beginner’s guide. Englewood Cliffs, CO: Libraries Unlimited. Berge, Z. L., & Lenora, G. (2008). Implementing and sustaining e-learning in the workplace. International Journal of Web-Based Learning and Teaching Technologies, 3(3), 44–53. Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–826. Bilodeau, H. (1995). Enseignement à distance à l’aide de tableaux électroniques. Report prepared for the Université du Québec en AbitibiTémiscamingue.

110

Blackmore, C., Tantam, D., & van Deurzen, E. (2008). Evaluation of e-learning outcomes: experience from an online psychotherapy education programme. Open Learning, 23(3), 185–201. doi:10.1080/02680510802420027 Blyth, C. S. (2000). Enhancing the study of foreign language with technology: A CD-ROM and companion Web site for first-year French. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 258-260). MA: Anker Publishing Company Inc. Bonk, C. J., Kirkley, J., Hara, N., & Dennen, V. P. (2001). Finding the instructor in post-secondary online learning: Pedagogical, social, managerial and technological locations. In J. Stephenson (Ed.), Teaching and Learning Online: Pedagogies for New Technologies (pp. 76-97), London: Kogan Page. Borstorff, P. C., & Lowe, S. K. (2007). Student perceptions and opinions toward e-learning in the college environment. Academy of Educational Leadership Journal, 11(2), 13–30. Breuleux, A., Henchey, N., Laferrière, T., Moll, M., Saint-Onge, H., Van Der Vlist, A., & Wall, E. T. (1996). Apprendre au 21ème siècle. Retrieved December 30, 2008, from http://www.fse.ulaval. ca/fac/tact/fr/html/ vision2.html Brindle, M., & Levesque, L. (2000). Bridging the gap: Challenges and prescriptions for interactive distance education. Journal of Management Education, 24(4), 445–457. doi:10.1177/105256290002400404 Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally. Carnevale, D. (2002, February 25). Online students don’t fare as well as classroom counterparts. The Chronicle of Higher Education, Distance Education Daily News. Retrieved December 30, 2008, from http://chronicle.com/ free/2002/02/2002022501u.htm

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Carr, J. (1999). The future is already here. A National Strategy for Australian Education and Training to Maximize Opportunities Offered by High Capacity Communication Services. Retrieved December 30, 2008, from http://www. educationau. edu.au/archives/broadbnd/REPORT. HTM Carswell, A. D., & Venkatesh, V. (2002). Learner outcomes in an asynchronous distance education environment. International Journal of HumanComputer Studies, 56, 475–494. doi:10.1006/ ijhc.2002.1004 Carte, T. A., & Russell, C. J. (2003). In pursuit of moderation: Nine common errors and their solutions. MIS Quarterly, 27(3), 479–501. Cartelli, A. (2008). Web technologies and the integration of different e-learning strategies in education. International Journal of Web-Based Learning and Teaching Technologies, 3(2), 23–34. Centra, J. A. (1979). Determining faculty effectiveness. San Francisco, CA: Jossey-Bass. Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in higher education. AAHE Bulletin, 39(7), 3–7. Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G.A. Marcoulides (Ed.), Modern methods for business research (pp. 295-336). Mahwah, NJ: Laurence Erlbaum Associates. Chin, W. W., Johnson, N., & Schwarz, A. (2008). A fast form approach to measuring technology acceptance and other constructs. MIS Quarterly, 32(4), 687–704. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217. doi:10.1287/isre.14.2.189.16018

Courte, J. E. (2007). Comparing student acceptance and performance of online activities to classroom activities. In Proceedings of the 8th ACM SIGITE Conference on Information Technology Education (pp. 185-190). Destin, Florida. Cowan, M. M., & Piepgrass, K. W. (1997). The first day of class on a two-year campus: What students really want us to know about their science classroom experiences. Journal of College Science Teaching, 27(2), 104–106. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi:10.2307/249008 Davis, F. D., & Venkatesh, V. (2004). Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Transactions on Engineering Management, 51, 31–46. doi:10.1109/ TEM.2003.822468 Dede, C. (1998). The scaling-up process for technology-based educational innovations. In C. Dede (Ed.), Learning with Technology: 1998 ASCD Yearbook (pp. 199-215). Alexandria, VA: Association for Supervision and Curriculum Development. Devaraj, S., Easley, R. F., & Crant, J. M. (2008). How does personality matter? Relating the fivefactor model to technology acceptance and use. Information Systems Research, 19(1), 93–107. doi:10.1287/isre.1070.0153 Doll, W. J., & Torkzadeh, G. (1989). A discrepancy model of end-user computing involvement. Management Science, 35(10), 1151–1171. doi:10.1287/mnsc.35.10.1151 Doll, W. J., & Torkzadeh, G. (1991). The measurement of end-user computing satisfaction: Theoretical and methodological issues. MIS Quarterly, 15(1), 5–10. doi:10.2307/249429

111

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman and Hall. Eynon, R. (2008). The use of the World Wide Web in learning and teaching in higher education: Reality and rhetoric. Innovations in Education and Teaching International, 45(1), 15–23. doi:10.1080/14703290701757401 Fallah, M. H., & Ubell, R. (2000). Blind scores in a graduate test: Conventional compared with Webbased outcomes. ALN Magazine, 4(2), 1–4. Fillion, G. (2004). Publishing in the organizational sciences: An extended literature review on the dimensions and elements of an hypotheticodeductive scientific research, and some guidelines on “how” and “when” they should be integrated. Academy of Information and Management Sciences Journal, 7(1), 81–114. Giddens, A. (1999). The third way: The renewal of social democracy. Melden, MA: Polity Press. Gomez, E. A., Wu, D., Passerini, K., & Bieber, M. (2007). Utilizing Web tools for computer-mediated communication to enhance team-based learning. International Journal of Web-Based Learning and Teaching Technologies, 2(2), 21–37. Green, S. G., & Taber, T. D. (1980). The effects of three social decision schemes on decision group process. Organizational Behavior and Human Performance, 25, 97–106. doi:10.1016/00305073(80)90027-6 Hackman, J., & Oldham, G. (1975). Development of the job diagnostic survey. The Journal of Applied Psychology, 60(2), 159–170. doi:10.1037/ h0076546 Harasim, L. M. (1987a). Computer-mediated cooperation in education: Group learning networks. Proceedings of the Second Guelph Symposium on Computer Conferencing, June 1-4.

112

Harasim, L. M. (1987b). Teaching and learning online: Issues in designing computer-mediated graduate courses. Canadian Journal of Educational Communication, 16(2), 117–135. Harasim, L. M., Hiltz, S. R., Teles, L., & Turoff, M. (1995). Learning networks: A field guide to teaching and learning online. Cambridge, MA: MIT Press. Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440–465. doi:10.1287/mnsc.40.4.440 Hembree, R. (1988). Correlates, causes, effects, and treatment of test anxiety. Review of Educational Research, 58, 47–77. Hennessy, S., Ruthven, K., & Brindley, S. (2005). Teacher perspectives on integrating ICT into subject teaching: commitment, constraints, caution, and change. Journal of Curriculum Studies, 37(2), 155–192. doi:10.1080/0022027032000276961 Hiltz, S. R. (1988). Learning in a virtual classroom. A virtual classroom on EIES: Final evaluation report (Vol. 1). Newark, NJ: New Jersey Institute of Technology. Hiltz, S. R. (1990). Evaluating the virtual classroom. In L.M. Harasim (Ed.), Online education: Perspectives on a new environment (pp. 133-183). New York: Praeger Publishers. Hiltz, S. R. (1994). The Virtual Classroom: Learning Without Limits Via Computer Networks. Human-Computer Interaction Series, Norwood, NJ: Ablex Publishing Corporation. Hiltz, S. R., & Turoff, M. (1997). Teaching in a virtual classroom. Collection of 81 PowerPoint slides used to structure an online course. Retrieved December 30, 2008, from http://eies.njit.edu/~hiltz/ CRProject/VC/ VCtraining/ index.htm

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Hiltz, S. R., & Wellman, B. (1997). Asynchronous learning networks as a virtual classroom. Communications of the ACM, 40(9), 44–49. doi:10.1145/260750.260764 Hobbs, V. M., & Osburn, D. D. (1989). Distance learning evaluation study report II: A study of North Dakota and Missouri schools implementing German I by satellite. ERIC ED, 317, 195. Holmberg, B. (1995). Theory and Practice of Distance Education (2nd ed.). London: Routledge. Ives, B., & Olson, M. (1984). User involvement and MIS success: A review of research. Management Science, 30(5), 586–603. doi:10.1287/ mnsc.30.5.586

Keegan, D. (1995). Distance Education Technology for the New Millenium: Compressed Video Teaching. FernUniversität, Hagen, Germany. Keegan, D. (1996). Foundations of Distance Education (3rd ed.). London: Routledge. Keller, T., & Dansereau, F. (2001). The effect of adding items to scales: An illustrative case of LMX. Organizational Research Methods, 4(2), 131–143. doi:10.1177/109442810142003 Kinuthia, W., & Dagada, R. (2008). E-learning incorporation: An exploratory study of three South African higher education institutions. International Journal on E-Learning, 7(4), 623–639.

Jackson, B., & Anagnostopoulou, K. (2001). Making the right connections: Improving quality in online learning. In J. Stephenson (Ed.), Teaching and Learning Online: Pedagogies for New Technologies (pp. 53-64). London: Kogan Page.

Knight, P. (2007). Promoting retention and successful completion on masters courses in education: A study comparing e-tuition using asynchronous conferencing software with faceto-face tuition. Open Learning, 22(1), 87–96. doi:10.1080/02680510601100184

Jankowski, D. (1997). Student attitudes toward Internet-resident course materials. Paper presented at the Association for Information Systems (AIS) 1997 Americas Conference, Indianapolis, Indiana, August 15-17.

Kruh, J. J., & Murphy, K. L. (1990), Interaction in teleconferencing: The key to quality instruction. Paper presented at the 12th Annual Rural and Small Schools Conference, Kansas State University, Manhattan. ERIC ED 329 498.

Jegede, O. J., & Kirkwood, J. (1992). Student’s anxiety in learning through distance education. This report is part of a larger project funded by a University of Southern Queensland Internal Research Grant, Toowoomba, Australia. ERIC ED, 360, 476.

Leidner, D. E., & Jarvenpaa, S. L. (1993). The information age confronts education: Case studies on electronic classrooms. Information Systems Research, 4(1), 24–54. doi:10.1287/isre.4.1.24

Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(4), 781–804. Karp, D. A., & Yoels, W. C. (1976). The college classroom: Some observations on the meanings of student participation. Sociology and Social Research, 60, 421–439.

Leidner, D. E., & Jarvenpaa, S. L. (1995). The use of information technology to enhance management school education: A theoretical view. MIS Quarterly, 19(3), 265–291. doi:10.2307/249596 Limayem, M., Banerjee, P., & Ma, L. (2002). Impact of GDSS: Opening the black box. Proceedings of the 6th Pacific Asia Conference on Information Systems (PACIS 2002), Tokyo, Japan, Sept. 2-4.

113

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Limayem, M., & DeSanctis, G. (2000). Providing decisional guidance for multicriteria decision making in groups. Information Systems Research, 11(4), 386–401. doi:10.1287/ isre.11.4.386.11874 Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–738. Linn, M. C. (1996). Cognition and distance learning. Journal of the American Society for Information Science American Society for Information Science, 47(11), 826–842. doi:10.1002/ (SICI)1097-4571(199611)47:113.0.CO;2-4 Lionarakis, A., & Papademetriou, D. (2003). The quality of the learning experience: A comparative study between open distance and conventional education. Turkish Online Journal of Distance Education, 4. Retrieved December 30, 2008, from http://tojde.anadolu.edu.tr/tojde10/index.htm. Liu, J. N. K., & Cheng, X. (2008). An evaluation of the learning of undergraduates using e-learning in a tertiary institution in China. International Journal on E-Learning, 7(3), 427–447. Lockwood, F. (1995). Open and Distance Learning Today. London: Routledge. Magambo, J. (2007). Use of Information and Communication Technologies (ICTs) in Teacher Education in Sub-Saharan Africa: Case Studies of Selected African Universities. Doctoral Dissertation, der Universität zu Köln, Uganda. Retrieved December 30, 2008, from http://deposit. ddb.de/cgi-bin/dokserv?idn=98581182x&dok_ var=d1&dok_ext=pdf&filename=98581182x. pdf

114

Marshall, J. M. (2002). Learning with technology: Evidence that technology can, and does, support learning. Report prepared for Cable in the Classroom Foundation, San Diego State University, San Diego. Retrieved December 30, 2008, from http://www.medialit.org/reading_room/ article545.html. Martz, W. B. Jr, & Shepherd, M. (2007). Managing distance education for success. International Journal of Web-Based Learning and Teaching Technologies, 2(2), 50–58. Matthews, H. R. (2000). Virtual lectures free students to think critically. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 111-113). Bolton, MA: Anker Publishing Company Inc. Mayes, T. (2001). Learning technology and learning relationships. In J. Stephenson (Ed.), Teaching and Learning Online: Pedagogies for New Technologies (pp. 16-26). London: Kogan Page. McKeen, J. D., Guimaraes, T., & Wetherbe, J. C. (1994). The relationship between user participation and user satisfaction: An investigation of four contingency factors. MIS Quarterly, 18(4), 427–451. doi:10.2307/249523 Newlin, M. H., Lavooy, M. J., & Wang, A. Y. (2005). An experimental comparison of conventional and web-based instructional formats. North American Journal of Psychology, 7(2), 327–336. Palloff, R., & Pratt, K. (2001). Lessons from the Cyberspace Classroom: The Realities of Online Teaching. San Francisco, CA: Jossey-Bass. Phipps, R., & Merisotis, J. (1999). What’s the difference? A review of contemporary research on the effectiveness of distance learning in higher education. The Institute for Higher Education Policy, Washington, DC. Retrieved December 30, 2008, from http://www.ihep.org/Pubs/PDF/ Difference.pdf.

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Piccoli, G., Ahmad, R., & Ives, B. (2001). Webbased virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401–426. doi:10.2307/3250989 Ricketts, J., Wolfe, F. H., Norvelle, E., & Carpenter, E. H. (2000). Multimedia: Asynchronous distributed education- A review and case study. Social Science Computer Review, 18(2), 132–146. doi:10.1177/089443930001800203

Shen, Q., Chung, J. K., Challis, D., & Cheung, R. C. T. (2007)... Computer Applications in Engineering Education, 15(1), 30–40. doi:10.1002/ cae.20092 Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). Manual for the state-trait anxiety inventory (STAI). Palo Alto, CA: Consulting Psychologists.

Riel, M. (1993). The SCANS report and the AT&T learning network: Preparing students for their future. Telecommunications in Education News, 5(1), 10–13.

Steele, G. A. (2008). Using online technology to encourage more student participation in a communication theory course. In Proceedings of the 2nd International Multi-Conference on Society, Cybernetics and Informatics (Vol. III, pp. 214219), Orlando, Florida, June 29 - July 2.

Russell, T. L. (1999). The no significance difference phenomenon. Office of Instructional Telecommunications, Chapel Hill, NC: North Carolina State University Press.

Tait, P., & Vessey, I. (1988). The effect of user involvement on system success: A contingency approach. MIS Quarterly, 12(1), 90–107. doi:10.2307/248809

Russell, T. L. (2001). The No Significant Difference Phenomenon: A Comparative Research Annotated Biography on Technology for Distance Education. Book available in print from The International Distance Education Certification Center (IDECC), Retrieved December 30, 2008, from https://www.arello.org/store/default.cfm? prodlistid=16-17.

Thach, E. C., & Murphy, K. L. (1995). Competencies for distance education professionals. Educational Technology Research and Development, 43(1), 57–79. doi:10.1007/BF02300482

Sambamurthy, V., & Zmud, R. (1999). Arrangements for information technology governance: A theory of multiple contingencies. MIS Quarterly, 23(2), 261–290. doi:10.2307/249754

Tucker, S. (2001). Distance education: Better, worse, or as good as traditional education? Online Journal of Distance Learning Administration, 4. Retrieved December 30, 2008, from http://www. westga.edu/~distance/ojdla/ winter44/ tucker44. html.

Santhanam, R., Sasidharan, S., & Webster, J. (2008). Using self-regulatory learning to enhance e-learning-based information technology training. Information Systems Research, 19(1), 26–49. doi:10.1287/isre.1070.0141

UNESCO. (2005). Information and Communication Technologies in Schools: A Handbook for Teachers or How ICT Can Create New, Open Learning Environments. France: ED/HED/TED/2. Retrieved December 30, 2008, from http://unesdoc.unesco.org/images/0013/001390/139028e. pdf

Schlais, D., & Davis, R. (2001). Distance learning through educational networks: The global new experience. In J. Stephenson (Ed.), Teaching and Learning Online: Pedagogies for New Technologies (pp. 112-126), London: Kogan Page.

Urban-Lurain, M., & Weinshank, D. (2000). Computing concepts and competencies. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 73-74). Bolton, MA: Anker Publishing Company Inc.

115

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Ury, G. (2004). A comparison of undergraduate student performance in online and traditional courses. Journal of Computing Sciences in Colleges, 19(4), 99–107. van Schaik, P., Barker, P., & Beckstrand, S. (2003). A comparison of on-campus and online course delivery methods in Southern Nevada. Innovations in Education and Teaching International, 40(1), 5–15. doi:10.1080/1355800032000038859 van Schaik, P., Barker, P., & Moukadem, I. (2005). Using a digital campus to support electronic learning in Lebanon. Innovations in Education and Teaching International, 42(2), 157–166. doi:10.1080/14703290500062532 Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 483–502. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. doi:10.1287/ mnsc.46.2.186.11926 Venkatesh, V., & Johnson, P. (2002). Telecommuting technology implementations: A within- and between-subjects longitudinal field study. Personnel Psychology, 55(3), 661–687. doi:10.1111/j.1744-6570.2002.tb00125.x Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Venkatesh, V., & Speier, C. (1999). Computer technology training in the workplace: A longitudinal investigation of the effect of mood. Organizational Behavior and Human Decision Processes, 79(1), 1–28. doi:10.1006/obhd.1999.2837

116

Venkatesh, V., & Speier, C. (2000). Creating an effective training environment for enhancing telework. International Journal of HumanComputer Studies, 52, 991–1005. doi:10.1006/ ijhc.1999.0367 Vigilante, R. (2000). Systems analysis: A graduate course. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 75-77). Bolton, MA: Anker Publishing Company Inc. Waits, T., & Lewis, L. (2003). Distance education at degree-granting postsecondary institutions: 2000-2001 (NCES 2003-017), U.S. Department of Education, National Center for Education Statistics. Retrieved December 30, 2008, from http://nces.ed.gov/pubsearch/pubsinfo. asp?pubid=2003017. Wang, X. (2007). What factors promote sustained online discussions and collaborative learning in a Web-based course? International Journal of Web-Based Learning and Teaching Technologies, 2(1), 17–38. Wang, Y., Peng, H., Huang, R., Hou, Y., & Wang, J. (2008). Characteristics of distance learners: Research on relationships of learning motivation, learning strategy, self-efficacy, attribution and learning results. Open Learning, 23(1), 17–28. doi:10.1080/02680510701815277 Waschull, S. B. (2001). The online delivery of psychology courses: Attrition, performance, and evaluation. Teaching of Psychology, 28(2), 143–147. doi:10.1207/S15328023TOP2802_15 Webster, J., & Hackley, P. (1997). Teaching effectiveness in technology-mediated distance learning. Academy of Management Journal, 40(6), 1282–1309. doi:10.2307/257034 Wilhelm, P., & Wilde, R. (2005). Developing a university course for online delivery based on learning objects: From ideals to compromise. Open Learning, 20(1), 65–81. doi:10.1080/0268051042000322104

Onsite and Online Students’ and Professors’ Perceptions of ICT Use in Higher Education

Wilson, B. (1990). Student’s assessment of distance learning. Paper presented at the Annual Meeting of the Midsouth Educational Researcher Association, New Orleans, Louisiana, November. ERIC ED 326 558. Yoo, Y., & Alavi, M. (2001). Media and group cohesion: Relative influences on social presence, task participation, and group consensus. MIS Quarterly, 25(3), 371–390. doi:10.2307/3250922

Young, S. S.-C., & Ku, H. H. (2008). Pioneering the Web enhanced learning between Taiwan and China (WELTaC) initiative in higher education: Potential and issues. Innovations in Education and Teaching International, 45(2), 193–203. doi:10.1080/14703290801950468

117

118

Chapter 7

Profiling Group Activity of Online Academic Workspaces:

The Hellenic Open University Case Study D. Karaiskakis Hellenic Open University, Greece D. Kalles Hellenic Open University, Greece Th. Hadzilacos Hellenic Open University, Greece

AbstrAct All undergraduate and postgraduate students of the Hellenic Open University (HOU) attend courses at a distance. The lack of a live academic community is reported by many as a drawback in their studies. Systematic exploitation of new communication and collaboration technologies is desirable in HOU but cannot be imposed universally as the average student’s IT competence level is relatively low. In this work, we present a key aspect of the development of an integrated communication environment in which collaboration spaces serving as open communities play a key role in user engagement in the whole communication environment. To track and evaluate user participation, we propose to use indices drawn from inexpensively collected usage data. Such indices, when combined with our detailed knowledge of the internal workings of user groups, provide concrete evaluation of the community online activity.

IntroductIon The Hellenic Open University (HOU) provides at-a-distance education taking into consideration a founding tenet for the universal access of students to educational resources. HOU is thus

formally based on traditional practices, namely, mailing books and educational material, encouraging students to personally communicate with their tutors, and organizing a small number of student-tutor consulting sessions per year. Thus, the use of new communication and collaboration

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Profiling Group Activity of Online Academic Workspaces

technologies is not mandatory for students to complete their studies. Still, such technologies are being systematically used for publishing announcements and general-purpose information, and for providing basic supplementary electronic material and sources for further study. As the only entry requirement of HOU students is the successful completion of high school studies, its students reflect the mean level of experience and competence in the use of electronic services in Greece, which, to date, is not particularly high; in 2005, for example, 59% of the population aged 25 to 54 had no basic computer skills (Eurostat, 2006). This problem is aggravated in the uptake of collaboration in e-learning services, which also demands an investigating attitude by the users (beyond usage skills). Thus, planning for the development of electronic services should address the need for universal access in services of stratified complexity (suitable for each team level in order for all to accept their use) and the organizational aspects of scaling up in numbers and in complexity. Moving from a model where Web technologies are used for publishing information to a model where such technologies constitute a basic working tool in the everyday life of students is a huge undertaking, which addresses both technical and cultural issues. Both types of issues are closely linked to the diversity of the backgrounds of the students and of the tutors, as well as to the availability and ease of use of the underlying infrastructure. Our laboratory is heavily involved in designing the entire communication environment provided to students and tutors. Collaboration spaces constitute a focal point in our environment, wherein users can engage in asynchronous communication, publishing content and opinions related to their work. Given that access to these spaces is allowed for every student and centrally managed but that attendance and participation are optional, these spaces function as emerging communities of practice for collaborating tutors and as communities of learning for students.

We liberally use the term community to refer to the tutors and their students who are actively engaged in the same subject during an academic year. In HOU nomenclature, this refers to a thematic unit (TU), the basic unit in HOU studies. The population of a TU consists of student groups, each of which is assigned to a tutor who oversees 10 to 35 students per group. There are some really small TUs with just one tutor and just over 10 students. There are also some very large ones with about 1,250 students in over 40 groups. Currently (2007) about 200 TUs are offered to about 28,000 students and about 1,100 tutors are allocated to TU groups. In the present article, we explore a key aspect of our work toward the goal of establishing a working communication environment. This aspect is to define indices that express user participation in the community spaces. We expect that a comparative evaluation of community online activity will help us propose actions to promote user engagement and participation across varying communities. In particular, we explore aspects of a methodology for the quantitative and qualitative follow-up and evaluation of users’ participation in combination with the participation of tutors. Our hypothesis is that we will eventually be able to provide a quantitative index of the maturity of communities and therefore will be able to offer sound advice to lagging communities. We are particularly interested in studying the participation of tutors who act as expert users providing advanced knowledge and guidance to their students and may, in the process, affect the way their students view and utilize the communication services we offer them. The rest of this article is structured in five sections. Next, we offer a coarse description of the infrastructure. Following that, we elaborate on numeric indices for quantifying the role of the tutors as experts. We then analyze specific groups with respect to their usage patterns and attempt to classify their maturity in using the collaborative work environment. We then proceed to qualitative remarks on the impact of personal attitudes

119

Profiling Group Activity of Online Academic Workspaces

of tutors toward communication and towards the uptake of the collaboration infrastructure. Finally, we conclude and highlight our research directions.

A communIcAtIon InFrAstructure to support collAborAtIon In HOU, a substantial part of the mandatory administrative procedures followed by students is carried out through a portal platform; a key example is the selection of TUs in which a student will be enrolled in the coming academic year. Typically, such portal platforms do not support specialized services for educational purposes; thus emerges the need to explore the deployment of specialized LMS (learning management system) applications. However, the latter tend to serve well advanced users only and are seldom harnessed to their potential. Because of the average level of student IT literacy, LMS acceptance and exploitation is fraught with difficulties, especially when attempted at a university scale. On the other hand, the same level of literacy does not seem to hamper the exploitation of electronic services in organization and administration. It is not, therefore, surprising that experience in EU (European Union) countries suggests that the use of new technologies in the educational domain is first introduced for organizational purposes and later for educational ones (Eurydice, 2004). HOU tutors who manage to promote the emergence of student communities often employ a constructivist learning instructional model (Savery & Duffy, 1995), usually subconsciously so. This means that they defer directly answering student queries by instead opting to generate online discussions around the query topics. The skillful running of such discussions is swiftly discovered by progressively more students who start frequenting the workspaces, realizing that

120

this is the preferred mode of communication by their group tutor. However, this approach is heavily dependent on a specific tutor’s modus operandi and is not a common characteristic of how group communication is carried out within individual student groups (and their tutors). Of course, HOU cannot match traditional campus-based universities as far as the existence of a vibrant academic community. The online workspace is no match for the campus. A high percentage of student dropout in HOU (at least, as far as the informatics undergraduate program is concerned) is related to academic factors, especially a lack of confidence to pursue universitylevel studies and the perceived lack of adequate assistance (Xenos, Pierrakeas, & Pintelas, 2002). Both reasons are far easier to emerge as obstacles when one studies at a distance as opposed to when one discusses problems with one’s classmates. Working toward addressing such problems, HOU today has an integrated common communication environment based on a portal infrastructure. To date, it supports information services, content management services, asynchronous team collaboration services, real-time services, and further administration services (see Figure 1). All users and user groups are updated in an LDAP server on an annual basis, with data drawn from the student registry management information system. Based on those user and group structures, a workplace was deployed for every TU to support the communication and collaboration among students with their group tutor, but also among tutors in the same TU. Beyond the content management space, each TU also has a forum accessed by all TU members and a special forum accessed only by the TU tutors. In the collaboration spaces of large TUs, additional spaces (inner rooms) were created to facilitate the private collaboration within one teaching subgroup (students and their tutor). Videoconferencing services were initially provided by an independent application (with its own user and group management infrastructure), but

Profiling Group Activity of Online Academic Workspaces

Figure 1. Layers of services offered through the HOU portal from the viewpoint of user initiative U se r In itia tive D ffiicu lty

I n fo rm a tio n A d m in istra tio n S e rvice s, C o n te n t S e rvice s M anagement

A w a re n e ss, Q .u e stio n s & . T e sts, C h a t, . A syn c. T e a m L e a rn in g V id e o . C o lla b o ra tio n . C o n fe re n cin g . O b je cts

now a new service allows users to access and use it in a seamless fashion through the existing (unified) LDAP-based authentication scheme. Additionally, the (open-source) Moodle LMS was installed and integrated; subsequently, it has been extensively used over a number of years by one TU to manage the submission and (automatic) grading of a large part of its homework assignments and is now progressively used by other TUs as an alternative means to communicate and to streamline several aspects of the educational process. Note that all administrative services, content management, team collaboration spaces, teleconferencing, and chatting services are hosted on different platforms but are all integrated through a common multiserver Web single-sign-on domain to provide authentication. Figure 2 shows a highlevel diagram of the overall infrastructure.

meAsurIng tHe role oF tHe communItY experts In FosterIng onlIne pArtIcIpAtIon We will start discussing the key aspects of measuring the role of the experts by drawing on statistics generated by our platform. We will first introduce the concepts using some examples before presenting the detailed results for all TUs. Participation of group members is defined as the average number of visits per month per

community member (Pm = Σ Vn/n), where a visit is defined as a sequence of successive page visits, with each page visit at most 30 minutes apart from the previous one. Such participation was examined in correlation with the activity of the expert (which is expressed as a percentage figure, Exp_Activity = Exp_Visits / 100* All_Visits). For example, with reference to Figure 3 (which also serves as a gentle introduction to our notation), we note that the members of group G37 visit the workplace on average 20 times per month (roughly once per working day), whereas that rate is about 5 visits per month for the members of G188 (y-axis). A group index denotes the size of the group (as does the corresponding circle area). Furthermore, we also note that within G188, about 6% of its overall traffic was generated by the tutors whereas in G37, this climbs up to about 9% (x-axis). Last, the dark filling of the G37 circle denotes a postgraduate group. At this point we would also like to note that we defer an actual comparative discussion (of groups G37 and G188, among others) to the fourth section. While there is a substantial qualitative difference between passive and active user contribution, we believe that such differentiation is only significant in the scope of individual user assessment (Hazari, 2004). When the focus is on the overall comparative evaluation of community activity (as in our case), the total number of visits is a sufficient index.

121

Profiling Group Activity of Online Academic Workspaces

Figure 2. The server-services architecture Use r & G ro u p D a ta LD A P s erv er

LD

s tr

a ti

on

Da

LD

AP

in i

AP

m

LD

Ad

LD

A dm inis tr ation M IS

ta

AP

m ee

AP

t in g

(N R

sc h e

PC

)

d u le

ns & ts

s

je c

st

Ob

Te

in g

web

at

R ealT im e

single sign on domain

br

ow

se

SM T P D ynam ic Group M ail Lists

P re se nc e – A w ar ene s s / C ha t

io

a rn

to es s ta A c c in D a Ad m

st

Le

ue

R epos itory

Q

LM S

Pre A w s en c e are ne s s Me e tin gs /C h

P ortal

C on te nt / e-F ora

A s y nc .

r U ser

W orkstation

Figure 3. A measurement example Participation (mean) 25

G37

20 15 10 G188

5

3

6

9

12

Expert Activity (% of total)

Figure 4 shows the aggregate results. Data regarding an undergraduate program (consisting of 13 TUs) and an affiliated postgraduate program (5 TUs) were analyzed. In 7 of those TUs, the use of collaboration services was almost null and thus we analyzed the activity in the remaining 11 (six undergraduate, PLIxx, and five postgraduate, PLSxx), accounting for a total of 2,086 engaged users. The data refer to a spring trimester of the 2005 to 2006 academic year. 122

The distributions of visits within each group are not identical (not surprisingly). As a side product, we computed two standard statistical measures of these datasets, namely kurtosis and skewness. Kurtosis in a distribution provides a way to estimate the homogeneousness in the distribution of participation in each group (by focusing on the distribution tail size). We report kurtosis in Figure 5. Skewness provides a direct way to estimate the relation between the number of users who are strong participators and those who are not. We noted that skewness was positive (ranging from 2 to 7) in all cases, meaning that very active members are significantly outnumbered by the less active ones (especially so in undergraduate groups). Herein, the differentiation between groups is less pronounced compared to the kurtosis case, suggesting that this pattern is traced in all groups. Before referring the reader to the appendix for a detailed interpretation of the particular results, we note that such interpretation is facilitated by the fact that we have a detailed knowledge of the internal workings of the reported groups. Such knowledge is easily diffused among people who

Profiling Group Activity of Online Academic Workspaces

Figure 4. The measurement results 4 0 ,0

PLS61

Participation (mean visits)

3 0 ,0 PLS60

2 0 ,0 1 0 ,0 0 ,0 -1 % -1 0 ,0

PLS50 PLI11

PLS51 PLI42

PLS62 PLI12

PLI30 PLI10

4%

9%

14%

PLI31

19%

Tutors Activity (percentage of total activity)

regularly share their tutoring experiences. Moreover, the systematic recording and analysis of activity in these spaces directly aims at tracking characteristic access patterns and at depicting problematic situations or highlighting efficient models of operation. In a working place, interaction between all the members of teams is desirable, particularly so for students. The role, however, of the tutor may be decisive since she or he, as an expert among other members, may be able to also open up new subjects and not simply respond to questions. Encouragement and participation by an instructor helps a community form more readily (Brown, 2001).

A communItY mAturItY Index bAsed on detAIled group Access proFIle AnAlYsIs For the next step in our research, we turned to a systematic analysis per group. We decided to study the access profile of every group for the first trimester of the current academic year (2006-2007), which witnessed scientifically higher levels of portal awareness and usage in the student

population. It should suffice to note that about 10 million page visits per month is now representative of the order of magnitude of portal usage. The corresponding server logs were preprocessed and imported into an industrial-strength relational database server application (which was not part of the portal infrastructure), along with student and tutor membership data for every TU. Subsequently, we built SQL (structured query language) queries to retrieve detailed statistics for every user in every group (we remind the reader that users typically attend more than one TU per year). We studied six undergraduate (PLIxx) and five postgraduate (PLSxx) TUs accounting for a total of 2,428 engaged students and 73 tutors. We calculated the distribution of access frequency of users at the day level during a period of 1 month. For each user, we recorded the number of distinct days during the month period that the user was active within the system (regardless of whether page visits indicated casual navigation, reads, or posts, or of whether the visited pages belonged to particular sections of that workspace). Reporting our results at the day level within a month was a choice made after careful deliberation of what might be considered as user participation

123

Profiling Group Activity of Online Academic Workspaces

Figure 5. Dataset kurtosis (small numbers indicate more even distributions) 90 80 70

PLI12

Kurtosis

60 50 40 30

PLI10

20

PLI30

10 0 -1 % -1 0

PLI42

PLS51 PLS62

PLS61

PLS60

5%

10%

15%

PLI31

20%

Tutors Activity

in an academic context. That context was drawn from our tutoring experience. To highlight the rationale of our resolution choice, we urge the cautious reader to contemplate the differences between an academic and a news reading context: The latter context may well deliver access statistics of several sessions dispersed within a day for the same user. In the following diagrams we report mean participation expressed in visits as well as in days (as a function of tutor activity) to smooth the transition from reporting in terms of visits to reporting in terms of days. These figures serve to establish a baseline for our results for the 2006 to 2007 academic year, and we decided to report that baseline in the format we introduced previously in this article. Incidentally, they also record an overall improvement in portal usage by the groups that we have focused on. It is instructing to note that the two diagrams are very similar, with the only exception of two quite active TUs, PLI42 (with new users recording many page visits every day they visited) and PLS60 (with active tutors actually stirring up students to watch closely the collaboration space).

124

PLI11 PLS50

The precise knowledge of the type of activity in a space would probably lead to a better analysis of the activity. Such knowledge could be of a qualitative or a quantitative nature. Qualitative issues could refer to whether a visit was mainly passive or active, whereas quantitative issues could refer to the actual duration of a visit. In any case, we believe that the total activity is an explicit indicator of systematic usage in a workspace because periodicity of usage is at least a hint that the user has indeed some benefit in visiting that space. We now turn to reporting the actual statistics. Let Pi (participation) indicate the number of distinct days (d) during a 30-day period that user i accessed the system (where 1≤i≤n, n is the numbers of users in a group and 1≤Pi ≤30). Let Pm (mean participation) indicate the mean value of Pi and let P(d) indicate the probability that Pi =d (where d is the number of distinct days). As we have already stated in the introduction of this article, the aim of studying the form of the P(d) distribution for each TU (user group) is the categorization of groups in maturity levels. Given this, we hope to be able to derive an estimate of the profile of each team by only knowing the total

Profiling Group Activity of Online Academic Workspaces

Figure 6. 2006 to 2007 student mean participation per TU (visits)

Student Participation (mean visits)

30 25 20

PLS50

P L I4 2 PLS61

15 PLS60

10 5 0 -2% -5

PLS51 P 6L 2I1 2 PLS

3%

PLP I1L1I3 1

P L I1 0 P L I3 0

8%

13%

18%

23%

28%

Tutors Activity (%page visits)

Figure 7. 2006 to 2007 student mean participation per TU (distinct days)

Student Participation (mean days)

20 18

PLS61

16

PLS50

PLS60

14 12 10 8

P L I4 2

6

PLS51 PLS62

4

P L I1 2

PLP I1L1I3 1

P L I1 0 P L I3 0

2 0 -2%

3%

8%

13%

18%

23%

28%

Tutors Activity (% pages visits)

activity of the TU. To do that, we sought simple test probability distribution functions (henceforth test pdfs) that best fitted the actual distribution at hand. A first examination of the distributions led to the more analytic examination of three typical simple pdfs. Exponential F ( A, B, d ) =

1 A−d exp( ) B B

Half-Normal F ( A, B, d ) =

1 B

2

1 d−A 2 exp(− ( ) ) 2 B

Normal F ( A, B, d ) =

1 d−A 2 exp( − ( ) ) 2 B B 2 1

We selected these three simple distributions as our main goal is not to accurately fit the actual distributions, but to categorize each TU activity instead. We used least squares to calculate the fit

125

Profiling Group Activity of Online Academic Workspaces

Table 1. Members

Pm

Least Sq Pm

Test pdf

PLI10

877

5,9

5,5

Exp

PLI11

449

7,0

7,2

Exp

PLI12

424

4,1

3,5

Exp

PLI30

129

4,1

3,6

Exp

PLI31

126

7,4

8,7

H Norm

PLI42

100

8,9

9,6

H Norm

PLS51

84

6,9

7,2

H Norm

PLS50

147

15,5

15,7

Norm

PLS60

44

14,3

14,5

Norm

PLS61

34

17,1

17,6

Norm

PLS62

14

6,0

5,8

Exp

TU

to the selected test pdf. Also, for each test pdf, we calculated a Pm(LeastSq) that minimized the sum of squares of the normalized residuals. The data for each of the eleven TUs are recorded in Table 1. We now delve into a more detailed description of each fit we observed. In the following diagrams, the actual distribution is recorded along with the distribution of the test function for the actual Pm value (and not for the Pm(LeastSq) value). For Pm values up to 6 (up to one visit per 5 days per user; see Figure 8), the distribution fits well an exponential one:

1 1− d exp( ). Pm Pm

For Pm values of 6 up to 8, the distribution gradually fits a half-normal distribution and gradually stops fitting an exponential one (see Figure 9). We believe that the exact value might also depend on the size of the group and the maturity of its members, and aim to examine this in more depth in the future. For Pm values larger than ~7 (one visit per 4 days per user; see Figure 10), the distribution fits well a half-normal one: 1 Pm

2

1 d −1 2 . exp(− ( ) ) 2 Pm

For Pm values larger than 10 (at least one visit per 3 days per user; see Figure 11), the distribution tends to fit an almost uniform distribution, which, in turn, can be approximated by the almostconstant mid-upper part of a normal pdf: C 1 d −1 2 e( − ( ) ). Pm 2 Pm

However, since most active TUs correspond to small user groups, the distributions have a particularly saw-like form.

Figure 8. Actual participation distribution and test pdf (PLI30 and PlI10, Exp) plI30 pm=4,1

30% 25%

plI10, pm=5,9

20% 16%

20%

12%

15% 8%

10%

4%

5%

0%

0% 1

126

4

7

10 13 16 19 22 25 28

1

4

7

10 13 16 19 22 25 28

Profiling Group Activity of Online Academic Workspaces

Figure 9. Actual distribution and test pdf (PLI1, Exp; PLS51, H Normal) plI11, pm=7,0

16%

pls51, pm=6,9

14% 12%

12%

10% 8%

8%

6% 4%

4%

2%

0%

0%

1

4

7

10 13 16 19 22 25 28

1

4

7

10 13 16 19 22 25 28

Figure 10. Actual distribution and test pdf (PL31 and PLI42, H Normal) plI31, pm=7,4

14% 12%

12%

10%

10%

8%

8%

6%

6%

4%

4%

2%

2% 0%

0% 1

4

7

•

1

10 13 16 19 22 25 28

Summarizing the findings, we have three clusters of TU activity: •

plI42, pm=8,9

14%

Groups in an exponential phase have Pm < ~6 days per month. Therein, average participation is relatively sparse. Based on our firsthand knowledge of how the TUs process in their day-to-day work, these are TUs in which a forum is not active and where the students occasionally visit the system to retrieve assignments and related, relatively static, material. Groups in a half-normal phase have ~7 < Pm < ~10 days per month. Therein, an active

•

4

7

10 13 16 19 22 25 28

student forum and a relatively active tutor group are observed. Groups in a normal phase have ~10 < Pm < ~20 days per month. These are TUs with strong all-around activity and a high level of community (Brown, 2001). It is important to note that in this category, the distribution is almost constant (presenting a relatively high deviation from the mean value). Also note that in the HOU context, the 20-days index can be safely considered as a realistic upper limit.

127

Profiling Group Activity of Online Academic Workspaces

Figure 11. Actual distribution and test pdf (PLS 50 and PLS61, Normal) pls50, pm=15,5

12%

6%

10%

5% 4%

8%

3%

6%

2%

4%

1%

2%

0%

0% 1

4

7

10

13 16

19

22 25

28

on obserVIng And resolVIng AttItude Issues In collAborAtIon workspAces As earlier described, since HOU communication is traditionally based on e-mail and telephone, student attendance is nowhere obligatory (except for exams, of course). Furthermore, the tutor has a mainly supporting and advisory role. However, HOU students are mostly professionals who do not easily engage in activities that do not carry a direct practical profit. The emergence and the evolution of the collaboration spaces of TUs as communities of practice is closely linked to how much these can satisfactorily address the real needs of their users. We have noted several problems that may limit user engagement and participation. • •

•

128

pls61, pm=17,1

14%

7%

Access problems (lack of basic skills and/ or adequate infrastructure) Lack of time (full-time or part-time employment and family matters may limit the availability of time to study to just some time chunks during weekends) Lack of apparent activity in the collaboration space by others, which is aggravated by physical isolation (Taplin, 2000)

1

4

7

10 13 16 19 22 25 28

A surprising finding (based on our intimate knowledge of the internal workings of groups) was that a postgraduate group had very low tutor activity because one of its most active tutors is strongly opposed to the use of collaboration technologies due to his strong preference for e-mail in the organization and carrying out of tutoring activities. This was, thus, a negative result. How does one counter such a negative stance? The answer might lie within deploying a symmetrically strong opposition. In particular, in another postgraduate group, one of the most active tutors strongly opposed the deployment of the portal-based collaboration spaces due to his strong preference for a then-existing open-source system for forum discussions. That opposition was unfortunately aggravated by several “teething” problems in the operation of the portal, at that time. It took a very focused and sustained contribution by at least one other tutor, in terms of generating fruitful discussions in the collaboration-place forum, to establish a culture of actually using the collaboration place for further work (coupled, of course, with increased system availability; Bhagyavati & Whitehead, 2005). As the portal gained credibility and opposition grew smaller, it turned out that group participation was sustained even if fruitful discussions were now forthcoming at a more relaxed pace compared to the initial phase.

Profiling Group Activity of Online Academic Workspaces

conclusIon And FurtHer work dIrectIons We have provided statistical evidence that links the maturity of the collaboration culture of study groups to the participation of the users, and we have explored the extent to which the activity of the tutors as expert users helps the student communities better adapt to an online environment. We have developed statistical indices to describe such maturity based on data that are readily available from typical server logs. This is crucial if we want our indices to be subsequently employed for identifying collaboration best practices at a large scale. There are a number of limitations in our approach. For example, we know that a small number of subgroups frequently engage in collaboration based on technologies that have not been integrated into our infrastructure, apart from e-mail (text or voice) chat mechanisms or virtual classrooms. Such collaboration statistics are much more difficult to collect reliably and we believe that this (pessimistically) skews our results. Our recent infrastructure upgrade that allows chat and meeting sessions to be organized tightly integrated with the collaboration software will increase the seamless availability of such services to our academic community and will also boost our ability to collect essential usage statistics. After all, we hope to use our detailed knowledge of the TUs that we focus on to progressively refine our indices to also reflect as accurately as possible the situation in all other TUs (currently at about 200) without requiring us to invest in understanding all of them. Not surprisingly, we are approaching the problem of the technology uptake in a rather conventional fashion, first trying several approaches on rather receptive users before applying the new concepts to more reluctant (even subconsciously) ones.

Acknowledgment This article is an extended version of “Tracking User Participation in a Large Scale Team Collaboration Environment,” which was included in the proceedings of the First International Workshop on Building Technology Enhanced Learning Solutions for Communities of Practice, held in conjunction with the First European Conference on Technology Enhanced Learning (October 2, 2006).

reFerences Bhagyavati, S. K., & Whitehead, C. C. (2005). Using asynchronous discussions to enhance student participation in CS courses. Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education SIGCSE ’05, 37(1). Brown, R. E. (2001). The process of communitybuilding in distance learning classes. Journal of Asynchronous Learning Networks, 5(2). Eurostat. (2006). News release 20/6/2006: The e-society in 2005. Retrieved from http://ec.europa. eu/eurostat/ Eurydice. (2004). Key data on information and communication technology in schools in Europe 2004 edition. Retrieved from http://www.eurydice.org Hazari, S. (2004). Strategy for assessment of online course discussions. Journal of Information Systems Education, 15(4), 349-356. Savery, J. R., & Duffy, T. M. (1995). Problem based learning: An instructional model and its constructivist framework. Educational Technology, 35(5), 31-38. Taplin, M. (2000). Problem-based learning in distance education: Practitioners’ beliefs about an action learning project. Distance Education, 21(2), 284-307.

129

Profiling Group Activity of Online Academic Workspaces

Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education, 39, 361-377.

130

Profiling Group Activity of Online Academic Workspaces

AppendIx: quAlItAtIVe AnAlYsIs oF group workIngs This appendix provides a qualitative analysis of the findings reported in the third section of this article. All discussions are with reference to Figure 4. While there are several axes of interpretation, some findings will be recurring, and we urge the reader to interpret these as nonorthogonal indications of the dynamics that exist in group collaborations. At this stage of our research, we seek to strengthen these indications by pointing out the common issues wherever they may be detected. We start by discussing groups PLS50, PLS51, PLS61, PLS60, and PLS62. These groups all refer to postgraduate TUs; we enumerate them in the respective expected order that a student would enroll in them. The figure reflects a strong indication that increased tutor activity raises student participation but group size adversely affects such participation (which is not unexpected since it is difficult to mobilize all group individuals when working at a distance). It is intriguing that PLS51 and PLS50 are relatively close in the respective student participation axis yet so far apart in the tutor activity axis. We believe this is because tutors in the PLS50 are consistently active in their workplace involvement, both in terms of communicating between them and with their groups. Frequent communication raises issues which, from time to time, transcend the boundaries of a discussion forum and may reappear in a neighboring forum, generating new rounds of collaboration. A further, subtler reason is that PLS50 is the first module that these postgraduates take. This instills a community culture, and when these students move on to PLS51, they are highly (and recently) aware of the benefits of community collaboration; presence is reinforced even without tutor involvement. This also refers to committed students who enroll in these TUs in the same year; they seem to be able to easily spot a good practice and stick with it. We thus note the flow of benefits from a group to another. Such flow is also apparent, yet more subtly so, when analyzing the apparent strong student involvement of (senior postgraduate) groups PLS61, PLS62, and PLS60. It might be tempting to compare PLS61 with PLS62 based on tutor involvement (undoubtedly, measurably apart) but subtler issues arise. It is interesting to note that PLS61 is a TU with a heavy software project management component, where the successful carrying out of assignments sometimes dictates the collaboration between students. That these students were already aware of the benefits of workplace collaboration facilitated their electing of the workplace to communicate during assignments. Note that both PLS61 and PLS62 refers to one student group per TU (and, hence, one tutor) and therefore there is no room for intra-tutor collaboration. This is in contrast to PLS60 where two tutors were involved in student tutoring and two further tutors are involved in developing educational material as well as communicating with the students as regards educational matters. So, a substantial part of the traffic generated by the tutor component of PLS60 does in fact refer to communication between tutors. In the PLS62 group, the tutor has not embraced workplace collaboration and, hence, the students have been consulting the workplace for relatively static information (for example, meeting dates and venues) and no academic discussions were made. Summarizing the postgraduate case, a unifying theme seems to emerge. This theme is that having instilled a collaboration culture in earlier TUs has been fundamental in sustaining student workplace involvement. It is reasonable to assert that we must invest as early as possible to educate the student population in workplace collaboration. Such indirect knowledge is only gained by example but is exploited in subsequent study years where tutors may ease their activity without a negative impact on student participation (allowing for obvious deviations in tutoring style); the system seems to have gained momentum. We note that the emergence of this common qualitative characteristic is best demonstrated

131

Profiling Group Activity of Online Academic Workspaces

by the kurtosis figure, which demonstrates that irrespective of tutor activity (after an initial investment), students’ access of the workplace more closely resembles that of a normal distribution. Interestingly enough, the kurtosis figure also suggests that the postgraduate groups demonstrate a more balanced way of accessing the workplace. We now turn to discuss groups PLI10, PLI12, PLI11, PLI30, PLI31, and PLI14, which all refer to undergraduate TUs (the first three being junior ones and the latter three being advanced ones). As observed in the postgraduate TUs, the larger the group, the smaller the student participation. However, in the undergraduate groups, which are on average substantially larger than the postgraduate ones, we also observe that the collaboration workplace is mostly frequented by tutors in advanced-year TUs. The first-year TUs display erratic performance, which can also be traced to their nature and educational content. For example, PLI12 is a mathematics-foundation TU where the near-zero student participation can be attributed to a number of factors. Most important and influential among these is the lack of maturity in students’ perceptions of the subject and of academic study requirements in general, as well as the limited know-how of students and tutors in collaboration technologies. That only 2 tutors (out of 25) engage in some collaboration activity is best captured, again, by the kurtosis figure, where that group is a clear outlier. A similar behavior is also demonstrated by the PLI10 group, which, again, contains students at the start of their academic path and contains informatics-foundations subjects. From then on, two clearly different paths are obvious. The first refers to the PLI11 group. Students in that group have been typically exposed to the learning curve (in terms of academic and attitude requirements) demanded by the mathematics and informatics foundations, and coupled with a strong tutor investment in collaborative technologies, display the relative emergence of a collaboration culture (with a healthy kurtosis figure), even at such a relatively large group size. It is most instructing to see that such a culture is readily harnessed by the PLI31 group, which has a reasonable participation index that is based on the majority of the student members. However, this is not the case with the PLI30 group, and we are considering the possibility that this may be linked to the educational content of that TU. It covers theoretical computer science and it may be argued that TUs with a relatively strong mathematics component are less suitable for collaborative work.

This work was previously published in the International Journal of Web-Based Learning and Teaching Technologies, Vol. 3, Issue 3, edited by L. Esnault, pp. 1-15, copyright 2008 by IGI Publishing (an imprint of IGI Global).

132

133

Chapter 8

A Method of Building Learner Model in Personalized E-Learning Xiyuan Wu Xi’an Jiaotong University, China Qinghua Zheng Xi’an Jiaotong University, China Hao Wang Xi’an Jiaotong University, China Haifei Li Union University, USA Guangdong Liu Xi’an Jiaotong University, China

AbstrAct Learner modeling is the key aspect in personalized e-learning. The quality of the personalization largely depends on the accuracy of the learner model. The core data of a learner model include generally learner’s personality characteristics, interesting etc. While personality characteristics can describe a learner’s stable traces internally, interest can describe something that a learner wants externally. But, a learner’s personality characteristic may have many attributes, and all of them may not have equal values, while learner interests exist implicitly in the information of learner network behavior. The work discusses and evaluates how to find the key personality attributes and their weight, and how to mine learner interest from learner behavior. The method has been successfully used in China e-learning for a major research university. The experimental evaluation shows the modeling method is effective in personalized e-learning. DOI: 10.4018/978-1-60566-938-0.ch008

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

A Method of Building Learner Model in Personalized E-Learning

IntroductIon The growing information on the Web is exceeding limited human cognitive capabilities and users do not have sufficient knowledge or time for choose, so personalized information services become a pressing matter recently (Berkovsky etc. 2008). Nowadays, there has been a shift from the industrial age to the information age and many of traditional instructional models are no longer suitable for today’s society characterized by rapid change, global communication and high technology (Reigeluth, 1997a, 1997b). Reigeluth maintains that one of the key results of this shift is that instruction needs to be customized rather than standardized and it needs to be learner-centered and help people learn and develop their potential. The instructor needs to become a facilitator, empowering the learners to construct their own knowledge, rather than being the sole source of direction and knowledge in the class (Reigeluth, 1997a, 1997b, 1999). The combined power of new communications and computer technologies is the driving force in this approach. The World Wide Web can be fruitfully employed to support every aspect of e-learning. The key aspect of hypermedia is that it should provide easy access to information within an interactive and customizable environment. The web-like linking of ideas that characterizes hypermedia is more akin to the functioning of human cognition than the traditional linear structure found in most educational programs. Also, as a web structure grows rapidly, it is easy for end users to customize the learning environment (Reigeluth, 1999). The economic revenues made by the online personalization industry reached 1.3 billion US dollars in 2000 and 5.3 billion US dollars in 2003. In 2001, Vermetten (2001) predicted that by 2003, 85% of the global 1000 Web sites would use some form of personalization. The world-wide revenues for personalization software rose from 10.85 million US dollars in 2000 to 93.4 million US dollars in 2005 (Kobsa, 2001).

134

The quality of the personalization largely depends on the accuracy of the user model. Learner modeling is the key aspect in personalized elearning. The core data of a learner model include generally learner’s personality characteristics, interest, etc. While personality characteristics can describe a learner’s stable traces internally, interest can describe something that a learner wants externally. But, a learner’s personality characteristic may have many attributes, and all of them may not have equal values, while learner interests exist implicitly in the information of learner network behavior. The work discusses and evaluates how to find the key personality attributes and their weight, and how to mine learner interest from learner web behavior log. Some fundamental learning theories show that learning strategies are vital aspects of personalized learning (Liu, 2002; Liu, 2004). Studies in educational psychology show that learning strategies are greatly affected by the learner’s personality characteristic (Liu, 2002; Vermetten, 2001). Personality characteristic refers to often subtle but relatively stable traits that are part of a person’s inner being. Physiologically, the characteristic includes physical traits that can be distinguished by human senses. Psychologically, the characteristic includes intellectual types, personal interests, motivation, emotion, will and others (Yang, 2003). Finding a learner’s key personality characteristic attributes is a challenging job. First, it is necessary to describe a learner’s characteristic and obtain it quantitatively. Second, it is important to discover the key attributes because the personality characteristic consists of hundreds of attributes and the importance of these attributes is not equal, with some attributes even being redundant. If all attributes in a personalized e-learning environment are considered, the problem of “dimension disaster” becomes unavoidable. One example will be an e-learning university where thousands of students are enrolled and each of them needs hundreds of attributes to describe his/her characteristic. In this

A Method of Building Learner Model in Personalized E-Learning

situation, finding appropriate strategies for each student is a challenging problem. Let’s assume that an e-learning university has 3000 students. In order to describe a learner’s characteristic, the following features are generally needed: demographic data, individual traits, user knowledge level, user skills, user interests, learning styles, user goals, learning conception, hyperspace experience, etc. Each feature is only a group name and has also many attributes to describe it. For example, the following attributes are generally needed in order to describe demographic data: name, address, class, age, sex, education, city, province, country, telephone, parents’ ages, occupation etc. Suppose 10 features are needed to represent a user’s characteristic and each feature needs 10 attributes to describe it. This means that 100 attributes will be required to describe one user’s characteristic. Clearly, it is an important research problem to reduce the number of attributes so that only key attributes are identified and used in the search for appropriate learning strategies in a personalized e-learning environment. This paper presents the results of a research project to explore learners’ key personality attributes and determine the relative weight of each one. Due to the information explosion in nowadays, to provide the information which users are interesting in is a high-priority issue involved in the development of personalized e-learning. The visiting data of users include the interest pattern of users (Guo, 2005), so users’ interests can be intelligently mined by analyzing the visiting data. Interest is positive tendency in human recognition of objects. It is only when this tendency of recognition is stable that human interest can be formed. Learning interest is a kind of interest, namely the interest carrying positive emotion and promoting students to learn more. Learning interest plays an important role in the maintaining of students’ motivation, improving the reactivity in learning, and the enhancement of learning efficiency.

The discovery of users’ interest in personalized e-learning environment is to automatically recognize the current interest model of users and to find out the spontaneous interest of users. However, the spontaneous interest possible can not facilitate user in achieving certain learning objective. Therefore, it is important to find a way to provide guidance, adjustment and incentives to users’ interest and to generate the position of interest which facilitates users’ learning and should be visited by users but can not be gained by users simply relying on their own knowledge. The work aims at proposing a method to intelligently recognize users’ learning interest in intelligent network learning environment to solve this problem. The rest of the paper is organized as follows. The section of finding key personality characteristics describes the proposed approach for obtaining the important characteristics attributes and their weight. The section of mining learner interest shows how to obtain learners’ interest from web behavior log. The results of the two sections compose the improved learner model. Finally, experiments, conducted in a university of China, show the improved learner model is effective in personalized e-learning.

An oVerVIew oF tHe metHod The schematic of building learner model is illustrated as figure 1. Finding key attributes and mining learner interest are two main sections. Finding key attributes section is responsible for obtaining the key personality characteristic attributes, while mining learner interest section detects learner’s interest by analyzing the user’s web behavior log. Improved learner model can be composed by the results of the above two sections. According to the improved learner model, the inferring method to obtain the learning strategy is applied. There are five modules and the data storage in figure 1. The modules are: (i) acquiring and representing module that describes and obtains the

135

A Method of Building Learner Model in Personalized E-Learning

Figure 1. Schematic of building learner model

learner personality characteristic, (ii) finding key attributes module that processes the initial learner model and obtains key personality attributes, (iii) mining learner interest module that exploits learner web behavior log and capture user’s learning interest, (iv)inferring learning strategy module, whose function is to generate the rules about learning strategies following improved learner model, and (v) presentation model that presents the learning strategies by following the rules in an e-learning environment. The paper focuses on the operations of the Acquiring and Representing Module, Finding Key Attributes Module and Mining Learner Interest Module, which are highlighted in figure 1. The module of finding key personality attributes receives its input from the module of acquiring and representing, which directly gathers numeric information about the learners. The output of the modules of finding key personality attributes and mining learner interest module provides the improved learner model and information for the module of inferring learning strategy to generate the rules that will be utilized by the presentation module to construct a personalized e-learning environment.

136

FInd keY personAlItY cHArActerIstIcs theoretical background As pointed out by Kobsa (2001), personalization is often a data-intensive task. Data about the user’s characteristic, data about the user’s interactive behavior with the systems (computer usage) and data about the user’s hardware, software and physical environment need to be considered by a personalized system. Kobsa argued that most systems were based on the following categories of user data for personalization: demographic data, or “objective facts” about the user; user knowledge level; user skills and capabilities level; user interests and preferences; and user goals and plans (Kobsa, 2001). user goals and plans.eferenceslifestyle)of user data for personalisation.er relat. Brusilovsky (2001) suggested that user data should include the following user features that were used by personalized systems developed until 1996: the user’s goals/tasks, knowledge, background, hyperspace experience, preferences and interests. He went further and added user’s individual traits to the list. Each of the user data categories is a group name with many attributes. For Example, demographic data include name, address, class, age, sex, education, city, province, etc.; personality traits can also

A Method of Building Learner Model in Personalized E-Learning

include many factors (e.g. Cattell 16 personality factors). The question to be answered is, with so many learners’ attributes, which of them are key for a personalized e-learning environment? Several issues should be considered. First of all, it is important to consider how to describe and capture learners’ attributes. Another important issue is how to effectively find key learner personality characteristic attributes. Many researchers have acknowledged that there is little agreement on this point (Brusilovsky, 2001) and it becomes the main challenge in exploiting learner personality characteristics for a personalized e-learning environment (Papanikolaou, 2003). Selecting appropriate personality attributes can improve learning accuracy when using numerical methods, such as decision-tree induction, to infer for personalization (Kobsa, 2001). Nowadays there are some studies on the relationship between learners’ characteristics and their learning strategies. Most of them are from the perspective of educational psychology. The methods used are correlation analysis, regression analysis and discriminator function (Busato, 1998; Vermetten, 2001). These studies only focus on the correlation study, but how to identify which attributes are key and which ones are redundant (i.e., which are the targets for reduction) still involves human experts. The process is subjective and time consuming. Furthermore, most researches simply study the relationship between a single attribute and learning strategies. They do not consider the influence of the combination of attributes to the strategies. Methods such as regression analysis cannot be applied when the attributes have interdependencies. Traditional statistical methods are employed to handle massive data sets in e-learning and the process may have to be re-designed because of the high number of attributes (Hand, 1999). Since there is no direct interaction between instructors and students in an e-learning environment, the collected data tend to be more haphazard than those obtained through traditional

face-to-face interaction, and choosing a method that can deal with the uncertainty is an important problem. Rough set theory, laid out by Zdzislaw Pawlak, provides a relatively new mathematical method to handle imprecise, uncertain and incomplete data (Pawlak, 1995, 1997). The introduction of this theory in 1980’s coincided with the surge of interest in artificial intelligence, machine learning, pattern recognition and expert systems (Ziarko, 2000). The indiscernibility(similar) relation is the mathematical basis of rough set theory and any vague(rough, imprecise) concept is replaced by a pair of precise concepts- called the lower and the upper approximation of the vague concept(Pawlak, 1997). In recently years, rough set theory has been applied to areas like machine learning, knowledge discovery, data mining and decision support (Krysinski, 2001; Pawlak, 1995, 1997, 2002; Ziarko, 2000). The inference process of rough sets naturally simulates the self-adaptive behaviors and characteristics that exist in human beings. The key advantage of the rough set approach over others is that there is no need for prior knowledge (Pawlak, 2002) and it is able to reveal the natural relationship through data itself (Pawlak, 1998). In order to remedy the shortcomings of correlation analysis, regression analysis and discriminator functions mentioned above, an improved method based on rough set theory is presented to study how to find learners’ key personality characteristic attributes and how these attributes influence learners’ learning strategies. The method first constructs an information system for learners’ personality characteristics, and then applies an improved algorithm based on rough set theory. The algorithm builds the core set and the nocore set to calculate the attribute reducts based on the logical calculation directly. At last, the method analyzes the results to find learners’ key personality attributes and their relative weights. The experiments and analysis have shown that the method is able to solve the problems of

137

A Method of Building Learner Model in Personalized E-Learning

dimension disaster and huge data volume in a personalized e-learning environment. For the sample data set, the initial personality characteristic has 28 attributes. After the process by the algorithm, the number of attributes of the key personality characteristic can be reduced to less than 25% of the original number and the reduction rate of data volume is about 50%.

concepts And definitions Educational psychology research and practices have shown that personality factors(traits), learning styles and learning conceptions have influences on learning strategies (Carson & Longhini, 2002; Chamot, 2005; Ehrman, Leaver, & Oxford, 2003; Ford, 2000; Vermetten, 2001; Wenden, 1991). Personality factors are representative of the affective and cognitive aspects of individual traits. Recent research results in psychology have shown that investigating personality factors helps predict a person’s learning patterns and strategies (Vermetten, 2001). Learning styles denote cognitive styles observed specifically in a learning context. Cognitive styles are tendencies that are consistently displayed by individuals to adopt a particular type of information processing strategy (Ford, 2000). Learning conceptions are a person’s conceptions and ideas about what learning is. They have a strong impact on which learning strategies will be used (Vermetten, 2001). There is still a debate about the definition of learning strategy in education and educational psychology. Our understanding of learning strategies is mainly based on the experiences gathered through the study of foreign language (mainly English) at Xi’an Jiaotong University, China. College English is a required course for almost all students in Chinese universities. Personality characteristics include personality traits, language learning styles and language conception, and learning strategies include metacognitive strategies, affective strategies, form-focused strategies, meaning-focused strategies, compensation and social strategies (Cohen, 1998; Liu, 2003).

138

Definitions used in this paper are presented below.

Definition 1. Personality Characteristic A learner’s personality characteristic is the relatively stable traces that are revealed internally. Personality characteristic include personality trait, learning style and learning conception. It can be represented as CA={PT,LS,LC}CA. is the whole attribute set for a personality characteristic. PT is the attribute subset for describing personality trait, one example being the sixteen personality factors (“16 PF” for short) identified by Raymond B. Cattell in the 1940s (Fehringer, 2004). LS is the attribute subset for describing learning style, such as visual, audio or experimental learning style. LC is the attribute subset for describing learning conception, such as self-management conception in learning. In this paper, LC is represented with the16 PF proposed by Cattell, Eber, & Tatsuoka (1970). Warmth, Brilliance, Emotional stability, Dominance, Excitement, Persistence, PT Social boldness, Sensitivity, Vigilance, Imagination, Shrewdness, Apprehension, Radicalism, Self reliance, Self discipline, Tension A, B, C , E, F, G, H , I , L, M , N , O, Q1, Q 2, Q 3, Q 4

LS refers to the language learning styles. It is represented with the scales of the special questionnaire, which is revised to fit the situation of Chinese students by School of Foreign Language, Xi’an Jiaotong University, according to the research of Oxford (2001), Qiufang Wen (2004).

A Method of Building Learner Model in Personalized E-Learning

LS

Visual | Auditory | Experimental, Independent | Dependent, Group | Individuality, Analytical | Synthesis, Systematic | Random, Spontaneity |Thoughtfulness GROUP1, GROUP 2, GROUP 3, GROUP 4, GROUP 5, GROUP 6

LC refers to the language learning conception. It is represented with the scales of the special questionnaire, which is revised to fit the situation of Chinese students by School of Foreign Language, Xi’an Jiaotong University, according to the research of Oxford (2001), Qiufang Wen (2004). LC {Self-management, Dependence on mother tongue, Self-effiicacy, Ascribing conception, Language learning conception, Learning strategy conception} {SM, DOMT, SE, AC, LLC, LSCC} CA can be described as the set of three subsets and each subset has its own attributes. Details are shown in the following. The corresponding abbreviations for each attribute are defined after the third equal sign.

Even if a learner’s demographic data, and other data concerning skills and capabilities level, interests, goals, etc., are not considered, there are at least 28 attributes about each learner’s personality trait, learning style and learning conception. So, it is important to find an approach to select the key attributes.

Definition 2. Learning Strategy Learning strategies can be described as a set with five elements: D={mcs, afs, ffs, mfs, css}

where, mcs represents metacognitive strategy, afs represents affective strategy, ffs represents form-focused strategy, mfs represents meaning-focused strategy, css represents compensation and social strategy. •

CA PT, LS, LC {{Warmth, Brilliance, Emotional stability, Dominance, Excitement, Persistence, Social boldness, Sensitivity, Vigilance, Imagination, Shrewdness, Apprehension, Radicalism, Self reliance, Self discipline, Tension} {Visual | Auditory | Experimetnal, Independent | Dependent, Group | Individuality, Analytical | Synthesis, Systematic | Random, Spontaneity |Thoughtfulness} {Self management, Dependence on mother tongue, Self efficacy, Ascribing conception, Language learning conception, Learning strategy conception}} {{A, B, C , E, F, G, H , I , L, M , N , O, Q1, Q2, Q3, Q 4}, {GROUP1, GROUP 2, GROUP 3, GROUP 4, GROUP 5, GROUP 6}, {SM , DOMT, SEE, AC , LLC , LSC }}

•

•

•

•

Metacognitive strategy refers to planning the organization of a learner’s learning, such as establishing the goal for learning, making a schedule, etc. Affective strategy refers to controlling one’s affection during learning, such as encouraging oneself when feeling depressed. Form-focused strategy refers to the concrete approach to mastering the knowledge, such as memorizing the words by reciting. Meaning-focused strategy refers to the practice of reading, speaking, listening, etc., such as improving listening ability by listening to foreign language radio. Compensation and social strategy refers to the methods used during the communications with others, such as gesturing when one can not express any words (Busato, 1998; Fehringer, 2004).

139

A Method of Building Learner Model in Personalized E-Learning

Definition 3: Information System for Learners’ Personality Characteristic(ISLPC) The construction of an information system for the personality characteristic of learners is the foundation for analyzing key personality characteristic attributes. In essence, the information system is the set of learners. The set can be represented as S=(U,CA,⋃D,V,f). U is the non-empty finite set for all learners. CA is the non-empty finite attribute set for the personality characteristic of learners as defined in definition 1. D is the non-empty finite set for the learning strategies as defined in definition 2. CA⋂D=⊘, CA⋃D=AS, where AS is all the attributes of S;V V , where Va is the value a AS a domain for a∈AS. f : U AS V is a single mapping function such that there is a unique value in V for all a∈AS. For example, if, Warmth∈As VWarmth={1,2,3,4,5,6,7,8,9,10}, for a learner z in U, s/he has a score of 7 through Cattell 16 PF questionnaire, then f(z,Warmth)=7.

Definition 4. Key Personality Characteristic Attribute Set A key personality characteristic attribute (‘key personality attribute’ for short) is the one that is vital to infer learners’ personalized learning strategies. The key personality characteristic attribute set, namely the reduct, is the reduction result of applying the approach described in this paper. Reduction is the process through which personality characteristic attributes, that have little influence on the learning strategies, are eliminated according to the implicit relationships among personality attributes and learning strategies. In rough set theory (Pawlak, 1997), suppose R⊆CA, x U : R(x ) X is the RX⊆U, R* (X ) lower approximation of X. posR (D ) X U /D(R* X ) is R-positvie region of U/D and is essentially the union set of all objects, which can be classified into U/D using the learners’ personality characteristic

140

subset R. If α∈R, posR (D ) posR {a } (D ), then the α attribute is superfluous in R. Otherwise, α is indispensable in R. If every α in R is indispensable, R is independent of D. For details see the reference (Pawlak, 1997). For a given information system of learners’ personality characteristic S, the key personality characteristic attribute set CA’ , namely the reduct, is a non-empty subset of CA such that (1) posC ' (D ) posC (D ), (2) CA’ is independent of A A D. redD(CA) represents the set of all reducts of CA. The core set is the intersection of all reducts, namely core(CA)=⋂redD(CA) . The core set is included in every reduct and is the most important subset of attributes (Pawlak, 1997).

Definition 5:Weight Value of Key Personality Characteristic Attribute Different key personality attributes have different weight values. For one key personality characteristic attribute α∈CA’ ,the weight value of a is defined as follows. SGF (a,C A' , D )

(| posC ' (D ) | | posC ' A

A

{ }

(D ) |)/ | U |

Definition 6: Discernibility Function If ai∈C A, x,y∈U, and belong to different equivalence sets of di∈D, a(x,y)={a1,a2,…,ak} represents the subset of CA and ai∈CA can differentiate x and y in U (the learner’s domain). a(x , y ) a1 a2  ak. If a(x,y)=⊘, then let ∑a(x,y)=1 . The discernibility function is defined a(x , y ). as: ( x ,y ) U U

Theorem 1:The minimal disjunctive normal form of the discernibility function △ corresponds to the all reduction sets of S. This has been proved by Zhang (2001).

A Method of Building Learner Model in Personalized E-Learning

Definition 7:Reduction Efficiency The data volume of the information system before the reduction is defined as: ES=|CA|*|U| and after the reduction is defined as: ES' | C A' | * | U |. The ' reduction efficiency is: E 1 E S / E S .

obtaining the personality characteristic Attributes This section describes how to construct a learner model, how to acquire the original data of a learner’s characteristic, and how to represent, organize and manage them. The learner model, shown as figure 2, is the collection of learner information. In figure 2, PF,LS,LC, Learning strategy were defined in the Concept and Definitions section. Personality factor, learning style etc., are stable features of a learner and are traditionally extracted not by a simple interview, but by specially designed psychological tests (Brusilovsky, 2001). So, simple interviews cannot extract the learner data needed in this paper. Psychological tests and questionnaires are specifically designed to acquire the data of learners. To acquire learner personality trait data, Cattell’s 16 Personality Factor questionnaire is applied, as demonstrated in figure 3. There are many methods to measure personality traits, such as 16PF(Cattell’s 16 Personality Factor)(Cattell, Eber, & Tatsuoka, 1970), EPQ(Eysenck Personality Questionnaire) (Eysenck, H. J. & Eysenck, S. B. G., 1975; Eysenck, S. B. G., Eysenck, H. J., & Barrett, P., 1985), MMPI(Minnesota Multi-

phasic Personality Inventory) (Hathaway, 1951; Hathaway & McKinley, 1967), CPI(California Psychological Inventory) (Gough, 1975), and MBTI(Myers-Briggs Type Indicator)(Myers, McCaulley, & Most, 1985), and so on. There are different about the definition of personality trait etc., so the methods often do not agree with each other (Fehringer, 2004). To eliminate the confusion, many personality psychologists have attempted to develop a common taxonomy. A notable attempt at developing a common taxonomy is Cattell’s 16PF Model, which is based upon personality adjectives taken from the natural language (Fehringer, 2004). The Cattell 16 PF model is probably the most widely used system for categorizing and defining personality. Other similar systems exist and may be preferred by certain organizations and professionals, but 16 PF in its various forms is universally understood (Famous models Cattell 16PF profile, n.d.). To acquire the data describing learners’ learning styles, learning conceptions and learning strategies, three questionnaires were applied for this paper. These questionnaires are based on the research of Oxford (2001), Cohen (2002) and Qiufang Wen (2004) etc., and were revised by the School of Foreign Language, Xi’an Jiaotong University according to Chinese students’ special needs. Learning styles denote cognitive styles observed specifically in a learning context. Cognitive style represents tendencies that are consistently displayed by individuals to adopt a particular type of information processing strategy (Ford, 2000). Learning is one of the cognitive activities. Learning conception is a person’s conceptions and ideas

Figure 2.Construction of the learner model

141

A Method of Building Learner Model in Personalized E-Learning

Figure 3. Cattell’s 16 PF questionnaire for acquiring data of a learner’s personality traits

about what (academic) learning is. It is believed to have a strong impact on learning strategy use (Vermetten, 2001). Entwistle argued that learning strategy is about how students tackle a specific learning task (Entwistle, 1998). To find key personality characteristic attributes, the learner model needs to be represented with one structure. Since inferences operate on the contents of user models, methods for user model representation and inferences are often closely related (Kobsa, 2001). In this paper, the approach based on rough set theory is applied on the learner model, so the model needs to be represented as an information system.

process of Finding key personality characteristic Attributes Because of the huge volume of learners and the high number of personality characteristic attributes in an e-Learning environment, the traditional reduction methods (Zhang, 2001) are generally not suitable. This paper proposes an improved method that directly builds a core set and a nocore set and calculates the reducts based on the logical calculation. A large discernibility matrix (Nguyen, 1998) is not necessary in this method. The attributes in the nocore set can be absorbed in advance to reduce the volume of calculation. A detailed explanation of the optimal strategy is described in Algorithm subsection. Personality characteristic attributes, denoted by CA, are the conditional attributes and learning strategies, denoted by D, are the decision

142

attributes. Once the reduct set is available, the key personality attributes and the weight valuesSGF can be computed.

Preprocessing Since the initial data acquired by the specially designed psychological tests is a quantitative measurement (the score in each attribute), they need to be transformed into a qualitative measurement in order to use the approach. According to the theory of psychological tests and the histogram method (Poosala, 1997), learner personality trait data, learner learning style data, learner learning conception data and learning strategy data can be quantized into three degrees (1 for low degree, 2 for medium, 3 for high) or four degrees (1 for low degree, 2 for rather low, 3 for medium, 4 for high). The equiwidth histogram for learner personality traits is shown in figure 4. Based on the above discussion, a function can be defined, : U [1, 2, 3], where U is the universe of discourse of the function. For example, the value γ(x) of the function for an input xof the initial data of the learner personality traits, represents the degree of x. The function is equal to:

(x )

1, 2, 3,

1 4 8

x x x

3 7 10

Then, for an input whose value equals 6, the value of function f is γ(6)=2 . The interpretation of

A Method of Building Learner Model in Personalized E-Learning

this is that one personality trait of a learner can be considered as medium degree. A similar method is applied to preprocess the data describing the learners’ learning styles, the learners’ learning conceptions and the learning strategies.

f (x j , a )}

compare other subjects in the domain. Else, compare the attributes in CA respectively in file; record the attributes to the variable attr that have unequal values in the two subjects and record the number of the attributes to the variable flag. At the same time, the optimal strategy is applied, that is to say, if the attr set and the core set have common attribute, the attributes recorded can be absorbed in advance and other attributes need not be compared continually; else, continue to compare the next attribute until all attributes in CA are compared. See the value of the variable flag. If the value is 1, the attribute in the variable attr is added to the core set; else, the attributes in the variable attr are added to the nocore set. The Cartesian product is generalized over the core set and the nocore set .The red set is the set that is minimum cardinality of the result, namely, the set of key personality characteristic attribute. According to the formula of definition 5, calculate weight value of the key attributes. The following is a detailed description of the algorithm. Algorithm: A rough set based approach to find learners’ key personality characteristic attributes.

The algorithm compares subjects in the domain U of S one by one. If two subjects have the same value in the attribute d∈D, do not need compare the attributes in CA and continue to

Input: Information system S=(U,CA⋃D,V,f) Output: The set of reduced result red and the set of key personality attribute K. Approach:

Algorithm The core is the set of the single attribute by which any two learners can be distinguished in the domain. It is defined as follows. core = {c ∈ C A : ∃c, such that ∀c* ∈ C A , c* ≠ c, having (f xi , c* ) = f ( x j , c* ) and f ( xi , c) ≠ f ( x j , c)}

That is to say, there exists a single attribute c for which any two learners (x, y) in U have different values on the attribute c and have the same value in other corresponding attributes. The nocore set includes the attributes that are not in the core set, but can still be used to distinguish some two learners in the domain: nocore

{a C A : a

core, having f (x i , a )

Figure 4.

143

A Method of Building Learner Model in Personalized E-Learning

Table 1. An example of an information system Learner Subject

CA

D1

• •

• •

144

Personality Characteristic Attributes

Learning Strategy

one

two

three

four

……

A

1

2

1

1

……

B

1

1

2

1

……

C

2

2

1

1

……

E

2

2

3

2

……

F

1

1

1

2

……

G

3

3

2

2

……

H

2

2

2

2

……

I

3

3

2

2

……

L

1

1

2

2

……

M

1

1

3

1

……

N

2

2

3

3

……

O

3

3

2

3

……

Q1

1

1

3

2

……

Q2

2

2

1

3

……

Q3

3

3

2

3

……

Q4

2

2

3

3

……

GROUP1

2

2

3

1

……

GROUP2

3

3

3

1

……

GROUP3

3

3

2

1

……

GROUP4

2

2

1

2

……

GROUP5

2

2

1

3

……

GROUP6

1

1

1

2

……

SM

1

1

3

3

……

DOMT

3

3

2

2

……

SE

2

2

1

1

……

AC

2

2

3

3

……

LLC

2

2

3

2

……

LSC

2

2

3

1

……

mcs

3

2

3

3

……

Step 1: Let red=⊘ ; nocore=⊘ ; core=⊘ ; flag = 0; attr=⊘ ; Step 2: Compare subjects in the domain one by one, calculate core set and nocore set. (Details of step 2 are given in Appendix.) Step 3: red=min{|α|α|core×nocore} ; Step 4: Convert S=(U,CA⋃D,V,f) to S’=(U,red⋃D,V,f) ;

• •

Step 5: According to the formula of definition 5, calculate SGF; Step 6: Weight value = SGF.

The following example shows the application of the optimal strategy. Table 1 shows an example of an information system.

A Method of Building Learner Model in Personalized E-Learning

According to the above algorithm, when comparing the learner subjects one and two in Table 1, core={A} is obtained, because the subjects one and two can be distinguished only by the single attribute A; when comparing the subjects two and three, attr={A} is got because it is the attribute that has unequal values on the subjects two and three. According to the optimal strategy, since attr⋂core≠⊘, it is not necessary to continue to compare the values of the attributes B, C and others on the subjects two and three. The reason is that if one more step is calculated, attr = {A, B} is got. However, according to the logic absorb principle (Zhang, 2001), {A, B} will be absorbed to {A} ultimately.After applying the optimal strategy in this step, core= {a} and nocore=⊘have been obtained without comparing the other attributes, such as b, c and others. The effect is prominent because the number of attributes in ISLPC is high. In the worst case, the number of the attributes is the cardinality of CA, and the time complex is O(N2*CA) . Table 2 shows the final results of core set and nocore set.

mIne leArner Interest From beHAVIor The modeling of users’ learning interest is basis of recognition of users’ learning interest. There are two solutions to build user interest model: explicit and implicit. However, the explicit approach involves the user, takes time and effort, and

user interest may change over time. This research focuses on implicit approaches that capture user interests without the user intervention and reflect changing user interests. According to the sources of user data, implicit user modeling approaches can be based on a user’s behavior or the contents of web pages that a user has visited, or both. A user behavior based approach observes a user’s action such as click, visiting retention time, times of visit, navigation path, action of saving, editing, revising, downloading, and the key words input in search engine, etc. This technique can obtain common user profiles based on association rule discovery and usage based clustering etc. However, as the association rule discovery is based on the web pages already visited, it can only recommend those web pages which have been visited by old users to new users whereas can not recommend those unvisited web pages. A content based approach analyzes the contents of web pages that a user has visited. Kim et al. propose a divisive hierarchical clustering approach to build user interest hierarchy model that can be learned from the contents of web pages bookmarked by a user (Kim & Chan, 2008). This approach can only recommend the resources similar to those resources which have been visited by users, whereas some researchers have found that users consider the unexpected more valuable. A hybrid approach observes user’s behavior and the contents of web pages visited by a user.

Table 2. Core set and nocore set for 5 learning strategies Learning Strategy

Personality Characteristic Attribute Set Core Set

Nocore Set

mcs

{E, H, O, Q4, GROUP1, DOMT, LLC}

{C, I, M, N, Q2, GROUP3, GROUP4, LSC}

afs

{B, E, G, H, I, N, O, Q2, Q3, Q4, GROUP2, GROUP3, AC, LLC, LSC}

⊘

ffs

{E, GROUP4}

{I, Q2, SE}

mfs

{A, C, G, H, N, O, Q2, Q3, Q4, GROUP1, GROUP3, GROUP6, DOMT, SE, AC}

⊘

css

{G, I, M, O, Q4, GROUP2, GROUP5, AC, LLC, LSC}

{A, C, F, GROUP1, GROUP3, GROUP4}

145

A Method of Building Learner Model in Personalized E-Learning

Trajkova and Gauch build user profile based on concepts from a predefined ontology (Kim & Chan, 2008; Trajkova & Gauch, 2004) . Tan Qiong etc. (2002) use the local autonomous agents to percept user’s action and adopt a learning algorithm to get user interest profile. These techniques show that the usefulness and accuracy of the resulting recommendations are increased (Canales etc., 2007). This article uses method of invisible tracking, which does not require users to provide information and recognize users’ interest by system automatically inspecting using actions.

learner Interest model Learner interest model is expressed in the form of weighted key words vector, including the interest in single knowledge point and the ranking of interest in visiting several knowledge points. The interest in single knowledge point is expressed as below: User_Interest=(UID,knlElem,ID,InterestGrad e)

In which: UID is the user’s ID; knlElemID is ID of knowledge point; InterestGrade is UID’s degree of interest in the knlElemID . If user’s visiting time exceeds a certain threshold value, then it is supposed that user has interest in the content visited. The threshold is determined by previous experiences. The computing of degree of interest involves three parameters: the frequency of visiting, last time visited, and the retention time of visiting. The longer of retention time of visiting, more of frequency of visiting, the nearer of last time visited to current time, the higher of user’s interest in one knowledge point. Degree of interest is in proportion to retention time of visiting and frequency of

146

visiting and in inverse proportion to the difference between last time visited and current time.

processing Flow The detailed processing flow and algorithm can be described as below: •

• •

Step 1: Rank the resources visited by the user according to duration of visit and the click time when resource is visited; Step 2: Select the top-N resources; Step 3: According to the results computed by the following formula, the degree of interest in all knowledge points is updated.

Kweight=Ninter*Linter/Tinter In the formula, Kweight is the degree of interest in knowledge point, Ninter is the time of clicking on knowledge point, Linter is the retention time on knowledge point, and Tinter is the time difference between the time when knowledge point is visited and current time. By processing described above, the user’s learning interest model can be set up to guide user’s learning. When log on next time, a user would enter the automatically showed resource page suitable for his or her interest instead of the default home page. If there is any new resource suitable for his or her interest, this resource can be directly recommended to him or her in order to suit his or her individual needs.

experIments And AnAlYsIs Based on the above approach, a Personalized English Learning System (PELS) was developed. It aims to facilitate learners by providing diagnosis and advice to learners and representing the appropriate knowledge for novices. Figure 5 shows PELS’s Web site and Figure 6 is the main screen of PELS.

A Method of Building Learner Model in Personalized E-Learning

Figure 5. The PELS Web site

Figure 6. The main screen of PELS

Finding key personality characteristics

with thousands of students, the result should be remarkable.

The above method is illustrated by the following example. More than 300 students from several colleges of Xi’an Jiaotong University, China, have used the personalized English e-Learning system to improve their English skills. 157 valid samples have been collected. In this situation, the personality characteristic of each sample has 28 attributes and, according to definition 7, the value of ES is 4396 (157 * 28 = 4396). As shown in the following, after processing by the above method, the number of key personality characteristic attributes can be decreased to below one-forth and the reduction rate E is about 50%. If the method were applied to a large-scale e-learning university

As described in definition 2, learning strategies set D={mcs,afs,ffs,mfs,css} includes multiple attributes. In order to simplify the calculation, these five learning strategies can be separately implemented to speed the process of finding the optimal learning strategies. So, S=(U,CA⋃D,V,f) can be decomposed into five different subsystems {S1,S2,S3,S4,S5}, where Si={U,Ri,V,f} , Ri=CA⋃{di}, and di(1≤i≤5 )represents the ith element in the set D={mcs,afs,ffs,mfs,css}. After applying the reduction algorithm, described in the Finding Key Personality Characteristic Attributes section, for Si={U,Ri,V,f}, (1≤i≤5), the key personality characteristic attributes for

147

A Method of Building Learner Model in Personalized E-Learning

Table 3. Key personality characteristic attributes for 5 learning strategies Learning Strategy

Key Personality Characteristic Attribute Set

mcs

{C, E, H, I, M, N, O, Q2, Q4, GROUP1, GROUP3, GROUP4, DOMT, LLC, LSC}

0.54

afs

{B, E, G, H, I, N, O, Q2, Q3, Q4, GROUP2, GROUP3, AC, LLC, LSC}

0.54

ffs

{E, I, Q2, GROUP4, SE}

0.18

mfs

{A, C, G, H, N, O, Q2, Q3, Q4, GROUP1, GROUP3, GROUP6, DOMT, SE, AC}

0.54

css

{A, C, F, G, I, M, O, Q4, GROUP1, GROUP2, GROUP3, GROUP4, GROUP5, AC, LLC, LSC}

0.57

five kinds of learning strategies are available and they are given in Table 3. Before being processed by the algorithm, the initial personality attribute characteristic sets are: After being processed by the reduction algorithm, for metacognitive strategy (‘mcs’ for short), the key personality characteristic attribute set is: Key Personality Characteristic Attribute Set= {{C,E,H,I,M,N,O,Q2,Q4},{GROUP1,GROUP3 ,GROUP4},{DOMT,LLC.LSC} The following less important attributes are eliminated. The reduction rate is almost 50%. {{A,B,F,G,L,Q1,Q3},{GROUP2,GROUP3,GR OUP6},{SM,SE,AC}} The results of the other four learning strategies are shown in table 3. Personality characteristic attributes included in each learning strategy vary significantly. No one characteristic attribute is related to all 5 strategies. Sensitivity (I), apprehension (O), self-reliance (Q2), tension (Q4), and group|individuality (GROUP3) are the attributes that affect four learning strategies. Brilliance (B), excite-

148

Key Personality Characteristic Attribute Set CA

ment (F), systematic|random (group5), and spontaneity|thoughtfulness (group6) are attributes that affect only one learning strategy. SGF is the weight value of each key personality attribute, and the detail is illustrated from table 4 to table 8. Key personality characteristic attributes have important influence on learning strategies and should be considered top-priority. The assessment of key personality attributes need to be revised progressively and iteratively following learners’ learning situations in an e-learning environment. According to definition 7, before using the reduction algorithm, the value of ES for S1 is 4396 (157 * 28 = 4396) and after the reduction, the value of E’S is 2355 (157 * 15 = 2355). The reduction rates E for S1, S2 and S4 are 46% respectively. The reduction rate E for S3 is 82%. The reduction rate E for S5 is 43%. Figure 7 shows that the data volume has been dramatically reduced.

mining learner Interest From behavior PELS is the undergraduate students’ on-line English learning actions in Xi’an Jiaotong University. Data were collected from PELS and there are 10471 learning log records of undergraduate

A Method of Building Learner Model in Personalized E-Learning

Table 4. The weight value of key personality attributes for mcs Key Personality Attributes (for mcs)

Attribute Abbr.

Weight Value (SGF)

Visual|Auditory|Experimental

GROUP1

0.1146

Shrewdness

N

0.0764

Social boldness

H

0.0701

Dependence on mother tongue

DOMT

0.0637

Language learning conception

LLC

0.0510

Sensitivity

I

0.0382

Group|Individuality

GROUP3

0.0382

Apprehension

O

0.0255

Self-reliance

Q2

0.0255

Tension

Q4

0.0255

Emotional stability

C

0.0127

Dominance

E

0.0127

Learning strategy conception

LSC

0.0127

Imagination

M

0.0009

Analytical|Synthesis

GROUP4

0.0009

Table 5. The weight value of key personality attributes for afs Key Personality Attributes (for afs)

Attribute Abbr.

Weight Value (SGF)

Ascribing conception

AC

0.0701

Sensitivity

I

0.0510

Independence|Dependence

GROUP2

0.0510

Social boldness

H

0.0446

Tension

Q4

0.0446

Persistence

G

0.0382

Self-reliance

Q2

0.0318

Apprehension

O

0.0255

Self-discipline

Q3

0.0191

Learning strategy conception

LSC

0.0191

Language learning conception

LLC

0.0127

Brilliance

B

0.0008

Dominance

E

0.0008

Shrewdness

N

0.0008

Group|Individuality

GROUP3

0.0008

149

A Method of Building Learner Model in Personalized E-Learning

Table 6. The weight value of key personality attributes for ffs Key Personality Attributes (for ffs)

Attribute Abbr.

Weight Value (SGF)

Dominance

E

0.2803

Analytical|Synthesis

GROUP4

0.0828

Self-efficacy

SE

0.0764

Sensitivity

I

0.0318

Self-reliance

Q2

0.0318

Table 7. The weight value of key personality attributes for mfs Key Personality Attributes (for mfs)

Attribute Abbr.

We i g h t (SGF)

Social boldness

H

0.0382

Apprehension

O

0.0318

Warmth

A

0.0255

Emotional stability

C

0.0255

Tension

Q4

0.0255

Ascribing conception

AC

0.0255

Persistence

G

0.0127

Self-reliance

Q2

0.0127

Self-discipline

Q3

0.0127

Visual|Auditory|Experimental

GROUP1

0.0127

Dependence on mother tongue

DOMT

0.0127

Self-efficacy

SE

0.0127

Shrewdness

N

0.0009

Group|Individuality

GROUP3

0.0009

Spontaneity|Thoughtfulness

GROUP6

0.0009

students in the School of Electronic and Information Engineering, the School of Energy and Power Engineering and the School of Human Settlement and Civil Engineering. On-line English learning includes seven parts: reading, listening, writing, translation, verbal and grammar. Each part involves a great amount of learning materials. It can be inferred from one user’s visiting frequency and retention time on web pages if the user is interested at the content of web pages or not.

150

Va l u e

After analyzing 10471 learning log records, we have obtained the interest data of 318 users, some of which are listed as figure 8. The first column is the learner’s ID name used when registering. The data in column 2 to column 8 respectively represent users’ degree of interest in each of the seven parts. We performed normalization on the computing results of the formula for comparison. We performed statistic analysis to find out the head count of users’ with highest degree of inter-

A Method of Building Learner Model in Personalized E-Learning

Table 8. The weight value of key personality attributes for css Key Personality Attributes (for css)

Attribute Abbr.

Weight Value (SGF)

Sensitivity

I

0.0446

Visual|Auditory|Experimental

GROUP1

0.0382

Warmth

A

0.0255

Persistence

G

0.0255

Imagination

M

0.0255

Emotional stability

C

0.0127

Excitement

F

0.0127

Apprehension

O

0.0127

Tension

Q4

0.0127

Independence|Dependence

GROUP2

0.0127

Ascribing conception

AC

0.0127

Language learning conception

LLC

0.0127

Learning strategy conception

LSC

0.0127

Group|Individuality

GROUP3

0.0007

Analytical|Systhesis

GROUP4

0.0007

Systematic|Random

GROUP5

0.0007

Figure 7. Reduction efficiency

est in each of the seven parts in those 318 users. Most learners (125 head count) were interested in reading part and the next part with most people having highest degree of interest in it was listening. Grammar is the part with least people having highest degree of interest in it.

conclusIon The work investigates learning modeling method in personalized e-learning, which include two

important aspects: find key personality characteristic attributes and mining learning interest from web log. From table 4 to table 8, the conclusion can be drawn that the learning style attribute, visual|auditory|experimental, has the deepest influence on the metacognitive strategy; the learning conception attribute, ascribing conception, has the deepest influence on the affective strategy; the personality trait attributes, dominance, social boldness and sensitivity, have the deepest influences on the form-focused strategy, meaning-focused strategy, and compensation and social strategy,

151

A Method of Building Learner Model in Personalized E-Learning

Figure 8. Learner Interests and Weight

respectively. Sensitivity, the personality trait, contributes most to all the learning strategies. Furthermore, these significant relationships make substantive sense. For example, one of the major characteristics of being ascribing conception is if he or she believes that the key factor for successful learning is himself/herself. It is not surprising that an individual with a strong ascribing conception use affective strategy to learn. After finding the key attributes of learner characteristics, a further process can be applied in order to generate the association rules between key attributes and each learning strategy. These association rules can be used to construct a personalized e-learning system (Wu, 2005). Rough set theory can also provide the initial method for rule generation. The improved approach is described in (Chang, 1999) and the value of SGF is used as heuristic information. All attributes are sorted according to the SGF value. The improved approach first processes the key attributes, and then processes other attributes that have higher values of SGF one by one. If the optimal rules have already been obtained in one phase, the remaining Figure 9.

152

attributes need not be processed. The details are discussed in (Wu, 2005). Figure 9 shows a rule. In the above rule, the abbreviations in the “if part” specify the key personality characteristic attributes with values (values are specified within the parenthesis). The “then part” indicates that the learner prefers metacognitive strategy (because the value for mcs is 3) and can achieve good performance. The rudimentary definition of metacognition strategy is that learners are aware of monitoring and controlling their learning, and planning and regulating before, during and after learning (Adkins, 2007). According to the theory of instruction design, applying a metacognitive strategy focuses on equipping learners with the appropriate navigation tools and not leaving the learner adrift in a sea of content. Generative summaries and generic self-monitoring, etc. are typical examples to maximize metacognitive strategy transfer (Adkins, 2007; Gogoulou, 2005). Figure 10 shows the presentation of a generative summaries example and figure 11 shows the presentation of a self-monitoring example.

A Method of Building Learner Model in Personalized E-Learning

Figure 10. Summary example

Figure 11. Self-monitoring example

This paper investigates the problem of key personality attributes that influence the personalized learning strategies in an e-learning environment and proposes an algorithm to effectively identify key personality attributes. The algorithm is based on rough set theory and does not require any prior knowledge. An extensive experiment has been conducted in a major Chinese research university to validate the method. Based on the above, learner interest model can be obtained. Figure 12 shows two learners’ models. The blue column is for learner one and

the purple column for learner two. It is clear that learner one has the most interesting in reading, while learner two has the most interesting in listening. Learner one has higher interesting in oral English than learner two. Both learners have similar interesting in vocabulary. According to learning model, a personalized e-learning can push the personalized materials for each learner. The algorithm’s categoricalness is not taken into account in the paper. More research in the problem will be developed in the future. The results of the

Figure 12. Learner interest model of two learners

153

A Method of Building Learner Model in Personalized E-Learning

paper will be used to guide the construction of a personalized e-learning system. The association rules, between personality characteristics and learning strategies, need to be assessed and adjusted to improve accuracy. Another important research problem is that more new criterions should been used for evaluating the quality of learner model.

Acknowledgment The research was supported by the National High-Tech R&D Program of China under Grant No.2008AA01Z131, the National Science Foundation of China under Grant Nos.60825202, 60803079, 60633020, the National Key Technologies R&D Program of China under Grant Nos. 2006BAK11B02, 2006BAJ07B06, 2008BAH26B02, the Open Project Program of the Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences under Grant No. 20080101.

reFerences Adkins, J. (2007). Metacognition: Designing For Transfer. Retrieved Feb. 25, 2008, from http:// www.usask.ca/education/coursework/802papers/ Adkins/ADKINS.PDF. Berkovsky, S., Kuflik, T., & Ricci, F. (2008). Cross-representation mediation of user models. User Modeling and User-Adapted Interaction, On-line First. Brusilovsky, P. (2001). Adaptive Hypermedia. User Modeling and User-Adapted Interaction, 11, 87–110. doi:10.1023/A:1011143116306 Busato, V. V., Prins, F. J., Elshout, J. J., & Hamaker, C. (1998). The relation between learning styles, the big five personality traits and achievement motivation in higher education. Personality and Individual Differences, 26, 129–140. doi:10.1016/ S0191-8869(98)00112-3 154

Canales, A., Peña, A., Peredo, R., Sossa, H., & Gutiérrez, A. (2007). Adaptive and intelligent web based education system: Towards an integral architecture and framework. Expert Systems with Applications, 33, 1080–1083. doi:10.1016/j. eswa.2006.08.034 Carson, J. G., & Longhini, A. (2002). Focusing on learning styles and strategies: A diary study in an immersion setting. Language Learning, 52, 401–438. doi:10.1111/0023-8333.00188 Cattell, R. B., Eber, H. W., & Tatsuoka, M. M. (1970). Handbook for the sixteen personality factor questionnaire (16 PF). Institute for Personality and Ability Testing. Chamot, A. U. (2005). Language learning strategy instruction: Current issues and research. Annual Review of Applied Linguistics, 25, 112–130. doi:10.1017/S0267190505000061 Chang, L., Wang, G., & Wu, Y. (1999). An approach for attribute reduction and rule generation based on Rough set theory. Journal of Software, 10(11), 1207–1211. Cohen, A., & Dörnyei, Z. (2002). Focus on the language learner: motivation, styles, and strategies. In N. Schmitt (Ed.), An introduction to applied linguistics (pp. 170-190). London: Arnold. Cohen. A. (1998). Strategies in learning and using a second language. London: Longman. Ehrman, M. E., Leaver, B. L., & Oxford, R. L. (2003). A brief overview of individual differences in second language learning. System, 31, 313–330. doi:10.1016/S0346-251X(03)00045-9 Entwistle, N., & Waterston, S. (1998). Approaches to studying and levels of processing in university students. The British Journal of Educational Psychology, 58. Eysenck, H. J., & Eysenck, S. B. G. (1975). Manual of the eysenck personality questionnaire. London: Hodder & Stoughton.

A Method of Building Learner Model in Personalized E-Learning

Eysenck, S. B. G., Eysenck, H. J., & Barrett, P. (1985). A revised version of the psychoticism scale. Personality and Individual Differences, 6, 21–29. doi:10.1016/0191-8869(85)90026-1

Kim, H., & Chan, K. (2008). Learning implicit user interest hierarchy for context in personalization. Applied Intelligence, 28, 153–166. doi:10.1007/ s10489-007-0056-0

Famous models Cattell 16PF profile. (n.d.). Retrieved Feb. 26, 2008, from http://www.chimaeraconsulting.com/16pf.htm.

Kobsa, A., Koenemann, J., & Pohl, W. (2001). Personalised hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review (pp. 111-155). Cambridge University Press.

Fehringer, H. M. (2004). Contributions and limitations of Cattell’s sixteen personality factor model. Retrieved Feb. 26, 2008, from http://www. personalityresearch.org/papers/fehringer.html. Ford, N., & Chen, S. Y. (2000). Individual differences, hypermedia navigation and learning: an empirical study . Journal of Educational Multimedia and Hypermedia, (9): 281–311. Gogoulou, A., Gouli, E., Grigoriadou, E., & Samarakou, M. (2005). ACT: A Web-based adaptive communication tool. In Proceedings of Computer Supported Collaborative Learning 2005: The Next 10 Years! Mahwah, NJ: Lawrence Erlbaum Assoc. Gough, H. G. (1975). Manual for the CPI, California Psychological Inventory. Consulting Psychologists Press. Guo, Y., Bai, Sh., Yang, Zh., & Zhang, K. (2005). Analyzing scale of web logs and mining users’ interests. Chinese Journal of Computers, 28(9), 1483–1496. Hand, D. J. (1999). Statistics and data mining: intersecting disciplines. ACM SIGKDD Explorations, 1, 16–19. doi:10.1145/846170.846171 Hathaway, S. R. (1951). Minnesota multiphasic personality inventory: manual. Psychological Corp. Hathaway, S.R., & McKinley J.C. (1967). Minnesota multiphasic personality inventory. fas. umontreal.ca.

Krysiński, J., Skzypczak, A., Demski, G., & Predki, B. (2001). Application of the rough set theory in structure activity relationships of antielectrostatic imidazolium compounds. Quantitative Structure-Activity Relationships, 20, 395–401. doi:10.1002/15213838(200112)20:5/63.0.CO;2-# Liu, D., & Huang, X. (2002). The introduction of learning research . Education Research, 2, 81. Liu, J., Li, R., & Zheng, Q. (2004). Research of personality mining in e-learning. Xi’an Jiaotong University Journal, 38, 575–578. Liu, R., & Dai, M. (2003). Research of Chinese college foreign language instruction reform situation and development. Beijing: Foreign Language Education and Research Press, China. Myers, I. B., McCaulley, M. H., & Most, R. (1985). Manual: a guide to the development and use of the Myers-Briggs type indicator. Consulting Psychologists Press. Nguyen, H. S. (1998). Discretization problem for Rough Sets methods. In L. Polkowski & A. Skowron (Ed.) (LNAI, pp. 545-552). Berlin: Springer-Verlag. Oxford, R. L. (2001). Language learning styles and strategies. In M. Celce-Murcia (Ed.), Teaching English as a second or foreign language (3rd ed., pp. 359-366). Boston: Heinle & Heinle/Thompson International.

155

A Method of Building Learner Model in Personalized E-Learning

Papanikolaou, K. A., Grigoriadou, M., Kornilakis, H., & Magoulas, G. D. (2003). Personalizing the interaction in a Web-based educational hypermedia system: the case of INSPIRE. User Modeling and User-Adapted Interaction, 13, 213–267. doi:10.1023/A:1024746731130

Skowron, A., & Rauszer, C. (1992). The discernibility matrices and functions in information systems. In R. Slowinski (Ed.), Intelligent Decision Support –Handbook of Applications and Advances of the Rough Sets Theory (pp. 331-362). Kluwer Academic Publishers.

Pawlak, Z. (1997). Rough set approach to knowledge-base decision support. European Journal of Operational Research, 99, 48–57. doi:10.1016/ S0377-2217(96)00382-7

Tan, Q., Li, X., & Shi, Zh. (2002). A method of providing individual service in search engine. Computer Science, 29, 23–25.

Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics and Systems, 29, 661–688. doi:10.1080/019697298125470 Pawlak, Z. (2002). Rough sets, decision algorithms and Bayes’ theorem . European Journal of Operational Research, 136, 181–189. doi:10.1016/ S0377-2217(01)00029-7 Pawlak, Z., Grzymala-Busse, J. W., Slowiński, R., & Ziarko, W. (1995). Rough sets. Communications of the ACM, 38, 88–95. doi:10.1145/219717.219791 Poosala, V., & Ioannidis, Y. (1997). Selectivity estimation without the attribute value independence assumption. International Conference on Very Large Data Bases (pp. 486-495). Athens, Greece. Reigeluth, C. M. (1997a). Educational standards: to standardize or to customize learning? Phi Delta Kappan, 79, 202–206. Reigeluth, C. M. (1997b). Instructional theory, practitioner needs, and new directions: some reflections. Educational Technology, 37, 42–47. Reigeluth, C. M. (1999). What is instructional design theory and how is it changing? In C. M. Reigeluth (Ed.), Instructional design theories and models: A new paradigm of instructional theory (Vol. 2, pp. 5-29). Hillsdale, NJ: Lawrence Erlbaum Associates.

156

Trajkova, J., & Gauch, S. (2004). Improving ontology-based user profiles. In Proceedings of the RIAO, Vaucluse, France (pp. 380-389). Vermetten, J., Lodewijks, G., & Vermunt, D. (2001). The role of personality traits and goal orientations in strategy use. Contemporary Educational Psychology, 26, 150–152. doi:10.1006/ ceps.1999.1042 Wen, Q., & Wang, L. (2004). Effects of various factors on L2 learning strategies: a review. Journal of Foreign Language and Teaching. Wenden, A. (1991). Learner strategies for learner automomy. New York: Prentice Hall. Wu, X. (2005). Study on dependency relation between learners’ characteristics and learning strategies in network learning. Unpublished master’s thesis, Xi’an Jiaotong University, Xi’an, Shaanxi, P.R.China. Yang, H., & Wang, L. (2003). An algorithm for extraction individual character of learner in the personalized learning system based on network environment. Computer Engineering and Application, 25, 179. Zhang, W., Wu, W., Liang, J., & Li, D. (2001). Theory and approach of Rough set. Beijing: Science Press, China. Ziarko, W. (2000). Rough sets: trends, challenges, and prospects. In W. Ziarko & Y. Yao (Eds.), RSCTC 2000 (pp.1-7). Berlin: Springer.

A Method of Building Learner Model in Personalized E-Learning

AppendIx Details of step 2, in the algorithm section, are given as follows.

1) For (i = N; i > 0; i--) //N is the number of learner subjects in U 2) { For (j = i-1; j >0; j--) { if f(i,d)≠f(j,d) // d∈D , refer to definition 2, f(i,d) is //described in definition 3. { for (k = 1; k 1 { nocore=nocore⋃attr;} // the attribute in attr //is not core. if flag = 1 { core=core⋃attr ; //the attribute in attr is already in the core. if (attr⋂nocore≠⊘ ) nocore=nocore\attr ; } flag=0; attr=⊘ ; } }

158

159

Chapter 9

OrPAF:

an Environment for Adaptive Hypermedia Courses in the Semantic Web Context Amel Yessad Université Badji Mokhtar, Algérie Catherine Faron-Zucker Université de Nice Sophia, France Peter Sander Université de Nice Sophia, France Med Tayeb Laskri Université Badji Mokhtar, Algérie

AbstrAct Adaptive learning support for learners becomes very important in the context of increasing re-use of resources from heterogeneous and distributed learning repositories. This chapter presents OrPAF, an Adaptive Educational Hypermedia (AEHS) and web-based System which integrates semantic web models and technologies in order to achieve interoperability with e-learning systems. The key feature of OrPAF is the construction of adaptive hypermedia courses: both the course structure and the course content are dynamically generated and adapted to learners. On the one hand, a learning ontology is proposed to describe, at a meta-level, abstract characteristics of an e-learning system. This learning ontology is instantiated to construct learning models: domain model, learner model and pedagogical model. On the other hand, semantic annotations and a semantic relevance measure are proposed to improve the LOM metadata associated to learning resources in order to reuse and share them. The authors tested the prototype on learners in order to evaluate the usability of OrPAF and to determine the conceptual capabilities developed by learners who used it.

IntroductIon A subject of much research interest is in personalizing learning supports in order to reuse and share DOI: 10.4018/978-1-60566-938-0.ch009

learning resources from distributed repositories (Nejdl, Wolf, Qu, Decker, Sintek, Naeve, Nilsson, Palmer, & Risch, 2002; Miklos, Neumann, Zdun, & Sintek, 2003; Dolog, Henze, Nejdl, & Sintek, 2004). The personalization of existing learning resources can be a solution to the problem of developing

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

OrPAF

online courses. However, personalized learning using distributed metadata of learning resources is still an unsolved problem in the e-Learning research area. Considering the increasing re-use of learning resources from the Web it becomes almost impossible for the learners, experts and course instructors to get an overview of all the available information relevant to their current needs, tasks, roles and goals. And even if they find some materials, which seem suitable, they are not able to assess completely whether the content is entirely appropriate for their goals or current knowledge and cognitive state. For that reason, learning resources retrieved from web repositories must be first subject to a pedagogy engineering work in order to render them reusable in the context of a specific training for specific learners. This engineering work is time and effort consuming in the design step of an e-learning system. To solve this problem, we propose an approach that moves part of this engineering effort from the course instructor/ expert to the software system and that delivers an adaptive hypermedia course directly to learners. In this context we aim to offer personalized course support which generates dynamically, for each learner, an individualized course structure and individualized course content by selecting the most optimal learning topics (e.g., the topic Function in the Algorithmics and Programming Languages domain) and the most relevant learning web resources (e.g., the definition of the topic Function) at any moment. Optimal learning topics and associated relevant learning resources are selected to bring the learner closest to his/ her ultimate learning goal. This approach is well suited for individual and autonomous learners taking a self-study distance-learning course. They can be employees in an organization who have various degrees of experience and background knowledge and where employees evolve in a competitive economic environment and requiring lifelong learning.

160

We propose a learning environment which generates adaptive hypermedia courses and reuses learning resources from distant web repositories, called “Organisateur de Parcours Adaptatifs de Formation” (OrPAF-“Personalized Learning Path Organizer”) (Yessad, Faron, Dieng, & Laskri, 2008). Queried learning resources are already annotated with LOM metadata but are very difficult to reuse automatically because of the semantic lack of LOM metadata. Our work is based on semantic web models, particularly ontologies and semantic annotations, in order to improve the quality of LOM metadata and describe in a standardized way several characteristics of e-learning system (i.e., learning resource, pedagogical strategy, learner model and domain model). The semantic Web for E-Learning (SW-EL) field has shown the greatest activity in this trend with several interesting and recurring practices (Dolog & Nejdl, 2003; Aroyo & Dicheva, 2004; Yessad & Laskri, 2006). Our aim is to improve learning process efficiency (1) by providing the learner with adaptive learning paths according to his/her level of knowledge, learning goal and time constraints; and (2) by reusing learning resources of different web repositories. On the one hand, an adaptive learning path is constructed on the basis of the conceptual structure of the domain model (e.g., the Algorithmics and Programming Languages domain) and the learner model (e.g., beginner). In the learning environment, the learner is assisted to construct a “correct” representation of a particular domain of knowledge and the learning is self regulated (Pintrich & Schunk, 2002; Perry, Phillips, & Hutchinson, 2004). For this purpose, we apply filters on the domain model to generate a map of relevant learning topics. We call this map an Adaptive Conceptual Map (ACM). An ACM is automatically generated and displayed to a learner, which takes into account a specific goal, the knowledge and temporal constraints of the learner. An ACM represents an adapted view of the structure of the hypermedia course. On the other hand, learning resources that are queried

OrPAF

from distant web repositories like ARIADNE or created locally by domain experts are annotated by adding a conceptual layer onto the LOM metadata description layer. We propose a measure to evaluate the semantic relevance of annotated learning resources in order to associate them to the generated ACM and to recommend them to the learner. This chapter is organized as follows: the next section presents an overview and the positioning of our learning environment approach. Then, we show how we represent e-learning knowledge in a meta-model and how we use it to construct different e-learning models. After that, we explain the generation of an adaptive structure and adaptive content of the hypermedia course. The last section is dedicated the implementation of OrPAF and our evaluation protocol.

posItIonIng Representative examples of personalized support for learners are adaptive textbooks constructed with AHA! (De Bra, Aerts, Berden, de Lange, Rousseau, Santic, Smits, & Stash, 2003), InterBook (Brusilovsky, Eklund, & Schwarz, 1998) and Net-Coach (Weber, Kuhl, & Weibelzahl, 2001), or adaptive courses within ELM-ART (Brusilovsky, Schwartz, & Weber, 1996), PAT (Ritter, 1997) and AIMS (Aroyo & Dicheva, 2001). There are also more global but still highly specialized efforts, such as ARIADNE and EdNa courseware-reusability frameworks that provide repositories of reusable learning resources. In this context, our research aims to propose a learning environment which is an adaptive educational hypermedia and web-based system (AEHS). Our learning environment integrates semantic web models and technologies like ontologies, semantic annotation and learning standards in order to achieve interoperability with e-learning systems and then to improve reuse of its components.

Similarly to the Dynamic Course Generation system (Brusilovsky & Vassileva, 2003) and the research of (Ullrich, 2004) and (Dehors, FaronZucker, & Dieng-Kuntz, 2006), the core of our learning environment is the explicit representation of the domain model, separated from learning resources and pedagogical strategies. We define a learning ontology that describes characteristics of e-learning systems (e.g., learner, pedagogical activity). This learning ontology is a meta-model which describes abstract learning characteristic independent of a specific learner, a specific domain (e.g., the Algorithmics and Programming Languages domain), a specific pedagogical strategy (e.g., the deductive strategy) or a specific learning resource (e.g., Slides created by JamesRumbaugh introduce the Object Modelling notion). The meta-model is instantiated to construct specific learning models: the domain model, the learner model and the pedagogical model. Contrary to the learning ontology, these models describe respectively learning topics of a specific domain (e.g., Arithmetic Operators in the Algorithmics and Programming Languages domain), a specific learner and a specific pedagogical strategy (e.g., inductive strategy). In contrast with other approaches mentioned above, the addition of learning characteristics to the meta-model makes it easier to extend our learning system. In authoring tools like InterBook (Brusilovsky et al., 1998), MetaLinks (Murray, 2001), and NetCoach (Weber et al., 2001), the expert explicitly provides the course structure as a textbook hierarchy (like page1 has subsection page1-1) whereas, in our approach, the structure of the course is an Adaptive Conceptual Map (ACM), a graph of domain topics. In fact, we use the structure of the domain model as a roadmap to generate course paths. Given a certain goal topic (e.g., Arithmetic Operator in the Algebra Domain) that the learner wants to acquire and given his/her learner model, our learning environment generates a map of learning topics to the learner in order to achieve his/her goal.

161

OrPAF

Moreover, our learning environment implements a query component. This component uses a semantic measure to detect the relevance of learning resources by computing the similarity between the ACM and the resource annotations and thus the semantic measure depends on the learning context of the learner. Much research proposes to querying resources from the web by relaxing the topics of the query to other topics close to them in the domain model. The degree of closeness of topics is determined using similarity measures. We can consider that there are two big families of approaches to compute the semantic similarity. Some approaches use only the subsumption relationship to compute the semantic similarity (Corby, Dieng-Kuntz, & Faron-Zucker, 2004; Rada, Mili, Bicknell, & Blettner, 1989; Wu & Palmer, 1994; others include more information besides the subsumption relationship. This information can be statistics about the use of concepts in a corpus (Resnik, 1995; Jiang & Conrath, 1997). In particular, the research of (Jiang et al., 1997; Lin, 1998) introduces the notion of conditional probability of encountering an instance of a childconcept given an instance of a parent-concept. Among the measures that are based on the context there is the approach of (Torniai, Jovanovic, Gasevic, Batemen & Hatala, 2008) which propose a weight-based measure to contextualize semantic relatedness measures in order to leverage folksonomies for domain ontology evolution. Similarly to previous research, this measure is based only on the concept hierarchy. Similarly to our approach, the research of (Zhong, Zhu, Li, & Yu, 2002) defines a similarity measure between conceptual graphs: a query graph and a resource graph. However, the originality of our approach stands in the use of a relative weightbased relevance measure where the weights of the topics depend on the learning context and are not fixed in advance.

162

leArnIng enVIronment knowledge Because of the increasing complexity and heterogeneity of knowledge in e-learning systems (e.g., domain knowledge, learner knowledge, pedagogical knowledge), we require an efficient and modular knowledge organization. We represent our learning environment knowledge in two levels: meta-model level and model level. The meta-model knowledge is used to construct learning models which are used by functional components of the learning environment: Querying resource components, annotation component, testing component, and course generation component (cf. figure 1).

learning ontology The learning ontology we developed is the metamodel and the backbone of the learning process. It describes concepts and properties that are instantiated in order to specify the domain of interest (e.g., Algorithmics and Programming Languages), profiles of the learners (e.g., beginner), pedagogical strategies (e.g. deductive strategy) and annotations of learning resources (e.g., learning resource role). Formally, we define a learning ontology LO as follows: LO = (C, P, H c, Hp, Signature, Rules), where: •

•

•

C and P are two disjoint sets, whose elements are called respectively concepts and properties. Each of these elements is identified with a URI (Unique Resource Identifier). Hc is a concept hierarchy, which is an acyclic directed graph Hc ⊆ C×C. Hc(c1, c2) means that c1 is a sub-concept of c2. Hp is the property hierarchy, which is an acyclic directed graph Hp ⊆ P×P. Hp(p1,p2) means that p1 is a sub-property of p2.

OrPAF

Figure 1. Architecture of the OrPAF system Learning Resource Metadata Local corpus Pedagogical Model

e-Learning Ontology (Meta-Model Level)

AdaptationContent AnnotationResource

Semantic Search Engine CORESE

QueryingLocalResource

•

ARIADNE KPS

AdaptationCourse

Domain Model

•

Expert

QueryingDistantResource

Learner Model

Test Metadata

Apprenants

Apprenants

Learners Test/Session Database

Signature: P→ C×C is a function which relates concepts and defines the signature of any property p. The function domain: P→C with domain(p)=∏1(Signature(p)) gives the domain of p and the function range(P)=∏2(Signature(P)) gives its range. Rules is a set of rules on C and P.

Concepts and properties are seen as general objects. So, the learning ontology (meta-model) is instantiated to construct three learning models: a domain model, a learner model and a pedagogical model. Contrary to the learning ontology, these models describe specific objects. For instance, in the learning ontology, we describe types of learning topics of a domain (e.g., a medium topic) and relationships between these types (e.g., prerequisite relationship) whereas in the domain model we describe concrete learning topics and their relationships. For instance, the topic Operator and the topic Statement are instances of the concept LearningTopic defined in the meta-model (cf. figure 2); and in the domain model, the topic Operator is related to the topic Statement by the relationship prerequisiteOf (cf.

Adaptive Conceptual Map

figure 3). The concepts of the learning ontology are hierarchically organized with one general and abstract concept called an ELearningConcept. The ELearningConcept has three direct subconcepts: LearningTopic, LearningPerson and LearningPedagogy which are used to construct respectively the domain model, the learner model and the pedagogical model. For example, all learning topics of the domain model are instances of sub-concepts of the concept LearningTopic. Similarly, properties of the learning ontology are hierarchically organized with one general and abstract property called eLearningProperty. The property eLearningProperty is specialized in three sub-properties: learningTopicProperty, learnerProperty and pedagogicalProperty. The set Rules is a set of rules and concerns the concepts and the properties of the learning ontology. For instance, we can have a rule which describes that the property prerequisiteOf is not a symmetric relation or that the property subTypeOf is a transitive relation. The concepts and the properties of the learning ontology are described in the OWL language (http://www.w3.org/TR/owl-features/). For example the concepts Learner and Author are

163

OrPAF

Figure 2. An excerpt of learning ontology

described in the learning ontology as sub-concepts of the concept LearningPerson and are used in the system OrPAF to define the learners and the learning resource authors: For example the property preferedAuthor connects the concept Learner to the concept Author and signifies that a learner prefers an author.

164



learning models The learning ontology is instantiated into three learning models: the domain model, the learner model and the pedagogical model. Formally, we define a learning model LM as follows: LM = (LO, I, InstC, InstP), where: • • • •

LO=(C, P, Hc, Hp, Signature) is the learning ontology. I is a set of instances (C, P and I are disjoint sets). InstC: C→ 2I is a function which defines the instances of concepts in C. InstP: P→2I×I is a function which defines the instances of properties in P.

Domain Model The domain model represents the domain of interest where the learner evolves. A specific domain of interest (e.g., Algorithmics and Programming Languages) is described by learning topics and their mutual relationships in a specific discipline. In figure 3, we show a fragment of the domain knowledge covering learning topics of the Algo-

OrPAF

Figure 3. An excerpt of the domain model Algorithmics and Programming Languages

rithmics and Programming Languages domain, including the subTypeOf and the prerequisiteOf relationships between learning topics. We build an Adaptive Conceptual Map (ACM) by filtering the domain model. An ACM is a sub-graph of the whole graph of related learning topics. It is used by a learner to construct his/ her cognitive representation of the domain of interest in order to achieve his/her learning goal. Learning topics related with an order relationship (e.g., prerequisiteOf) must be taught following a certain order. The learning topics of the domain « Algorithmic and programming languages » are described by RDF triplets (http://www.w3.org/TR/REC-rdfsyntax/). For example the topic ComplexStatement is an instance of the concept MediumTopic and a specialization of the topic Statement. The topic Operator is a prerequisite of the topic ComplexStatement.



<MediumTopic rdf:ID=”ComplexStatement”> <MediumTopic rdf:ID=”Operator”/>

pedagogical model

Learner Model The learner model captures knowledge and preferences of the learner. It represents what the system knows about the learner. In our learning environment, we represent the knowledge of the learner by the overlay model (Galeev, Tararina, & Kolosov, 2004). For instance, as shown in figure 4, if the learning topic Operator is mastered by the learner Rose, the knowledge occurs in the learner model, else the topic Operator is unknown by the learner Rose. The learner model changes during the learning process when the learner passes tests. In this way, our learning environment provides mechanism for self regulated learning (Pintrich & Schunk, 2002; Perry et al., 2004).

The structure of the domain model alone is not sufficient to decide how to present the selected learning topic to the learner, i.e. what pedagogical type of learning resources to select, or how to sequence several learning resources to teach a given learning topic. For this purpose, we define

165

OrPAF

Figure 4. An excerpt of a learner model

a pedagogical model which describes pedagogical strategies to teach learning topics. It describes different pedagogical activities (e.g., Exercise, Lecture) and their relationships. For instance, as shown in figure 5, the Definition activity (instance of the concept PedagogicalActivity in the meta-model) must precede the Exercise activity; both activities are related by the sequencingStrategy property which is defined in the meta-model. The alternativeStrategy relationship between pedagogical activities means that learning resources related to these pedagogical activities can be accessed by a learner in any order whereas the sequencingStrategy relationship requires an order in the presentation of learning resources to the learner. Figure 5. An excerpt of the pedagogical model

166

AdAptIVe course generAtIon The learning environment aims to construct a hypermedia course as a combination of an adaptive course structure and adaptive course content. The course structure is represented by an Adaptive Conceptual Map and the course content is a set of reused relevant learning resources searched from external learning repositories.

Adaptive conceptual map (Acm) An ACM is a course structure generated and displayed by the learning environment in order to help the learner to construct a “correct” mental

OrPAF

representation of the learning domain. It is a subgraph of the domain model and contains learning topics that the learner must learn to achieve his goal in required time. An ACM enables the learner to navigate between the learning topics of the course. We distinguish between three conceptual maps constructed on the goal topic G of the domain model (DM): simple Ms(G), hierarchical Mh(G) and relational Mr,m(G). Each one of these maps results from the application of a specific filter on the domain model and only the topics which pass the filter are displayed to the learner. Simple Ms(G) is the smallest map. It is composed of the goal topic and all topics related to it directly or by transitive closure of order relationships. M s (G) = {G} ∪ {t∈InstC (DM) / ∃ p∈orderRelationship pt(t, G)} Hierarchical Mh(G) is the conceptual map that extends the simple conceptual map to descendants and ascendants of the goal topic. Mh(G) = Ms(F), where F = {G} ∪{c∈ InstC (DM) / subTypeOf (G, c) ∨ subTypeOf (c, G)} • Relational Mr,m(G) is the conceptual map that extends the simple conceptual map to all topics related to the goal topic by a path of relationships, the length of this path being less than m. Mr,m(G) = Ms(F), where F = {G} ∪{t∈ InstC (DM) / ∀p∈ plearningTopicProperty (pt(G, t) ∨ pt (t, G)) ∧ length (G, t)≤m} •

OrPAF implements each of these three filters. For the same topic goal, the filter depends on learner temporal constraints: the simple filter for learners with hard temporal constraints, the hierarchical filter for learners with medium temporal constraints and the extended filter for learners with flexible or no temporal constraints. This approach can be compared to the micro, the meso and the macro learning approaches (Hug, 2005). Before displaying the ACM to a learner, an additional adaptation layer is applied on it. It consists

of applying rules in order to annotate each learning topic similarly to link annotation technique in adaptive Hypermedia (De Bra, Brusilovsky, & Houben, 1999). Different icons represent different states of a learning topic (cf. figure 6). A learning topic is accessible if all its prerequisites are mastered by the learner. Elsewhere, the learning topic is not accessible and no learning resources are attached to it. A topic without prerequisites is always accessible. In our environment, we distinguish also between a mastered topic and not yet mastered topic. Graphical icons are used to represent the difference between these three learning topic states. For instance, figure 6 presents an ACM where the learning topic Operator is mastered, the learning topic Procedure is not mastered and the learning topic SemanticLanguage is not accessible. The state of a learning topic can change from inaccessible to accessible if its prerequisites become mastered. Also, it can change from non mastered to mastered. These alterations in the ACM result from updating the learner model.

Adaptive course content In our work, learning resources are files with different formats (.pdf, .doc, .html, etc.) queried from web repositories. In our prototype, we use the ARIADNE Knowledge Pool System (ARIADNE KPS), a distributed database of learning resources annotated with LOM metadata elements. In the learning environment, we propose: (1) A conceptual annotation process for annotating learning resources and (2) a query component for querying learning resources from the local repository.

Annotating Learning Resources Once a distant learning resource is downloaded, it is submitted to a semi-automatic annotation process assisted by a teacher/expert; and finally the learning resource is stored in a local repository. Conceptual annotations of the learning resource

167

OrPAF

are constructed by instantiating some concepts and properties from both the domain model and the pedagogical model. For instance, in figure 7, the learning resource R1 is an Exercise (defined as PedagogicalActivity in the pedagogical model) and teaches the learning topic ComposedStatement (defined as a LearningTopic in the domain model). Characteristics (e.g., learningResourceTopic, learningResourceRole, learningResourceAuthor) of the resource are manually identified by experts and annotations are automatically generated by the system according to learning models. These conceptual annotations are then added to learning resource metadata (in our case, the RDF-LOM binding metadata).

Querying a Local Learning Resource The learner can consult resources about accessible learning topics in their ACM by a simple click on it. The querying component searches for relevant learning resources from the local repository. It computes the relevance of a learning resource by matching its conceptual annotations with the ACM. We propose an approach for computing the semantic relevance of a learning resource in a particular learning context. In our case, the learning context is composed of the current learning topic Figure 6. A screenshot of a course structure

168

(the topic clicked by the learner) and the state of the ACM. Only learning topics of conceptual annotations are used in the calculus of the semantic relevance. Our calculus relies on the assignment of a relative weight to each learning topic related to the learning resource. These relative weights depend on the current topic in the ACM and the type of relationships that connect the current topic to topics related to the learning resource. Let P be a learning path of length n and composed of topics ti . Let wti/t1 (i>1) be the weight of topic ti relative to the current t1 topic in the ACM. Let wt1 be the weight of the current topic t1. We define the relative weight as follows: Wt1/t1= N Wt2/t1= 1/a Wti/t1= (1/a) Wti-1/t1, i>2 Where “a” is a variable whose value is as follows: a=2 if the relationship between ti-1 and ti is subTypeOf or its inverse relationship a=3 if the relationship between ti-1 and ti is prerequisiteOf or its inverse relationship

OrPAF

Figure 7. An excerpt of the conceptual annotation of a learning resource R1

a=5 if the relationship between ti-1 and ti is aggregationOf or its inverse relationship N is the weight of the current concept and chosen as a big number. Each time that we have a new relationship in the domain model we add a new value to the variable “a”. The inverse value of “a” represents the weight of the relationship connectig two concepts in the domain model. This weight represents the relation strength between neaby concepts. The correspondence between the values of the factor “a” and the relationships were empirically defined by experts. For instance, in the domain of algebra, we conducted an experiment where domain experts were requested to position topics on the screen according to the distance that they estimated between them. We noticed, for instance, that most experts positioned topics related with the subTypeOf relation closer to each other than topics related with the prerequisiteOf relation. However, in other domains the result may be different. When there are several relative weights for one learning topic (due to graph cycles) we take the smallest value. Once defined the relative weight of each learning topic related to the learning resource, the semantic relevance SR of the learning resource can be measured as follows:

Let E be the set of topics both present in the learning resource annotation and the accessible topics of the ACM, let F be the set of topics present in the learning resource annotation and not present in the accessible topics of the ACM, and let t be the current concept of the ACM. SR=1

x E

wx /t

y F

wy /t

The definition of SR reflects the fact that the weight of a topic depends on the current topic and the state of the ACM and therefore of the learning context. A resource is relevant if its learning topics have important relative weights and are largely similar to the accessible learning topics of the ACM. Otherwise, the resource is less or not relevant.

ImplementAtIon And eVAluAtIon Implementation The implemented prototype of the learning environment integrates several functions to fulfill requirements for the generation of adaptive

169

OrPAF

courses. We used the JAVA language for implementing all user interfaces and functional components of OrPAF. The interoperability between the implemented prototype and ARIADNE KPS is implemented by a java API named KPS client package. The learning ontology was described in OWL Lite language and learning models were described in the RDF language. OrPAF uses Corese, an ontology-based search engine for the semantic web, dedicated to the query of RDF annotations by using the SPARQL query language (Corby, Dieng-Kuntz, Faron-Zucker, & Gandon, 2006). Corese is used to extract concepts and properties of learning models and conceptual annotations. OrPAF prototype was used to teach learning topics of Algorithmics and programming languages on a group of thirty learners in mathematics and computer sciences. Each learner has to give identification information (username and password) in order to access to OrPAF. Once logged in, he/she can: •

•

•

Open a new learning session by submitting his/her learning goal topic and his/her time constraints that define the filter type and therefore the size of the ACM. Save a current learning session (the ACM, the learner comments, the relevant learning resources, etc.). The learner sessions are stored in his/her learner model. Reload an interrupted session.

evaluation We tested the OrPAF prototype on a group of thirty learners who belong to three different levels of competence. We applied an accurate evaluation protocol: we divided the group of learners into two subgroups A and B. In the first step of the protocol, the learners of the group A used OrPAF whereas the learners of the group B used paper-support resources. In the second step of the protocol, the learners of the group B were invited to use OrPAF. After each step, an exercise

170

was performed by the learners. It aimed to detect the conceptualization capabilities of the learners before and after the use of OrPAF. The purpose of this evaluation protocol was to analyse behaviour /ratings of the learners according to three orthogonal directions: the usability of OrPAF, the conceptualization capabilities acquired by the learner and the learner ratings of the relevance of learning resources proposed by OrPAF. For each direction, learners were interviewed to get their feedback. We present in figure 8 a histogram which synthesizes the results of the evaluation. Each bar of this histogram represents the repartition of the thirty learners according to the OrPAF usability (the first bar), the results of the conceptualization exercises (the second and the third bars) and the learner ratings of the learning resource relevance (the fourth bar). In the first bar, the learners of the group B liked OrPAF more than those of group A - certainly because they found that the learning process with OrPAF is easier than the learning process with the paper-support resources. They found more assistance with OrPAF. After step1, the learners of group A obtained good results in the exercise of conceptualization whereas, the learners of group B had difficulties performing the exercise and obtained bad results (see the second bar). After step 2, the learners of group B obtained the best results in the exercise of the conceptualization (see the third bar). Most learners agreed that the learning resources which were presented to them are relevant (see the fourth bar). Therefore, we can conclude that the evaluation of the system OrPAF gives positive results according to the user interface usability, the development of conceptual capabilities of the learner and the relevance of the generated content.

OrPAF

Figure 8. Results of the experimentation of OrPAF on learners evaluation of orpAF

100% 90% 80% 70% Group B (negative judgment/results)

60%

Group A (negative judgment/results)

learners 50%

Group B (positive judgment/results)

40%

Group A (positive judgment/results)

30% 20% 10% 0%

OrPAF Usability

Conceptualization Capabilities (after step1)

Conceptualization Capabilities (after step2)

Resource Relevance

evaluation criterea

conclusIon This chapter presents OrPAF, a learning environment for the generation of adaptive hypermedia courses which enables self-regulated learning for learners with different profiles. OrPAF reuses learning resources from distant web repositories. A learning ontology is proposed to describe, at the meta-level, abstract characteristics of an e-learning system. This learning ontology is instantiated to construct a domain model, a learner model and a pedagogical model which describe concrete characteristics of e-learning systems. As a result, the learning environment is multi-domain and multi-profile-learner with minimum changes in the learning models in order to adapt them to new learner profiles and domains. In addition, the common description of all learning characteristics in the learning ontology improves the reusability of the external resources. Contrary to the learning systems, cited in section 2, the learning environment OrPAF provides personalization support for both course structure and course content; and due to the use of the semantic web technologies is an efficient yet lightweight support. OrPAF generates a hypermedia course as the combination of an adaptive structure and an adaptive content. On the one hand, the course structure is a map of relevant learning topics. This

map is generated by filtering the domain model and applying two layers of adaptation: (1) a first adaptation according to the learning goal and the temporal constraints of the learner; and (2) a second adaptation according to the knowledge of the learner by annotating the topics of the map. On the other hand, an adaptive content consists of relevant learning web resources. We propose a measure of the semantic relevance of a learning resource for a specific learning context. It is based on relative weights of learning topics related to the learning resource. These relative weights depend on the learning context. The experiments of the OrPAF prototype give positive results, both for the usability and the utility of the learning environment. In addition, we can conclude that topic-based course structure develops the conceptualization capabilities of the learners and their mental representation of domains.

reFerences Aroyo, L., & Dicheva, D. (2001). AIMS: Learning and teaching support for www-based education. International Journal of Continuing Engineering Education and Lifelong Learning, 11(1-2), 152–164. doi:10.1504/IJCEELL.2001.000390

171

OrPAF

Aroyo, L., & Dicheva, D. (2004). The new challenges for e-learning: The educational semantic web. Educational Technology & Society, 7(4), 59–69. Brusilovsky, P., Eklund, J., & Schwarz, E. (1998, April 14-18). Web-based education for all: A tool for developing adaptive courseware. In Proceedings of Seventh International World Wide Web Conference, Computer Networks and ISDN Systems, 30(1-7), 291-300. Brusilovsky, P., Schwartz, E., & Weber, G. (1996, June 12-14). ELM-ART: An intelligent tutoring system on the world wide web. In Proceedings of Third International Conference on Intelligent Tutoring Systems (ITS), Montreal, Canada. Brusilovsky, P., & Vassileva, J. (2003). Course sequencing techniques for large-scale web-based education. International Journal of Continuing Engineering Education and Lifelong Learning, 13(1-2), 75–94. Corby, O., Dieng-Kuntz, R., & Faron-Zucker, C. (2004, August 22-27). Querying the semantic web with the Corese search engine. In Proceedings of ECAI (pp. 705-709). Valencia, Spain: IOS Press. Corby, O., Dieng-Kuntz, R., Faron-Zucker, C., & Gandon, F. (2006). Searching the semantic web: Approximate query processing based on ontologies. IEEE Intelligent Systems Journal, 21(1), 20–27. doi:10.1109/MIS.2006.16 De Bra, P., Aerts, A., Berden, B., de Lange, B., Rousseau, B., Santic, T., et al. (2003, August 2630). AHA! The adaptive hypermedia architecture. In Proceedings of fourteenth ACM Conference on Hypertext and Hypermedia (pp. 81-84). Nottingham, United Kingdom: ACM Press. De Bra, P., Brusilovsky, P., & Houben, G.-J. (1999). Adaptive hypermedia: From systems to framework. ACM Computing Surveys, 31(4). doi:10.1145/345966.345996

172

Dehors, S., Faron-Zucker, C., & Dieng-Kuntz, R. (2006, July 5-7). Reusing learning resources based on semantic web technologies. In Proceedings of 6th IEEE International Conference on Advanced Learning Technologies, ICALT, Kerkrade, Netherlands. Dolog, P., Henze, N., Nejdl, W., & Sintek, M. (2004, May 17-22). Personalization in distributed e-learning environments. In Proceedings of Thirteenth International World Wide Web Conference (pp. 170-179). New York, USA. Dolog, P., & Nejdl, W. (2003) Challenges and benefits of the semantic web for user modelling. In Proceedings. of AH. Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems at World Wide Web, UserModelling and Hypertext Conference, (Budapest, Pittsburgh, Nottinngham). Galeev, I., Tararina, L., & Kolosov, O. (2004, August 30-September 1). Adaptation on the basis of the skills overlay model. In Proceedings of IEEE International Conference on Advanced Learning Technologies (pp. 648-650). Joensuu, Finland: IEEE Computer Society. Hug, T. (2005, May 6-8). Micro learning and narration. In Proceedings of fourth Media in Transition Conference. Cambridge, MA: MIT Miklos, Z., Neumann, G., Zdun, U., & Sintek, M. (2003, October 20-23). Querying semantic web resources using TRIPLE views. In Proceedings of the 2nd International Semantic Web Conference (ISWC), Sanibel Island, Florida, USA. Murray, T. (2001). Metalinks: authoring and affordances for conceptual and narrative flow in adaptive hyperbooks. The International Journal of Artificial Intelligence in Education . Special Issue on Adaptive and Intelligent Web-Based Systems, 13(II), 199–233.

OrPAF

Nejdl, W., Wolf, B., Qu, C., Decker, S., Sintek, M., Naeve, A., et al. (2002, May 7-11). EDUTELLA: a P2P networking infrastructure based on RDF. In Proceedings of 11th World Wide Web Conference, Hawaii, USA. Perry, N. E., Phillips, L., & Hutchinson, L. R. (2004). Preparing student teachers to support for self-regulated learning. The Elementary School Journal, 106(3), 237–254. doi:10.1086/501485 Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications (2nd ed.). Upper Saddle River, NJ: Merrill, Prentice Hall. Rada, R., Mili, H., Bicknell, E., & Blettner, M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics, 19(1), 17–30. doi:10.1109/21.24528 Resnik, P. (1995). Semantic similarity in a taxonomy: An information-based measure and its applications to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 11, 95–130. Ritter, S. (1997, August 18-22). PAT Online: A model-tracing tutor on the world-wide web. At the Workshop on Intelligent Educational Systems on the WWW, Kobe Japan. Torniai, C., Jovanovic, J., Gasevic, D., Batemen, S., & Hatala, M. (2008, August 4-7). Leveraging Folksonomies for Ontology Evolution in E-learning Environments. In Proceedings of Second IEEE International Conference on Semantic Computing. Santa Clara, CA, USA.

Ullrich, C. (2004, November 7-11). Description of an instructional ontology and its application in web services for education. SWEL Workshop, ISWC (pp. 17-23). Hiroshima, Japan. Weber, G., Kuhl, H.-C., & Weibelzahl, S. (2001). Developing adaptive internet based courses with the authoring system Netcoach. In Proceedings of third workshop on adaptive hypertext and hypermedia, UM, TU/e Computing Science Report 01/11. Wu, Z., & Palmer, M. (1994, June 27-30). Verb semantics and lexical selection. In thirty second Annual Meeting of the Associations for Computational Linguistics (pp. 133-138). New Mexico State University, Las Cruces, New Mexico, USA. Yessad, A., Faron-Zucker, C., Dieng-Kuntz, R., & Laskri, M. T. (2008). Adaptive Learning Organizer for Web-based Education. International Journal of Web-Based Learning and Teaching Technologies, 3(4), 57–73. Yessad, A., & Laskri, M. T. (2006, December 7-9). Ontology-driven dynamic course generation for web-based education. In Proceedings of MCSEAI, Agadir, Morocco. Zhong, J., Zhu, H., Li, J., & Yu, Y. (2002, July 15-19). Conceptual graph matching for semantic search. In Proceedings of 10th International Conference on Conceptual Structures, ICCS (LNCS 2393, pp. 92-106).

173

174

Chapter 10

E-Learning with Wikis, Weblogs and Discussion Forums: An Empirical Survey about the Past, the Presence and the Future Reinhard Bernsteiner University for Health Sciences, Austria Herwig Ostermann University for Health Sciences, Austria Roland Staudinger University for Health Sciences, Austria

AbstrAct This chapter explores how social software tools can offer support for innovative learning methods and instructional design in general and those related to self-organized learning in an academic context in particular. In the first section the theoretical basis for the integration of wikis, discussion forums and weblogs in the context of learning are discussed. The second part presents the results of an empirical survey conducted by the authors and explores the usage of typical social software tools which support learning from a student’s perspective. The chapter concludes that social software tools have the potential to be a fitting technology in a teaching and learning environment.

IntroductIon One major task of higher education is to train students for the requirements of their future work in order to apply and adapt their knowledge to specific workplace-related requirements and settings. Due to the ongoing pressure on enterprises to cut costs, the periods of vocational adjustment in a company will become shorter and shorter. DOI: 10.4018/978-1-60566-938-0.ch010

On the one hand the rising pressure of innovation and the fast-paced development in the economy results in increased demand for continuous employee training. On the other, growing global competition forces enterprises to use available resources very economically, so that employee training is considered to be necessary and desired even though it is conducted under considerable time and cost pressure (Köllinger, 2002). According to these goals, the settings of the education must be changed adequately. “While most

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

E-Learning with Wikis, Weblogs and Discussion Forums

of higher education still ascribes to traditional models of instruction and learning, the workplace is characterized by rapid changes and emergent demands that require individuals to learn and adapt in situ and on the job without the guidance of educational authorities“ (Sharma & Fiedler, 2004, p. 543). In the field of higher education, it has become an important goal to develop “digital literacy” and educate learners as competent users and participants in a knowledge based society (Kerres, 2007), but it can be assumed that there is a new generation of students, the “digital natives”, who are accustomed to the digital and internet technology (Prenksy, 2001). Oblinger and Oblinger (2005) characterise next generation students (called “n-gen”, for NetGeneration) as digitally literate, highly internet savvy, connected via networked media, used to immediate responses, preferring experiential learning, highly social, preferring to work in teams, craving interactivity in image rich environments and having a preference for structure rather than ambiguity. According to a study conducted by Lenhart and Madden (2005), half of all teens in the USA may be considered as “content creators” by using applications that provide easy-to-use templates to create personal web spaces. Classical face-to-face learning is seen as rigid and synchronous and it promotes one-way (teacher-to-student) communication. Thus it is not surprising that more and more students are opting for web-based education, as a more flexible and asynchronous mode (Aggarwal & Legon, 2006). The higher education system should provide answers to this new generation of students who enter the system with different background and skills. They are highly influenced by social networking experiences and able to create and publish on the internet (Resnick, 2002). Educators and teachers therefore have to consider the implications of these developments for the future design of their courses and lectures.

In 2002 a new term, “Social Software”, entered the stage to refer to a new generation of internet applications. One focus of this new generation is the collaboration of people in sharing information in new ways such as social networking sites, wikis, communication tools and folksonomies (Richter & Koch, 2007). Wikis, weblogs and discussion forums will play a central role in the new context so the areas of application and possibilities will enlarge enormously. It can be assumed that this will also have considerable influence on learning and the usage of these instruments as learning tools. The paper presents the results of an empirical survey in order to highlight the benefits of the above mentioned web-based social software tools from the student’s point of view. 268 firstsemester students, all in the first term of their studies) at Austrian Universities from different study programs took part in this survey. The students were asked to use one or more of these tools as a learning tool. The participation in this survey was voluntary. The presentation of the results of this survey is divided into three parts: first the usage of the tools by the students (before they started with their studies), secondly the experiences the students had made with the tools during the study and, thirdly, the potential future usage. The paper concludes with a discussion of the results of this survey in contrast with other empirical studies already published. Also the limitations of this survey and ideas for further research are pointed out.

tHeoretIcAl FrAmework This part refers to the necessary theoretical background required for the following empirical study. Especially the areas of “Social Software” and “Learning” are addressed.

175

E-Learning with Wikis, Weblogs and Discussion Forums

social software The term “social software” emerged and came into use in 2002 and is generally attributed to Clay Shirky. Shirky, a writer and teacher on the social implications of internet technology, defines social software simply as “software that supports group interaction” (Shirky, 2003). Another definition of social software can be found in Coates (Coates, 2005) who refers to social software as “Software that supports, extends, or derives added value from human social behaviour“. Users are no longer mere readers, audiences or consumers. They have the ability to become active producers of content. Users can act in user and producer positions and they can rapidly change the position. Nowadays the term “Social Software” is closely related to the “Web 2.0”. The term “Web 2.0” was introduced by Tim O’Reilly, who suggested the following definition: “Web 2.0 is the network as platform, spanning all connected devices; Web 2.0 applications are those that make the most of the intrinsic advantages of that platform: delivering software as a continually updated service that gets better the more people use it, consuming and remixing data from multiple sources, including individual users, while providing their own data and services in a form that allows remixing by others, creating network effects through an ‘architecture of participation,’ and going beyond the page metaphor of Web 1.0 to deliver rich user experiences” (O’Reilly, 2005). Web 2.0 technologies such as blogs, wikis, podcasts, and RSS feeds or discussion forums have been dubbed “Social Software” because they are perceived as being especially connected and allowing users to develop Web content collaboratively and publicly (Alexander, 2006). Until now the internet (Web 1.0) has one big disadvantage: it is easy to get information in it, but it is quite complicated and inconvenient to act as an author and take part in the development

176

of contents. Web 2.0 should enable all internet users to actively take part in the further development of the internet. Everyone should be able to contribute easily. The focus of Web 2.0 is on the behaviour of the user. It should empower people to communicate and collaborate and contribute and participate. This growing phenomenon is very interesting and ought to be examined carefully, in order to understand how the web is evolving and how this continuously regenerative cycle of performance and technological innovation empowers “learning by sharing” (Thijssen & Vernooij, 2002). Based on the key principle of “architecture of participation”, social software can be seen as part of the Web 2.0. Wikis, weblogs and discussion forums are tools that are seen as social software applications and were selected for further research and the empirical study presented below.

related empirical research Institutions in the field of higher education have made efforts to introduce various IT-supported learning tools in the daily routine of students and lecturers (Evans & Sadler-Smith 2006; Aggarwal & Legon, 2006; McGill, Nicol, Littlejohn, Grierson, Juster & Ion, 2005; Dooley & Wickersham, 2007; Duffy & Bruns, 2006). Published results of the usage of weblogs in the prolearn-project (www.prolearn-project.org) have shown that a large majority of respondents considers personalization and adaptation of the learning environment as important and crucial factors. Learning should be individualized to become more effective and efficient. Personalization is a key element of the learning process, and specific problems need specific solutions, as students differ greatly in their backgrounds and capabilities. Learning materials are typically too general in order to cover a very wide range of purposes and personal learning needs. Compared to classical learning personalization can be the most

E-Learning with Wikis, Weblogs and Discussion Forums

important added value that e-learning can offer. With it, education can be optimised and adjusted to various working conditions and needs, because students have different goals, interests, motivation levels, learning skills and endurance (Klamma, Chatti, Duval, Fiedler, Hummel & Hvannberg et al., 2006). Chao (2007) explored the potential uses of wikis in the field of software engineering (38 participants), especially for software project team collaboration and communication. Overall, twenty-five students agreed and one student disagreed (two neutral) that wiki is a good tool for project collaboration. Concerning the applications of wikis, more than twenty-three students found that a wiki is a good tool for maintaining a group diary, managing user stories (project requirements), and project tracking and reporting. While a majority of students found that a wiki is a good tool for updating a project plan, managing acceptance tests, defect tracking, and developing user document, there was also a significant number of students who disagreed (Chao, 2007). First results using wikis for collaborative writing (about 40 participants) also reported similar results. In this study students used wikis to write articles partly together with the lecturer. After early problems with software usage software and writing contributions in the wiki, students were able to write articles by themselves or in teams. The motivation among students was on different levels, so the lecturer had to increase it during lessons. Other students, however, were highly motivated and were creating the contents and added them to the wikis (Bendel, 2007).

constructivism and learning presentation of the learning model From a constructivist point of view learning focuses on the learning process by looking at the construction of knowledge by an individual. As a consequence there is a recommendation to align (Holmes, Tangney, FitzGibbon, Savage & Mehan,

2001; Du S. & Wagner, 2005; Jonassen, Mayes T. & McAleese R., 1993) learning environments, especially in the academic context, with associate complex learning objectives to constructivist learning principles. Learning is not seen as a transmission of content and knowledge to a passive learner. Constructivism views learning as an active and constructive process which is based on the current understanding of the learner. Learning is embedded in a social context and a certain situation (Schulmeister, 2005). The constructivist approach shifts learning from instruction-design-centered to a learnercentered learning and teaching mode. The role of the educator changes from directing the learner towards supporting and coaching him/her. Baumgartner et al. (2004) have suggested three different prototypical modes of learning and teaching. These three different modes of learning and teaching are neutral to specific so they can be applied across all subject domains. Therefore, each teaching model can be used to teach e.g. sociology subjects as well as for teaching e.g. technical sciences. Learning can be portrayed as an iterative process that can subsequently be subdivided into different phases which are summarized in Figure 1. In particular, these three different prototypical modes for learning encompass the following:

Learning/Teaching I – Transferring Knowledge At the starting point the learner needs to be provided with abstract knowledge to lay the theoretical foundations and to understand relevant signposts, road markings and orientation points. This kind of factual knowledge is static and has little value by itself in real and complex situations. It merely serves as a shortcut to prevent pitfalls and to help to organize his/her learning experiences. The knowledge of the student is based on knowledge possessed by the teacher. Students have to learn what teachers ask them to learn.

177

E-Learning with Wikis, Weblogs and Discussion Forums

Figure 1. Prototypical Modes of Learning and Teaching (Baumgartner, 2004)

The teacher has the responsibility to make the knowledge transfer as easy as possible.

Learning/Teaching II – Acquiring, Compiling, Gathering Knowledge In this section of the individual learning career, the student actually applies the abstract knowledge and gathers own experiences. In order to limit the action and reflection possibilities, the learner interacts within a somewhat restricted, artificial environment, which is reduced in complexity and easy to control by the teacher. To provide feedback, the learning environment is designed to include relevant devices where students can deposit their interim products and teachers can inspect it. The emphasis in this model lies on the learning process of the student. Teachers try to help the students overcome wrong assumptions, wrong learning attitudes and assist in the reflection process of the subject domain.

Teaching III – Developing, Inventing, Constructing Knowledge Teacher and learner work together to master problems. This model includes problem generation and/or invention. The environment is constructed

178

in such a way that it represents, at least in certain aspects, reality or reality in a constrained form. This model includes two-way communication on equal terms, using either linguistic representations or other adequate kinds of language. Teaching III has strong links to constructivism. From a constructivist point of view learning is considered as an active process in which people construct their knowledge by relating it to their previous experiences in complex and real situations in life. In their practical lives people are confronted with unique, unpredictable situations whose inherent problems are not readily observable (Baumgartner, 2004). Constructivism does not represent a distinct theoretical position in the field of education. There exist some different approaches, thus constructivism can be understood as a continuum. For learning the two streams “communal constructivism” and “social constructivism” are essential (Pountney, Parr & Whittaker, 2002). The social constructivism regards learning as a social process. Reality cannot be discovered, it does not exist prior to its social invention. Thus knowledge is also a human product, and is socially and culturally constructed. This means that knowledge is socially co-constructed, which is a negotiation process by the individuals with

E-Learning with Wikis, Weblogs and Discussion Forums

other individuals and with their environment. Social constructivists accept the existence of an “inter subjectivity” as a shared understanding about a knowledge object among individuals. Social constructivism stresses the importance of feedback and reinforcement. On the basis of the upcoming technologies for learning and education Holmes et al. (2001) have suggested a new educational theory that goes beyond the existing social constructivism. According to their theory learners are not only constructing their own knowledge but they also produce knowledge for other students and learners. They consider that peer tutoring and project-based learning are obvious techniques. Furthermore they advocate the ideas of cognitive apprenticeship, the publishing of information, flexibility in the time table, a radical look at the way in which assessment is done. Siemens (2005; 2006) coined the term “connectivism” due to the fact that by using the web learning and knowledge management have changed dramatically. He understands learning as creating networks. In order to deal with the increasing plethora of information he suggests the outsourcing of explicit knowledge to the network of the respective community. Having knowledge is not in the center, but rather knowing who can provide the necessary information to generate the knowledge needed Students should be enabled to invent new things, produce or generate new knowledge. Consequently, learning and teaching at universities in most cases can be assigned to the requirements presented in Learning/Teaching II and III with respect to theories concerning the digital learning support. In order to achieve this goal, a special learning environment must be provided.

consequences for It-supported learning and teaching Computer software can be used for all three models ranging from programmed instruction (Learning/

Teaching I) to problem solving software (Learning/Teaching II) to complex simulations and/or so-called micro worlds (Learning/Teaching III). It is said that the inherent nature of the internet brings the real world into the classrooms and with its hyperlink structure it clearly advocates the model Teaching III (Hsu, 2008; Baumgartner, 2004). The use of the internet, and especially through its social software, gains in importance because it can contribute to exceed the limits of classical teaching models. By adapting learning and teaching models to the new technical possibilities, the roles of learner and teacher are becoming more indistinct, because the learner can take a central part in the design and arrangement of the learning process (Kerres, 2006). Systems that support learners with respect to the learning model III are called Personal Learning Environments (PLEs). PLEs are mostly web-based applications and are based on learning management systems (Seufert, 2007). PLEs are personal and open learning environments and they are suitable for cross-linking contents and people. Learners can use PLEs to manage individual learning progress. They are ideally available for life-long-learning and are supported by the following processes: Reflexive writing: Besides easy reading access to contributions it is the simple and efficient and rather robust encoding standard usually used in social software that allows the explicit modeling of content flows, feedback loops, and monitoring procedures of various kinds. Thus supporting these systems support an ongoing reiterative process of explication and reflection (Fiedler, 2003). PLEs should support the development of the ability to learn (“learning to learn”): Through the publication of one’s thoughts and reflections, content is made available for assessment as well as for further development, thereby improving self-observation and self-reflection skills. The way the learner can learn and acquire will be improved (Baumgartner, 2005).

179

E-Learning with Wikis, Weblogs and Discussion Forums

As already discussed new learning theories for digital learning support have been developed, for example “social and communal constructivism” that considers learning as a social process or “connectivism” that understands learning as creating networks. Communication and discussion: The integration supports the exchange of ideas as well as finding like-minded people. Furthermore, social software tools simplify the process of establishing connections between people of the same interests. They also simplify the construction of connections between people with similar interests. Simultaneously its open and expandable philosophy supports going beyond the thinking in groups (of a common interest) by supporting diversity and bringing together different perspectives and backgrounds (Efimova & Fiedler, 2004; Schulmeister, 2004). This supports learning from different perspectives. Community building: PLE-tools have to provide a personal learning area for their authors. However, this does not force a general learning flow or learning style. Nevertheless, learners are not alone and can profit from the feedback of a community in order to examine and enhance the development of own ideas (Efimova & Fiedler, 2004; Fiedler, 2003; Böttger & Roll, 2004). Achieving synergies of self-organized and joint learning should be enabled by those tools. Through reading in other learning environments, especially beginners are enabled to learn from experts. At the same time they can actively participate in discussions beyond geographic or thematic borders (Efimova & Fiedler, 2004; Fiedler, 2003). Life-long-learning: Life-long learning is a key issue for knowledge society for today and tomorrow. In order to cope with the fast change of knowledge future knowledge workers have to thus be prepared to react fast and manage their further education and training at the workplace. In addition, they have to take responsibility for their own employability (Klamma, Chatti, Duval, Hummel, Hvannberg & Kravcik et al., 2007).

180

Life-long learning is seen as multi episodic, with individuals spending occasional periods of formal education and training throughout their working life (Attwell, 2007b). Life-long learners need tools that can be used for all learning activities no matter what subject they learn or which educational institution they attend. Quality of contents: The quality of contents is a key factor that determines the sustainable usage of a knowledge management system. (Maier, 2004, p. 247) Pleasure: Besides the activities that should be supported by social software tools the usage of the tools must give pleasure to the users. If knowledge management should be fostered, playful behavior needs to be supported rather than strict norms (Schneider, 2004; Landry, 2000). Unlike a Learning Management System (LMS) that is usually related to one special institution or to one special course, a PLE is focused on the individual learner. A PLE should combine a broad mixture of different resources and sub-systems in a “personally-managed space” (Attwell, 2006). In the previous decade, Learning Management Systems were developed that moved toward enterprise-level applications. “But the wealth of new, user-friendly, tools in the Web 2.0 environment suggests that the all-in-one monolithic e-learning systems may be entering a phase of obsolescence by the ongoing development of the web” (Craig, 2007). Social software applications have the potential to cope with these requirements (Brahm, 2007).

descrIptIon And clAssIFIcAtIon oF socIAl soFtwAre tools In the following section three social software tools, weblogs, discussion-forums and wikis, are described more in detail and the tools are compared. Students were able to select these tools during the empirical study.

E-Learning with Wikis, Weblogs and Discussion Forums

weblog A weblog, a compound of “web“ and “logbook”, usually just called “blog”, is a website that contains new articles or contributions in a primarily chronological order, listing the latest entry on top. Primarily, a weblog is a discussion-oriented instrument especially emphasizing two functions, RSS-feed and trackback. RSS-feeds, also called RSS-files can be read and processed for further use by other programs. The most common programs are RSS-readers or RSS-Aggregators that check RSS-enabled websites on behalf of the user to read or display any updated contribution that can be found. The user can subscribe to several RSS-feeds. Thus, information of different websites can be retrieved and combined. Preferably, news or other weblogs are subscribed to. Trackback is a service function that notifies an entry in a weblog if a reference to this contribution has been made in another weblog. By this mechanism a blogger (person who writes contributions in a weblog) is immediately informed of any reactions to his contribution on other weblogs (Hammond, Hannay & Lund, 2004). Weblogs are often interrelated with other weblogs, the blogosphere, and discussion forums are rather concentrated on one specific topic. The interrelations between discussion forums are not as intensive as in the blogosphere.

discussion Forum A discussion forum or web forum is a service function providing discussion possibilities on the internet. Usually, web forums are designed for the discussion of special topics. The forum is furthermore subdivided into sub-forums or subtopics. Contributions to the discussion can be made and other people may read and/or respond to them. Several contributions to a single topic are called a thread. The application areas of the two instruments weblogs and forums are quite similar. The most

essential differences between weblogs and discussion forums can be described as follows: •

•

•

A forum is usually located on one platform while many bloggers develop their own, individual environment. They connect their weblogs via RSS-feed and trackback functions. Through the integration of RSS-files and trackback functions a discussion process can be initiated and continued crossing the boundaries of the bloggers’ own weblogs without having to observe other weblogs. Weblogs tend to be more people-centered whereas forums are more topic-focused. Through the use of weblogs, learner-specific learning environments can be constructed without interfering with the learning environments of others (Baumgartner, 2004).

wiki AWikiWikiWeb, shortly called Wiki, is a hypertext system for storing and processing information. Every single site of this collection of linked web pages can be viewed through a web browser. Furthermore, every site can also be edited by any person. The separation between authors and readers who write their own text, change and delete them is obsolete as also third parties can carry out these functions (Augar, Raitman & Zhou, 2004). The most essential differences between weblogs, wikis and discussion forums can be described as follows (Wagner & Bolloju, 2005, p. 5).

learning Activities supported by social software The integration of different Social Software Tools offers support in the following learning activities:

181

E-Learning with Wikis, Weblogs and Discussion Forums

Figure 2. Comparison of Weblogs, Wikis and Discussion Forums

•

•

182

Learning from different perspectives: The integration supports the exchange of ideas as well as finding like-minded people. Furthermore, social software tools simplify the process of establishing connections between people of the same interests. They also simplify the construction of connections between people with similar interests. Simultaneously its open and expandable philosophy supports going beyond the thinking in groups (of a common interest) by supporting diversity and bringing together different perspectives and backgrounds (Efimova & Fiedler, 2004; Schulmeister, 2004). Synergies of self-organized and joint learning: Social Software tools provide a personal learning area for their authors. However, this does not force a general learning flow or learning style. Nevertheless, learners are not alone and can profit from the feedback of a

•

•

community in order to examine and enhance the development of own ideas (Efimova & Fiedler, 2004; Fiedler, 2004; Böttger & Röll, 2004). Digital apprenticeship: Through reading other wikis, forums or weblogs regularly, beginners are enabled to learn from experts. At the same time they can actively participate in discussions beyond geographic or thematic borders (Efimova & Fiedler, 2004; Fiedler, 2004). Weblogs and comparable tools support the development of the ability to learn (“learning to learn”): Through the publication of ones own thoughts and reflections, content is made available for assessment as well as for further development, thereby improving self-observation and self-reflection skills. The knowledge change of the learner will be improved (Baumgartner, 2005).

E-Learning with Wikis, Weblogs and Discussion Forums

•

Social Software supports reflexive writing: The simple, but efficient and rather robust encoding standard usually used in Social Software allows for the explicit modelling of content flows, feedback loops, and monitoring procedures of various kinds, thus supporting an ongoing reiterative process of explication and reflection (Fiedler, 2004).

Integration of social software tools and the learning/teaching modes Baumgartner (2004) has integrated different types of Content Management Systems in relation to the most suitable learning/teaching mode. He clearly states that the boundaries are overlapping and that every tool – in one way or the other – could be used for every teaching model. The following Figure 3 presents the integration of the Social Software tools and the learning/teaching modes: Weblogs and forums can be defined as “discussion-oriented“ tools because the discourse and exchange of ideas related to a certain topic is the pre-eminent aim. Weblogs offer the possibility to support all three phases of the learning process. However, the main focus can be assigned to the modes “Teaching II” and “Teaching III”. Based on the multitude of interaction possibilities, wikis can be attached to “Teaching III“ (Baumgartner, 2004). Additional functions

were added to weblog-tools that go beyond the scope of the central use of weblogs, e.g. longer articles can also be stored. Through the creation of directories, a structured collection of links can be implemented. Through the additional linking of weblogs, wikis and forums, there is the possibility to develop a personal knowledge collection (Kantel, 2003).

empIrIcAl surVeY The purpose of this survey was to determine if the integration of web-based social software tools (wikis, discussion forums and weblogs) are suitable to foster learning from the student’s point of view.

Aim of the survey and methodology Scrutinizing the possibilities and constraints of social software tools (wikis, discussion forums and weblogs) as a personal learning environment, students at Austrian universities were asked to use one or more of the offered tools for their research, home work and documentation purposes. In most cases collaboration of students was required to perform the assigned tasks. The students were asked to use the tools for one course only during Winter Term 2006. Furthermore, there was no obligation for the students

Figure 3. Prototypical Modes and Social Software Tool

183

E-Learning with Wikis, Weblogs and Discussion Forums

to use a tool at all, they were just encouraged to do so. Students were also offered the possibility using two or three tools - their selection was up to the students. The courses were organized as blendedlearning courses, so they included on-campus lessons and off-campus work in which the students could work face-to-face or using the social software tools. More than 90% of the students attending the courses took part in this survey. In order to give the participants an impression of the functionality and usage of the tools short presentations of the tools were made by an instructor before the students made their choice. At the end of the testing phase - after four weeks of using the tools - selected student reported their experiences with the tools used. Thereby students who had decided not to use the tools in the first place got an impression about the usage, advantages and disadvantages of the tools by their fellow students. Following these short presentations a questionnaire was completed that provided the basic findings for further inspections and research. A total of 268 first-semester students of different Austrian universities in five selected courses took part in this survey. The majority of the participants were between 18 and 20 years old. The portion of female students was about 17%. According to a survey conducted by Seybert (2007) concerning gender differences in computer and internet usages for young people (aged between 16 and 24), there is no gap between men and women in Austria. The proportion of women and

men (in the relevant age class) that used a computer (almost) once a day is with 72% the same. A study by Otto et. al. (Otto, Kutscher, Klein & Iske, 2005) indicates that there is a positive correlation between a formal educational background and the usage of the internet in Germany. “Beside socio-cultural resources like family background, peer structures and social support in general, the formal educational background turns out to be the main factor for explaining differences in internet usage” (Kutscher, Klein & Iske, 2005, p. 219). As a consequence for the analysis of the results of this survey, no distinction between male and female students was made. Table 1 presents the distribution of the participants concerning the degree program the students are attending. For the further analysis of the results no distinction according to the degree programs will be made. This questionnaire asked each participant questions about her or his subjective impression of the application of the tools. It included fivepoint Likert scales for rating constructs such as eligibility, perceived quality or enjoyment. The study was conducted to find answers about the • • • •

usage of social software before the study started selection of the offered tools perceived quality of the contributions and the support for learning applicability of the instruments to support

Table 1. Distribution of students regarding the degree program Distribution Management & Law

17%

Management & IT

31%

Management & Industrial Engineering

22%

Mechanical Engineering, Electronics

30%

184

E-Learning with Wikis, Weblogs and Discussion Forums

• • •

communication and community-building the correlation of the usage for private and educational purposes of the tools fun factor using the instruments potential future usage

The results of the study are presented in three parts: •

• •

Part 1: Analysis of the usage of wikis, discussion forums and weblogs of the students before the study was started Part 2: Experiences made with the tools during the study Part 3: Potential future usage of the tools

part 1: tool-selection and pre-study usage Due to the fact that the students could select the tools on their own, the following table shows the results of this selection process. According to the above table the combination of wikis and discussion forums is the most selected combination of tools (42,9%), followed by wikis only (23,1%) and discussion forums only (22,4%). In the end only five students

(1,9%) did not take part in the study; they did not select a tool, although they first had had the intention to do so. Only one student used weblogs only. Generally, weblogs were not used very intensively by the participants. The following table shows the usage of the tools by the participants before they took part in the study. It indicates that wikis (76%) and discussion forums (78%) are currently the most widely used tools. Weblogs are only used by 11% of the asked students. The results clearly show that the weblog hype had not yet reached the surveyed students. Due to the fact that only about 11% of the students are currently using weblogs, the results for this instrument are not published for the first part of the analysis. When it comes to the potential future usage of the instruments, weblogs are taken into consideration again. The following section presents the results for questions analyzing the usage in more detail. The following two results present the current usage of the tools for private and educational purposes. The question: “I often use (wikis, forums) for private purposes” was asked: In the following table the results for the corresponding question “I often use (wikis, forums) for educational purposes” are presented:

Table 2. Tools selected by the Students Per Cent

Number

Only one tool selected Wikis only

23,1%

62

Discussion forums only

22,4%

60

Weblogs only

0,4%

1

Wikis and discussion forums

42,9%

115

Wikis and weblogs

1,9%

5

Discussion forums and weblogs

0,7%

2

Wikis, discussion forums and weblogs

6,7%

18

1,9%

5

More than one tool selected

No tool selected No tool selected

185

E-Learning with Wikis, Weblogs and Discussion Forums

A huge majority (90%) stated that they use wikis for educational purposes and about two thirds (68%) used wikis for private purposes. Wikis are therefore more intensively used for educational purposes than for private purposes, whereas the usage of forums is exactly the opposite, they are more used for private purposes than for education. The answers of the students concerning this question were that wikis are foremost considered as a source of serious information, whereas forums are ideal for getting hints or clues to problems related to their privacy. Questions about computer problems, computer games, leisure activities, etc. were mentioned. A repetition of this image can be identified when the disagreement with the question is analyzed. 29% of the students do not or rarely use forums for private purposes, compared to 36% in their education.

part 2: experiences made during the study This section presents the results of the study concerning experiences with the usage of the tools during the study.

Quality and Support for Learning The next section refers to questions concerning the quality of contributions of wikis and discussion forums and their support for learning. The results of the question “The quality of contributions in (wikis, forums) is in general good” regarding the quality of contribution are presented in the table above. The contributions of wikis are evaluated to be much better than those of forums.

Table 3. Students already using the tools Wiki

Forum

Weblog

Yes

76%

78%

11%

No

24%

22%

89%

Table 4. Usage for Private Purposes Wiki

Forum

I totally agree

33%

33%

I generally agree

35%

29%

neither ... nor (neutral)

9%

9%

I slightly disagree

16%

17%

I disagree

8%

12%

Table 5. Usage for Educational Purposes Wiki

Forum

I totally agree

57%

22%

I generally agree

33%

29%

neither ... nor (neutral)

3%

12%

I slightly disagree

8%

24%

I disagree

1%

12%

186

E-Learning with Wikis, Weblogs and Discussion Forums

Table 6. Perceived Quality of Contributions Wiki

Forum

I totally agree

38%

10%

I generally agree

52%

31%

neither ... nor (neutral)

10%

41%

I slightly disagree

2%

15%

I disagree

0%

4%

Table 7. Clarity of Contributions Wiki

Forum

I totally agree

2%

4%

I generally agree

6%

18%

neither ... nor (neutral)

29%

37%

I slightly disagree

34%

27%

I disagree

29%

14%

Table 8. Reading Contributions helps to acquire Contents Wiki

Forum

I totally agree

23%

8%

I generally agree

36%

21%

neither ... nor (neutral)

32%

31%

I slightly disagree

5%

25%

I disagree

3%

15%

Table 9. Writing Contributions helps to acquire Contents Wiki

Forum

I totally agree

8%

7%

I generally agree

13%

19%

neither ... nor (neutral)

45%

34%

I slightly disagree

14%

22%

I disagree

19%

17%

The surveyed pupils had the possibility to give reasons for their assessment concerning the quality of contributions via additional qualitative answers. The following summarizes the addressed reasons:

One reason for this excelling grade for the quality of wikis is the “Wikipedian Community”. The term “wiki” is often seen as synonym for the free online encyclopaedia Wikipedia (www.wikipedia. org). Wikipedia is widely used for a great variety

187

E-Learning with Wikis, Weblogs and Discussion Forums

of tasks, so for research on all topics needed for educational and private purposes. In contrast to the good evaluation of contributions of wikis, the open architecture of wikis was also mentioned. In most cases this open architecture allows everyone to edit entries which results in the uncertainty of whether the knowledge presented is correct or not. The quality of contributions in discussion forums was rated rather mediocre. Forums are primarily used for technical problems, especially computer related problems, and to get in contact with an expert of a certain topic and to get information on online-games. The next question “The usage of (wikis, forums) leads to misunderstandings and confusion” is about the clarity of the contributions. Only a minority think that the contributions are not clear and may lead to misunderstandings. In this case wikis are also rated better than forums. The next questions addressed the support of these instruments for learning. Table 8 summarizes the results for the question “When reading contributions in (wikis, forums) it is easier for me to acquire the learning contents”. More than half of the students express that reading contributions in wikis is helpful for learning, whereas only about 8% think that it is not helpful. Compared to forums, wikis were again much better evaluated especially considering the big difference with the negative evaluations of forums. Table 9 presents the learning support achieved by writing contributions (“When writing contributions in (wikis, forums) it is easier for me to acquire the learning contents”). A different picture emerges in the statistics when comparing the evaluation of how writing an article or post supports the learning process. Here forums take the lead when it comes to positive assessment. In both cases there were a large number stating that writing is neither positive nor negative. The majority of the students rather read than wrote, whereas more students wrote in forums than in wikis.

188

Applicability for Communication and Community-Building The question was formulated as follows: “(wikis, forums) are appropriate to support communication”. The results clearly demonstrate that discussion forums are “made for communication” whereas wikis are rather seen as a kind of reference book or encyclopedia, as already mentioned above. The results of the next question, “(wikis, forums) support the set up of communities”, can be seen in the following table. Opinions about the applicability of wikis to establish a community is split. About 35% say that wikis are supportive of building a community, compared to 25% who said that wikis do not support community-building. The support of forums to build a community is rated much better – 50% indicated that forums are well suited to build a community. These results were to be expected because they confirm the nature of the instruments.

Fun Factor when Using the Instruments In surveying whether students gain pleasure (“I enjoy using (wikis, forums)”), wikis again came back on top. A majority of 62% enjoy using wikis and forums (56%). Considering the answers that there is no (I disagree) or little (I slightly disagree) fun when using these instruments wikis (6%) are much better rated than forums (18%).

part 3: potential Future usage of the tools The third section of the empirical study deals with the potential usage by students who had not used one of the instruments before the study. Students gained knowledge and experiences by using the tools during the study by themselves or on the basis of the reported experiences made by their fellow students.

E-Learning with Wikis, Weblogs and Discussion Forums

Table 10. Applicability for Communication Wiki

Forum

I totally agree

9%

39%

I generally agree

33%

37%

neither ... nor (neutral)

29%

17%

I slightly disagree

15%

4%

I disagree

15%

3%

Table 11. Support for Community-Building Wiki

Forum

I totally agree

10%

28%

I generally agree

25%

32%

neither ... nor (neutral)

39%

23%

I slightly disagree

15%

11%

I disagree

10%

6%

Table 12. Fun Factor when using the Instruments Wiki

Forum

I totally agree

26%

19%

I generally agree

36%

37%

neither ... nor (neutral)

31%

26%

I slightly disagree

5%

14%

I disagree

1%

4%

Table 13. Future Usage in Educational Context (current non-users) Wikis

Forums

Weblogs

I totally agree

18%

16%

13%

I generally agree

36%

23%

23%

neither ... nor (neutral)

30%

16%

24%

I slightly disagree

9%

12%

13%

I disagree

7%

33%

27%

The first question “I will use (wikis, forums, weblogs) for educational purposes in the future” yielded the following results. According to this study wikis will then have a bright future and will be used often for educational purposes, whereas forums will be used less often.

About 54% of the surveyed students had the intention of using wikis more or less often in the future. About 16% did not think that they will use wikis often in the future and 30% are not yet sure if they will use this instrument or not.

189

E-Learning with Wikis, Weblogs and Discussion Forums

The results for forums and weblogs indicate no clear trend, but forums were rated slightly higher than weblogs. 39% of the students stated that they can image to use forums (36% for weblogs) in the future for their education. At the other end of the scale, 45% did not have the intention to use forums (40% for weblogs). The equivalent question “I will use (wikis, forums, weblogs) for private purposes in the future” leads to similar results. From this point of view, wikis are again the leading instrument, followed by forums and weblogs. It must be said that the answers to this set of questions represented feelings, attitudes and opinions about instruments that had not yet been used by the asked participants. The purpose of posing these questions was to gain insight into the mindset in regard to these instruments.

dIscussIon The results clearly show that wikis are currently the most often used instrument and furthermore have the greatest potential as a tool for learning and knowledge management in the field of learning – and these findings are in line with other empirical studies (Bendel, 2007; Chao, J. 2007). Other studies (Nicol and Macleod, 2004; McGill, Nicol, Littlejohn, Grierson, Juster and Ion, 2005) report that a shared workspace helped to support collaborative learning. Especially the possibility of being able to access and contribute to

the development of resources at any time and from any location was appreciated by the students. In an empirical study conducted by Wheeler, Yeomans and Wheeler (2008) 35 undergraduate students (1st-, 2nd- and 3rd-year) of a teacher training (Bachelor of Education) have been using open-content wiki software for one year as an integral part of their studies. Ages ranged from 18 to 25 years, and there were two mature students. Each participated in this evaluative study voluntarily. Students used the wikis regularly during their classroom sessions as a space to store and edit the work from their research exercises, and as a forum for discussion. During teaching sessions students were invited via the integrated discussion board to post their views on their use of the wiki. They were also invited to complete a post-module questionnaire via email. The authors conclude that “wikis have the potential to transform the learning experiences of students worldwide. The benefits appear to outweigh the limitations.“ (Wheeler, Yeomans & Wheeler, 2008, 994) Collaboration, rather than competition, should be emphasised as a key aim of any wiki-based activity. A study conducted by Solvie (2008) took place during a three week period of a semester long reading methods course for preservice teachers. Eighteen preservice teachers participated in the study. A combination of quantitative and qualitative methods was used. One major purpose of this study was to get insights into the usage of a wiki in combination with individual learning styles thereby the cog-

Table 14. Future Usage in Private Context (current non-users) Wikis

Forums

Weblogs

I totally agree

11%

14%

9%

I generally agree

36%

23%

22%

neither ... nor (neutral)

30%

25%

24%

I slightly disagree

14%

7%

16%

I disagree

9%

32%

28%

190

E-Learning with Wikis, Weblogs and Discussion Forums

nitive learning style model by Kolb was used. Generally the results show that the used wiki provided a space for effective collaborative work. The furthermore found out that there is a correlation between the individual learning style and the specific usage of the wiki. Participants who believed the wiki was helpful in constructing knowledge of reading contents in this environment said seeing all the information together was helpful. Learning more about and looking in depth at their particular work was helpful, and researching their approach was enjoyable. The participants had some problems using the technology but these problems were associated with their own level of confidence in use of technology. The survey at hand made a distinction between reading and writing contributions with wikis and discussion forums. The results show that reading contributions in wikis is helpful (with the answers “I totally agree” and “I generally agree”) for learning (59%) compared to 21% who stated that writing in wikis is helpful for learning. Reading contributions in forums helped 29% of the participants whereas writing in forums is helpful to 26%. This survey supports the general statement that a shared workspace that supports a constructivist and learner-centered approach is helpful for learning. An empirical study on an eLearning module on an MSc in Information Technologies and Management was conducted by Gilbert, Morton and Rowley (2007). Nineteen students located across the globe where enrolled on the module. All students were graduates, but most of their prior learning experiences had been in standard face-to-face delivery mode. A discussion forum was integrated in the learning environment that was used by all students more or less frequently. Most of them were very or quite comfortable about posting contributions to the discussion threads, although some students were not confident to make contributions. Compared to email, chat, telephone or face to face the discussion forum

was the most widely used communication channel among the students. Concerning the impact on learning students responded that they learnt from other students. The support from other students with discussion forums were the most frequently cited aspects of the learning process whereas some were reluctant to be the first contributor. The pedagogical value in the context of learning is described in several publications (Babcock, 2007; Hurst, 2005). Weblogs can foster the establishment of a learning and teaching environment in which students and teachers can experience a greater degree of equality and engagement. Du & Wagner (2007) published results of a study of an Information Systems undergraduate course (31 participants). This study indicated that the performance of students’ weblogs was a significant predictor for learning outcomes, while traditional coursework was not. Moreover, individuals’ cognitive construction efforts to build their own mental models and social construction efforts to further enrich and expand knowledge resources appeared to be two key aspects of the constructivist learning with weblogs. According to this study there is a potential benefit of using weblogs as a knowledge construction tool and a social learning medium (Du & Wagner, 2007). In this survey at hand weblogs were not yet widely used and their potential seems to be limited. It can be assumed that these limited prospects will change, when the penetration of weblogs into the daily routine of the students will increase – for private as well as for educational purposes. These results are confirmed by Reinmann (2008) who states that blogging in an educational context (“edublogs”) is obvious when the possibilities are taken into consideration whereas the usage of these tools is not widely spread in the daily routine. There are already projects and there is a lot of information about using weblogs in educational settings (Scheloske, 2008; Reinmann & Bianco, 2008).

191

E-Learning with Wikis, Weblogs and Discussion Forums

The results about the potential future usage of wikis, weblogs and discussion forums show that these tools have the potential to be used for life-long learning. According to this study, wikis will have a bright future and will be used often for educational and private purposes. The results for discussion forums and weblogs indicate no clear trend, but forums were rated slightly higher than weblogs. Other studies (Klamma, Chatti, Duval, Hummel, Hvannberg & Kravcik et al., 2007; Attwell, 2007a) confirm these results, whereas weblogs are considered to be more powerful for personal knowledge and learning management (Williams, 2004; Böttger & Roll, 2004; Fiedler, 2003). The contribution of this thesis to the discussion that can be found in Oblinger and Oblinger (2005), Lenhart and Madden (2005), Aggarwal and Legon (2006) or Prensky (2001) about the already available “digital literacy” of young people indicates that wikis and discussion forums are currently the most widely used tools. More than 75% are already using these tools, whereas weblogs are only used by 11% of the asked participants. From these results it can be concluded that there is some form of digital literacy among young students. Critics of the net-generation and the derived consequences for learning and teaching state that there is “a sense of impending crisis pervades this debate. However, the actual situation is far from clear. […] Our analysis of the digital native literature demonstrates a clear mismatch between the confidence with which claims are made and the evidence for such claims” (Kennedy, Judd, Churchward, Gray & Krause, 2008) Schulmeister (2008) points out that the term “generation” has often been used in the past by attributing some specific characteristics or qualities to people living in a certain period of time. In the USA, for example, earlier generations have been classified by researchers as the “matures” (1900-1946), the “baby boomers” (1946-1964), “Generation X” (1965-1982) or the “net genera-

192

tion” (1983-1991). In many cases these attributes are only valid for a minority of this generation but by establishing a classificatory system this minority is used to represent a whole generation. In other publications (Media Awareness Network, 2004) it is reported that for young people it is normal to grow up with new technologies. Computers and other new technologies like mobile phones are not new for them, they just exist and they use new technologies to manage and organize their daily life (Tully & Zerle, 2005). A Canadian study summarizes its results as follows: “The Internet, for young people, is part of the pattern of their day and integrated into their sense of place and time. The Internet just is.” (Media Awareness Network, 2004, p. 8) With respect to the individual learning styles of young people Bennett et al. (2008) come to the conclusion that “young people’s relationships with technology is much more complex than the digital native characterization suggests. While technology is embedded in their lives, young people’s use and skills are not uniform. There is no evidence of widespread and universal disaffection, or of a distinctly different learning style the like of which has never been seen before. We may live in a highly technologized world, but it is conceivable that it has become so through evolution, rather than revolution.” Thus it is important to provide systems that offer all possibilities of setting up a personal learning environment in order to enable people to work and learn according to their individual learning style. To avoid possible pitfalls about the application of these instruments in the context of learning, some social and psychological issues must be taken into consideration (Kreijns, Kirschner & Jochems, 2003). Social interaction is essential for members of a team to get to know each other, commit to social relationships, develop trust and develop a sense of belonging, in developing a learning community. The size and the composition of the learning communities seem to be an

E-Learning with Wikis, Weblogs and Discussion Forums

important factor how interaction and communication within the learning community will take place (Dooley & Wickersham, 2007). There are also many unresolved issues, like provision of the technology and the services, intellectual property rights and digital rights management, security of data, access restrictions to the contents or questions in the field of information ethics (McGill, Nicol, Littlejohn, Grierson, Juster & Ion, 2005; Attwell, 2006; Sharma & Maleyeff, 2003).

FInAl remArks The aim of this contribution was to investigate the experiences of students using social software tools in the context of learning. Wikis, weblogs and discussion forums are typical social software tools and were used for this survey. The results clearly show that wikis and discussion forums can support learning and collaboration. The usage of weblogs in this study was limited and hence no statements about their applicability can be made. In order to assure a successful implementation of these tools social and psychological issues must be taken into consideration as well. The results of this study are the basis for the introduction of social software into education to help students to set up their individual learning environment. These learning environments should support life-long-learning and the structuring of a personal learning environment. In the „Hype Cycle for Emerging Technologies” report of 2008 describes microblogging and social social computing platforms as technologies and trends that are around the peak of the Hype Cycle in 2008 with the prospect that they will mature in two to five years (Fenn & Raskino, 2008) Microblogging is a relatively new addition to the world of social networking, in which contributors post a stream of very short messages (normally fewer than 160 characters) providing information via various information channels.

There are likely to be other unplanned consequences of the intensive use of the internet in general and social software especially. Further research is needed to explore possible problems along with hinting at solutions. The results of the empirical survey indicate that a long-term study in combination with the further development of social software tools may be promising.

reFerences Aggarwal, A. K., & Legon, R. (2006). Web-Based Education Diffusion. International Journal of Web-Based Learning and Teaching Technologies, 1(1), 49–72. Alexander, B. (2006). Web 2.0: A New Wave of Innovation for Teaching and Learning? EDUCAUSE Review, 41(2), 32–44. Attwell, G. (2006). Personal Learning Environment. Retrieved on May 17, 2007 from http://www. knownet.com/writing/weblogs/Graham_Attwell/ entries/6521819364 Attwell, G. (2007a). The Personal Learning Environments - the future of eLearning? eLearning Papers, 2(1). Attwell, G. (2007b). Web 2.0 and the changing ways we are using computers for learning: what are the implications for pedagogy and curriculum? Retrieved March 3, 2008, from http:// www.elearningeuropa.info/directory/index. php?page=doc&doc_id=9756&doclng=6. Augar, N., Raitman, R., & Zhou, W. (2004). Teaching and learning online with wikis. In R. Atkinson, C. McBeath, D. Jonas-Dwyer & R. Phillips (Eds.), Beyond the Comfort Zone, Proceedings of the 21st ASCILITE Conference, Perth, Western Australia, December 5-8 2004. ASCILITE. Retrieved on 17 May, 2007 from http://www.ascilite.org.au/ conferences/perth04/procs/contents.html

193

E-Learning with Wikis, Weblogs and Discussion Forums

Babcock, M. (2007). Learning logs in introductory literature courses. Teaching in Higher Education, 12(4), 513–523. doi:10.1080/13562510701415615 Baumgartner, P. (2004). The Zen Art of aching. Communication and Interactions in eEducation. Retrieved on 17 May, 2007 from http://bt-mac2. fernuni-hagen.de/peter/gems/zenartofteaching. pdf Baumgartner, P. (2005). Eine neue Lernkultur entwickeln: Komptenzbasierte Ausbildung mit Blogs und E-Portfolios. In V. Hornung-Prähauser (Ed.), ePortfolio Forum Austria, Proceedings of the ePortfolio Austria 2005 – Salzburg, 4 April 2004 (pp. 33-38).

Brahm, T. (2007). Social Software und Personal Broadcasting - Stand der Forschung. In S. Seufert & D. Euler (Eds.) Ne(x)t Generation Learning: Wikis, Blogs, Mediacasts & Co. - Social Software und Personal Broadcasting auf der Spur, SCIL Arbeitsbericht 12, Universität St. Gallen. (pp. 20-38). Chao, J. (2007). Student Project Collaboration Using Wikis. In Proceedings of the 20th Conference on Software Engineering Education & Training (pp. 255-261). Washington, DC. Coates, T. (2005). An addendum to a definition of Social Software. Retrieved on March 4, 2007 from http://www.plasticbag.org/archives/2005/01/an_ addendum_to_a_definition_of_social_software

Baumgartner, P., Häfele, H., & Maier-Häfele, K. (2004). Content Management Systeme in e-Education. Auswahl, Potenziale und Einsatzmöglichkeiten. Innsbruck: Studienverlag.

Craig, E. (2007). Changing paradigms: managed learning environments and Web 2.0. CampusWide Information Systems, 24(3), 152–161. doi:10.1108/10650740710762185

Bendel, O. (2006). Wikipedia als Methode und Gegenstand der Lehre. In K. Hildebrand (Ed.), HMD - Praxis der Wirtschaftsinformatik: Vol. H. 252. Social software (pp. 82-88). Heidelberg: dpunkt-Verlag.

Dooley, K. E., & Wickersham, L. E. (2007). Distraction, Domination and Disconnection in whole-class online Discussions. Quarterly Review of Distance Education, 8(1), 1–8.

Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British Journal of Educational Technology. Retrieved June 1, 2008, from http://www. blackwell-synergy.com/toc/bjet/0/0. Böttger, M., & Röll, M. (2004). Weblog publishing as support for exploratory learning on the world wide web. In P. Isaias, K. Demetrios & G. Sampson (Eds.), Cognition and Exploratory Learning in Digital Age (CELDA 2004), Proceedings of the IADIS International Conference – Lisbon, 15-17 December 2004, IADIS Press

194

Du, H. S., & Wagner, C. (2007). Learning With Weblogs: Enhancing Cognitive and Social Knowledge Construction. IEEE Transactions on Professional Communication, 50(1), 1–16. doi:10.1109/ TPC.2006.890848 Du, S. H. & Wagner, C. (2005). Learning with Weblogs: An Empirical Investigation. HICSS ’05: Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS’05) - Track 1 (pp. 7b-7b). Washington, DC, USA: IEEE Computer Society. Duffy, P. D., & Bruns, A. (2006). The Use of Blogs, Wikis and RSS in Education: A Conversation of Possibilities. [Brisbane.]. Proceedings Online Learning and Teaching Conference, 2006, 31–38.

E-Learning with Wikis, Weblogs and Discussion Forums

Efimova, L., & Fiedler, S. (2004). Learning webs: Learning in weblog networks. In P. Kommers, P. Isaias & M. B. Nunes (Eds.) Web Based Communities. Proceedings of the IADIS International Conference 2004, Lisbon 24-26 March 2004, IADIS Press. (pp. 490-494). Retrieved on May 17, 2007 from https://doc.telin.nl/dscgi/ds.py/ Get/File-35344 Evans, C. & Sadler-Smith, E. (2006). Learning styles in education and training: problems, politicisation and potential. Education + Training, 48(2/3), 77-83 Fenn, J., & Raskino, M. (2008). Mastering the hype cycle: How to choose the right innovation at the right time. Boston, MA: Harvard Business Press. Fiedler, S. (2004). Personal Webpublishing as a reflective conversational tool for self-organized learning. In T. Burg (Ed.), BlogTalks (pp. 190216). Retrieved on May 7, 2007 from http:// seblogging.cognitivearchitects.com/stories/ storyReader$963 Gilbert, J., Morton, S., & Rowley, J. (2007). elearning: The student experience. British Journal of Educational Technology, 38(4), 560–573. doi:10.1111/j.1467-8535.2007.00723.x Hammond, T., Hannay, T., & Lund, B. (2004, December). The Role of RSS in Science Publishing. Syndication and Annotation on the Web. D-Lib Magazine. Retrieved on April 20, 2007 from http://www.dlib.org/dlib/december04/ hammond/12hammond.html Holmes, B., Tangney, B., FitzGibbon, A., Savage, T., & Mehan, S. (2001). Communal Constructivism: Students constructing learning for as well as with others. In C. Crawford, D. A. Willis, R. Carlsen, I. Gibson, K. McFerrin & J. Price, et al. (Eds.), Proceedings of Society for Information Technology and Teacher Education Inte rnational Conference 2001 (pp. 3114-3119). Norfolk, VA: AACE.

Hsu, J. (2008). Innovative Technologies for Education and Learning: Education and KnowledgeOriented Applications of Blogs, Wikis, Podcasts, and More. International Journal of Web-Based Learning and Teaching Technologies, 3(3), 62–81. Hurst, B. (2005). My journey with learning logs. Journal of Adolescent & Adult Literacy, 49(1), 42–46. doi:10.1598/JAAL.49.1.5 Jonassen, D. H., Mayes, T., & McAleese, R. (1993). A manifesto for a constructivist approach to uses of technology in higher education. In T. M. Duffy, J. Lowyck, D. H. Jonassen & T. Welsh (Eds.), Xpert.press: Vol. Vol. 105. Designing environments for constructive learning. Proceedings of the Nato Advanced Research Workshop on the Design of Constructivist Learning Environments Implications for Instructional Design and the Use of Technology, held at the Catholic University Leuven, Belgium, May 14 - 18, 1993 (pp. 231247). Berlin/Heidelberg: Springer. Kantel, J. (2003). Vom Weblog lernen... Community, Peer-to-Peer und Eigenständigkeit als ein Modell für zukünftige Wissenssammlungen. Retrieved on March 12, 2007 from http://static. userland.com/sh4/gems/schockwellenreiter/ blogtalktext.pdf Kennedy, G. E., Judd, T. S., Churchward, A., Gray, K., & Krause, K.-L. (2008). First year students’ experiences with technology: Are they really digital natives? Australasian Journal of Educational Technology, 24(1), 108-122. Retrieved June 1, 2008, from http://www.ascilite.org.au/ajet/ajet24/ kennedy.html Kerres, M. (2006). Web 2.0 and its implications to E-Learning. In T. Hug, M. Lindner & P.A. Bruck (Eds.), Micromedia & e-Learning 2.0: Gaining the Big Picture, Proceedings of Microlearning Conference 2006 – Innsbruck, June 9 2004. University press

195

E-Learning with Wikis, Weblogs and Discussion Forums

Kerres, M. (2007). Microlearning as a challenge for instructional design. In T. Hug & M. Lindner (Eds.)‚ Didactics of Microlearning. Muenster: Waxmann. Retrieved on March 10, 2007 from mediendidaktik.uni-duisburg-essen.de/files/ Microlearning-kerres.pdf Klamma, R., Chatti, M. A., Duval, E., Fiedler, S., Hummel, H., Hvannberg, E. T., et al. (2006). Social software for professional learning: Examples and research issues. In B. Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. Sampson & W. Didderen (Eds.), Proceedings of the Sixth International Conference on Advanced Learning Technologies (pp. 912-914). IEEE. Klamma, R., Chatti, M. A., Duval, E., Hummel, H., Hvannberg, E. T., Kravcik, M., et al. (2007). Social Software for Life-long Learning. Educational Technology & Society, 10(3), 72-83. Retrieved from http://dspace.ou.nl/bitstream/1820/910/4/ ET%26S\_socialsoftware.pdf Kreijns, K., Kirschner, P., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research. Computers in Human Behavior, 19, 335–353. doi:10.1016/ S0747-5632(02)00057-2 Kutscher, N., Klein, A., & Iske, S. (2005). Differences in Internet Usage - Social Inequality and Informal Education. Vol. 3. Social Work & Society (pp. 215-233). Landry, J. R. (2000). Playing at Learning: Why Knowledge Creation Needs Fun. In HICSS. Lenhart, A., & Madden, M. (2005). Teen Content Creators and Consumers. Pew internet project data memo. Retrieved on June 21, 2007 from http:// www.pewinternet.org/pdfs/PIP_Teens_Content_Creation.pdf Lenhart, A., & Madden, M. (2005). Teen Content Creators and Consumers. Washington, D.C. PEW INTERNET PROJECT DATA MEMO, Retrieved on 23 June, 2007 from http://www.pewinternet. org/pdfs/PIP_Teens_Content_Creation.pdf 196

Lewinson, J. (2005). Asynchronous discussion forums in the changing landscape of the online learning environment. CampusWide Information Systems, 22(3), 162–167. doi:10.1108/10650740510606162 Maier, R. (2004). Knowledge Management Systems: Information and communication technologies for knowledge management (2nd ed.). Berlin: Springer. McGill, L., Nicol, D., Littlejohn, A., Grierson, H., Juster, N., & Ion, W. J. (2005). Creating an information-rich learning environment to enhance design student learning: challenges and approaches. British Journal of Educational Technology, 36(4), 629–642. doi:10.1111/j.14678535.2005.00540.x Media Awareness Network. (2004). Young Canadians in a Wired World. Phase II. Focus Groups. Retrieved June 1, 2008, from http://www.mediaawareness.ca/english/research/YCWW/phaseII/ upload/YCWWII_Focus_Groups.pdf. Nicol, D. J., & MacLeod, I. A. (2005). Using a Shared Workspace and Wireless Laptops to Improve Collaborative Project Learning in an Engineering Design Class, v44 n4 p459-475 May 2005, 459-475. O’Reilly, T. (2005). Web 2.0: Compact Definition? Retrieved on May 17, 2007 from http://radar. oreilly.com/archives/2005/10/web_20_compact_definition.html Oblinger, D., & Oblinger, J. (2005). Is It Age or IT: First Steps Towards Understanding the Net Generation. In D. Oblinger & J. Oblinger (Eds.) Educating the Net Generation - Educating the Net Generation., Educause 2005, Retrieved on June 12, 2007 from http://www.educause.edu/ educatingthenetgen/

E-Learning with Wikis, Weblogs and Discussion Forums

Otto, H.-U., Kutscher, N., Klein, A., & Iske, S. (2005). Social Inequality in the Virtual Space: How Do Young People Use the Internet? Results from Empirical Research about Online Use Differences and Acquiring Patterns of Young People. Retrieved January 16, 2008, from http://www. kib-bielefeld.de/externelinks2005/Social_Inequality%20KIB.pdf Pountney, R., Parr, S., & Whittaker, V. (2002). Communal Constructivism and Networked Learning: Reflections on a Case Study. In Proceedings of Networked Learning 2002, Berlin. Sliedrecht, the Netherlands: NAISO Academic Press. Köllinger, P. (2002). Report E-Learning in deutschen Unternehmen: Fallstudien, Konzepte, Implementierung (1. Aufl.). Düsseldorf: Symposion. Prensky, M. (2001). Digital Natives, Digital Immigrants. Retrieved on April 13, 2007 from http:// www.marcprensky.com/writing/Prensky -Digital Natives, Digital Immigrants - Part1.pdf and http:// www.marcprensky.com/writing/Prensky -Digital Natives, Digital Immigrants - Part2.pdf Reinmann, G. (2008). Lehren als Wissensarbeit? Persönliches Wissensmanagement mit Weblogs. Information Wissenschaft & Praxis, 59, 9–57. Reinmann, G., & Bianco, T. (2008). Knowledge Blogs zwischen Kompetenz, Autonomie und sozialer Eingebundenheit, from Institut für Medien und Bildungstechnologie: http://www. imb-uni-augsburg.de/medienp-dagogik/biblio/ knowledge-blogs-zwischen-kompetenz-autonomie-und-sozialer-eingebundenheit-0 Resnick, M. (2002). Rethinking learning in the digital age. In G. S. Kirkman, P. K. Cornelius, J. D. Sachs & K. Schwab (Eds.), Global information technology report 2001 - 2002. Readiness for the networked world (pp. 32-37). New York: Oxford Univ. Press.

Richter, A., & Koch, M. (2007). Social Software – Status quo und Zukunft, Technischer Bericht Nr. 2007-01, Fakultät für Informatik, Universität der Bundeswehr München, Retrieved on 01 June, 2007 from http://www.unibw.de/wow5_3/forschung/ social_software/ Scheloske, M. (2008). Die populärsten wissenschaftlichen Blogs. Retrieved January 5, 2009, from http://www.wissenswerkstatt. net/2008/03/19/die-populaersten-wissenschaftlichen-blogs-wissenschaftsblog-charts-032008/ Schneider, U. (2004). Wie funktionieren Communities of Practice. In R. Reinhardt (Ed.), Wissenskommunikation in Organisationen. Methoden, Instrumente, Theorienmit 15 Tabellen (pp. 157156). Berlin: Springer. Schulmeister, R. (2004). Diversität von Studierenden und die Konsequenzen für eLearning. In D. Carstensen & B. Barrios (Eds.), Kommen die digitalen Medien in die Jahre? Medien in der Wissenschaft 29 (p. 133-144). Münster: Waxmann. Schulmeister, R. (2005). Lernplattformen für das virtuelle Lernen: Evaluation und Didaktik (2. Aufl.). München: Oldenbourg. Schulmeister, R. (2008). (Manuscript submitted for publication). Gibt es eine »Net Generation«? [from Universität Hamburg, Zentrum für Hochschul- und Weiterbildung: http://www.zhw.unihamburg.de/pdfs/Schulmeister_Netzgeneration. pdf]. Work (Reading, Mass.). Retrieved June 1, 2008. Seufert, S. (2007). Ne(x)t Generation Learning: Was gibt es Neues über das Lernen? In S. Seufert & D. Euler (Eds.) Ne(x)t Generation Learning: Wikis, Blogs, Mediacasts & Co. - Social Software und Personal Broadcasting auf der Spur, SCIL Arbeitsbericht 12, Universität St. Gallen. (pp. 2-19).

197

E-Learning with Wikis, Weblogs and Discussion Forums

Seybert, H. (2007). Gender differences in the use of computers and the Internet. Retrieved January 15, 2008, from http://epp.eurostat.ec.europa. eu/portal/page?_pageid=1073,46587259&_ dad=portal&_schema=PORTAL&p_product_ code=KS-SF-07-119 Sharma, P., & Fiedler, S. (2004). Introducing technologies and practices for supporting selforganized learning in a hybrid environment. In K. Tochterman & H. Maurer (Eds.), Proceedings of I-Know ‘04, Proceedings of I-Know ‘04 – Graz June 30 - July 2 2004. Know-Center Austria (pp. 543-550). Sharma, P. & Maleyeff, J. (2003). Internet Education: Potential Problems and Solutions, v17 n1 2003, 19–25 Shirky, C. (2003). Social software and the politics of groups. Retrieved on May 3, 2007 from http:// shirky.com/writings/group_politics.html Siemens, G. (2005). Connectivism: Learning as Network-Creation. Retrieved May 16, 2008, from http://www.elearnspace.org/Articles/networks. htm Siemens, G. (2006). Knowing Knowledge. Retrieved May 16, 2008, from http://www.elearnspace.org/KnowingKnowledge_LowRes.pdf

198

Solvie, P. A. (2008). Encouraging Preservice Teachers’ Construction of Knowledge in Reading Methods Courses. The Journal of Literacy and Technology, 9(2), 57–99. Thijssen, T., & Vernooij, F. (2002). Breaking the Boundaries Between Academic Degrees and Lifelong Learning. In T. A. Johannessen (Ed.), Educational innovation in economics and business: Vol. 6. Teaching today - the knowledge of tomorrow (pp. 137-156). Dordrecht: Kluwer Acad. Tully, C. J., & Zerle, C. (2005). Handys und jugendliche Alltagswelt. Medien + Erziehung: Merz. Zeitschrift für Medienpädagogik, 49(3), 11–16. Wagner, C., & Bolloju, N. (2005). Supporting Knowledge Management in Organizations with Conversational Technologies: Discussion Forums, Weblogs, and Wikis. Journal of Database Management, 16(2), 1–8. Wheeler, S., Yeomans, P., & Wheeler, D. (2008). The good, the bad and the wiki: Evaluating student-generated content for collaborative learning. British Journal of Educational Technology, 39(6), 987–995. doi:10.1111/j.14678535.2007.00799.x Williams, J. B. (2004). Exploring the use of blogs as learning spaces in the higher education sector. Australasian Journal of Educational Technology, 20(2), 232–247.

199

Chapter 11

Social Tagging and Learning: The Fuzzy Line between Private and Public Space A. Kohlhase German Centre for Artificial Intelligence, Germany M.Reichel Waterford Institute of Technology, Ireland

AbstrAct Social tagging systems celebrate enormous growth rates on the World Wide Web. This chapter looks at social tagging from an educational perspective, particularly its use for educational environments. The authors identify the processes underlying social tagging from an embodied interaction perspective. The authors will support the thesis that emerging folksonomies are at the base of meaningful interaction processes between user and system and also, at the same time, social processes between groups of people. This chapter argues that the fuzzy line between private and public space plays a crucial role. Moreover, tags represent embodied conceptualizations, whose potential effectiveness for learning will be discussed in this chapter. The authors will provide an example of a learning software for children (Amici, implemented by one of the authors) in which social tagging is used to support sharing in a programming environment to showcase how embodiment of conceptualization as well as constant coupling through moving between private and public space is achieved through tagging in the system.

IntroductIon The field of Human Computer Interaction deals with the natural gap and difficulties in interactions between human beings and machines. From an educational perspective, we are especially interested in its subfield of social computing as it is concerned with meaningful interaction. Interfaces, the contact DOI: 10.4018/978-1-60566-938-0.ch011

points between humans and computers, and the underlying interaction can be designed to be more or less familiar and meaningful for users (Dourish, 2001, p. 100). They can also support interaction between an individual and the computer only, or provide a setting for communication between a larger group of users. This design is especially important in learning systems as the value of social learning is widely known and is decisive for self-steered learning processes using them.

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Social Tagging and Learning

In particular, social computing enables embodiment, i.e. “the property of our engagement with the world that allows us to make it meaningful” (ibid., p. 126). On the one hand, as humans are social beings, it seems to be a good idea to introduce social computing as interaction form. On the other hand, people understand and assign meaning to the world because they are embedded in it and other people are always a part of this world according to a phenomenological point of view. Dourish’s definition of embodied interaction as “creation, manipulation, and sharing of meaning through engaged interaction with artifacts” (ibid.) highlights the role of real world experiences and social practices in the processes of creating meaning. The idea, on which this chapter is based, consists in applying such an embodied interaction perspective to the current success story of social tagging (which we assume is a particular technology within social computing), in order to obtain a working design of a learning environment for children. Tagging systems are recently experiencing considerable interest and acceptance (i.e. usage) rates within the Internet community; see e.g. (Murnane, 2006). Our specific thesis is that this high acceptance is based on its meaningful interaction process with respect to conceptualization. In particular, these systems make use of the fact that they enable an embodiment of concept development, i.e. embodied conceptualizations, and the underlying processes are therefore valuable for individual learning in a social setting. Social tagging systems are particularly interesting for learning as they provide the opportunity of individual as well as shared embodied conceptualizations by offering a place in the Internet users can shape together. Through switching between the private and public conceptualizations the learner is constantly forced to reflect on and recreate meaning. We start off by introducing social tagging and related academic research. Then we will support our thesis by discussing the social tagging phenom-

200

ena with respect to learning. Here, folksonomies will be interpreted as embodied conceptualizations that support understanding and learning processes. In the next section we elaborate on the fuzzy line between private and public space used by social tagging systems. Later on, we concretely present an implementation of such concept embodiment within the integrated development environment Amici for children’s programming.

socIAl tAggIng In contrast to social software, which is the generic term for software that “enables people to rendezvous, connect or collaborate through computer-mediated communication and to form online communities” (Wikipedia, 2001), in social tagging systems users more specifically label system-specific objects like bookmarks (e.g., del. icio.us, see www.delicious.org, or scientifically Connotea, see www.connotea.org) or images (e.g., flickr, see www.flickr.com) with any number of free text tags to organize and share their respective objects. Various definitions for the term ‘tagging’ exist, we refer to Beckett: “Tagging: describing web content using whatever words seem right” (Beckett, 2006). Formally, tagging systems can be described as tripartite networks, i.e. networks “with three different kinds of nodes (the users, the items and the tags) and where the links relate three nodes of different kinds” (Lambiotte & Ausloos, 2005, p. 3). Note that according to this definition tagging systems also connect the user nodes meaning that through using the same tag an implicit connection between different people is made. Therefore the entity of tags of all users in a social tagging system builds not only a continuously developing navigation structure but also a communication setting between people. The navigation structure is called “folksonomy” (Vander Wal, 2004) - short for “folks” and “taxonomy” - because of its quality as a bottom-up organized, decentralized structure.

Social Tagging and Learning

Tags are often visually represented in tag clouds by their frequency of occurrence at a certain point of time (mapped by font size). Other examples of visualizations in tagging systems are tag clusters or recommender systems for similar tags as it can be found in flickrs’ image clustering. According to Marlow et al. (2006) tagging systems can be differentiated by the following dimensions: tagging rights, tagging support, aggregation model, object type, source of material, resource connectivity, and social connectivity. The user interface and the visibility of these dimensions are highly important for the user’s awareness of the other users within the system. Sometimes social connections are visualized and presented as important features (as in flickr), other systems focus on personal storage, management, and retrieval of data (e.g., del.icio.us). Though tagging can be seen as a practical way to annotate content with metadata (since users do it by themselves), it also has problems to overcome. For instance, formalism problems like ambiguity or synonyms as well as syntactical problems are common in tag systems. An example for ambiguous tags or a polysemy (e.g., Golder & Hubermann, 2005) is the tag “apple” that might refer to a fruit as well as to a Macintosh computer. Tags can also represent strongly personal opinions or concepts, e.g., in tags like “boring”, “funny”, or “seen live”. Efficiency issues are discussed and evaluated by Chi and Mytkowicz (2008). Still relatively little research is done on how and why people tag and change their tagging behavior – the social aspects of tagging. According to Marlow et al. (2006) taggers are not only motivated by social goals like self-presentation and expression of opinion, they use tagging systems for different reasons namely: Future retrieval, contribution and sharing, attract attention, play and competition, self presentation, and opinion expression. Empirical evidence exists proving the influence on intrapersonal communication through shared tagging (Sen et. al., 2006). Marlow et

al.

(2006) show that vocabulary in social tagging systems changes over time depending on other users who tag the same item. Users that have contact between each other will more often use common vocabulary than others (Reichel & Schelhowe, 2008). A lot of academic work uses data of commercial tagging systems to study how vocabulary but also how social networks or interest groups evolve and change over time, e.g., (Lee, 2006). The trend of converging vocabularies in social tagging systems is reinforced nowadays by promising results in tag recommendation systems (e.g., Heymann et. al., 2008). From an embodied interaction standpoint, we observe that social tagging systems strongly support the creation of meaning or conceptualization on different levels of abstraction. Dourish (2001, pp. 170 – 175) formulates six design principles for systems to support embodied interaction: 1) 2) 3) 4) 5) 6)

Computation is a medium Meaning arises on multiple levels Users, not designers, create and communicate meaning Users, not designers, manage coupling Embodied technologies participate in the world they represent Embodied interaction turns action into meaning

These principles apply to social tagging systems. For example, let us consider Principle 2, which can be demonstrated via the difference between geotagging and freetext tagging. In geotagging, i.e. tagging an object with a specific location, where the context and the tag semantics are understandable for the public - and directly represent real world experiences - as well as for the machine. In other cases a shared understanding between people is needed to interpret the ambiguous meaning of a tag that is also changing its meaning over time influenced by folksonomies and interaction between users. Principle 4, i.e. “users, not designers, create and communicate

201

Social Tagging and Learning

meaning”, is also clearly fulfilled in tagging systems were the users tag, date, and create content while the designers only provide the functionality to tag objects.

socIAl tAggIng And leArnIng While other equally successful social software applications like blogs and wikis are applied in the educational domain and are referred to as “E-learning 2.0” (Downes, 2005), social tagging is seldom discussed as a new technology with a great potential to support self-steered learning. The European MELT (http://info.melt-project.eu) project represents a distinguished exemption by pioneering the use of user-generated metadata for learning content. Generally, this relative lightness is thought to be in contrast to the rigid taxonomies or ontologies that structure the underlying set of learning objects in learning systems. On a very general level, learning has been shown to be the more efficient the more engaging the interaction between educator (like teacher, book, museum, etc.) and learner is. Learning from and with peers in a common setting is also considered as more successful and motivating in many cases compared to isolated individual learning. We are especially interested in the case, where the educator is a software program and this software program provides the interface to other learners. Social tagging systems are considered as engaging, showcased e.g., by their very high use rates. But why is it so? We want to discuss social tagging from various perspectives to find answers and to make use of it with respect to learning. The learning theories “constructivism” - and for our purpose particularly “constructionism” are successfully used in the design of more recent e-Learning scenarios. Under a constructionist perspective it is striking, that taggers create personal, meaningful, public objects. That is: tags constitute PaPert’s well-known “objects-to-thinkwith” (2000).

202

Tagging can be considered an active practice with which objects (e.g., web documents) and knowledge about them are organized, i.e. put into a subjectively coherent place. thüring et al. (1995) state a close relationship between “coherence and information processing”(p. 58). In particular, coherence of documents means that readers can build a mental model from it that corresponds to their knowledge about the real world (see also Reinmann, 2005). In social tagging systems taggers not only create an inner mental model by assigning tags to information objects in the broadest sense, they actively (help) construct an outer conceptual model as described above. By conveying this coherence to the user, comprehension is supported. The well-known pedagogue Bruner recapitulates in (1970, p. 12), that “if earlier learning is to render later learning easier, it must do so by providing a general picture in terms of which the relations between things encountered earlier and later are made as clear as possible” which directly implicates the advantage of dynamic folksonomies as a result of social tagging in learning processes as embodied conceptualizations. Moreover, these conceptualizations enable users to control the coupling process by “disengaging and reengaging” (Dourish, 2001, p. 139) or analogously “diving in and stepping out” (Ackermann, 2004) as people cannot learn from their experience as long as they are entirely immersed in it. Hence, they fit well for a scenario driving a self-steered learning process. Often the product of the learning process is considered to be knowledge which can be handled or managed afterwards. Therefore, it is not surprising that recently synergies between knowledge management and e-Learning have been investigated intensely (e.g., the workshop LOKMOL (Memmel et al., 2006), an extra journal edition “eLearning und Wissensmanagement” (Reinmann, 2006)), or publications like e.g., (Kohlhase, 2008; Yordanova, 2007; Schmidt, 2005; Bönnighausen & Wilkesmann, 2005). Especially the “user as

Social Tagging and Learning

consumer and producer”-scheme has come up to be a valuable link between them, so the social tagging practice, in which the user produces and consumes tags (containing meaningful content) at the same time, can be made use of in this context. In particular, social tagging provides a “brilliantly lazy” (Miezkowski, 2005) way of constructing personal context. Vander Wal (2006) even likes to call tags “hooks”. People are provided with a means to connect items by placing hooks with their meaning in their own understanding. Moreover, for a long time knowledge management tended to overlook the social component of knowledge and just in the last years (Brown & Duguid, 2003; Davenport & Prusak, 2000), this aspect is seriously taken into account. Now, it is frequently centred around Lave and Wenger’s “Communities of Practice” (1991). In particular, they state that “because the place of knowledge is within a community of practice, questions of learning must be addressed within the developmental cycles of that community.’” (p.100). Even though the tagging process per se is a rather private activity, the awareness about the underlying social system transforms it into a social one where tagging is the common practice. As mentioned above, from a constructionist viewpoint tags themselves are self-constructed, meaningful “objects-to-think-with” (Papert, 2000). They can serve as learning objects ”where the learner is consciously engaged in constructing a public entity, whether it’s a sand castle on the beach or a theory of the universe” (ibid.). That is, whereas from a knowledge management perspective it presents an informal entry door for formal knowledge management procedures like semantic mark-up (Kohlhase, 2006), from a constructionist perspective it generates a social context (see e.g., Golder & Huberman, 2006) - hence providing an opportunity for learning. Moreover, the well-known social constructivist Vygotsky (1978) describes a child’s development as “transformation from an interpersonal process into an intrapersonal one” (p. 57), i.e. learning

takes place in a communication setting between people and thereafter inside the person. Therefore, social tagging presents a well-fitting support for this transformation.

tHe FuzzY lIne between prIVAte And publIc spAce It is well-known that the Web changed people’s attitude towards privacy (e.g., Weinberger, 2002, p.12). But the line between public and private is still relevant for our actions. As we have seen above, this line is also crucial with respect to learning. The mediation between a local and a global context enables and enforces learning and can be interpreted as promenading this line. We argue that one of the strengths of social tagging systems for its use in educational scenarios consists in its fuzzy line between private and public space. VanDer wal (2006) argues that people tag knowledge objects mainly for their own recall - matching Marlow et al.’sFuture retrieval incentive. In particular, the tag text expresses the specific interest this person (with her own identity) has in the object under consideration and determines her vocabulary, thereby providing a defining relationship in form of metadata. This triad of object, identity, and metadata is at the heart of the private tagging approach. The transformation from this to the public one is carried out in VanDer wal’s “dual folksonomy triad” which forms the basis for emergent folksonomies. Here, (personal) conceptualization gaps can be filled with suggestions by community information. The dual or complementary triad to the private one consists in object, community, and metadata. At first sight only identity is replaced by community. But the relationships turned into social, public ones as well: personal interest becomes culture and vocabulary is transformed into terminology. For e-Learning systems, this social quality allows folksonomies to be considered as lightweight support for the formalized and general-

203

Social Tagging and Learning

Figure 1. Dual folksonomy triad (based on Vander Wal, 2006)

ized conceptualizations carried out in taxonomies. In the following we will elaborate on this idea. If we look at social tagging from an embodied interaction perspective, we note that the interaction between either people (shared tagging) or computer and human (tagging) is “embodied” in the emergent folksonomies. That is, latter are embedded in the world and their reality depends on being embedded (Dourish, 2001, p. 18). Therefore, folksonomies represent embodied interactions, thereby allowing meaningful interaction between users of such systems. At the same time they structure and build their resources and hence their knowledge. Note that this knowledge does not only concern the objects they created, but moreover the meaning of their resources for themselves and among the people who also tagged them, i.e. the emerging folksonomy. Moreover, if perceived, it changes the tagger’s world- and self-references, hence it can even be considered an identity-forming process (Marotzky, 1990). These discursive qualities mark folksonomies as embodied conceptualizations and distinguish them from mere objects. Therefore, exactly the fuzzy line between private and public while tagging enables and enhances learning processes. Dynamic folksonomies force the user to constantly go through the coupling process mentioned above, thereby reflecting on the connection between meaning and tag. 204

In particular, because of the private approach towards social tagging systems, they are considered a trustworthy resource for information management affording learning. Concretely, as shown in Figure 2, an actual person “Usery” might have labeled a certain knowledge object “LearningObject1” with the same tag as “User1”. Then it seems probable that User1 and Usery share meaning about this object. Now, if User1 accepts this assumption and realizes that Usery tagged another object “LearningObjectx” with the same text, User1 induces that this other object may be interesting to her as well. We argue that the underlying trust in people (embodied as conceptualizations in folksonomies) engage users to go the next step and not only gather, but also reflect on more and more information – which is a learning process. We like to emphasize that for a user of social tagging systems the line between private and public is rather fuzzy as it is fully dependent on a user’s awareness state. While experienced and frequent users of tagging systems might be fully aware that their tags will be read and interpreted by other users, we assume that novice users are influenced by other users and tags, but they are not aware of this influence. Depending on the user’s awareness the tagging system can be considered a mere space for learning or a social place in which the learning

Social Tagging and Learning

Figure 2. The fuzzy line between private and public

process is embedded. The distinction between such a space and a place where people feel at home is: “Measured space is the same everywhere; that is its essence. Lived space is different everywhere; that is its nature. […] Lived space consists of places.”(Weinberger, 2002, p. 46/47). Many learning environments establish learning spaces with wonderful learning opportunities, but people have difficulties taking them up. What is special about social tagging systems is that they not only generate learning spaces, they offer learning places once the users become aware of the other users in the system. In the following, we showcase how such embodied conceptualizations and such a place for tagging can be created and made use of in a concrete educational scenario.

AmIcI – A tAg-bAsed progrAmmIng enVIronment For cHIldren Since the early 60’s and PaPert’s work on Logo (http:\\el.media.mit.edu\Logo-foundation) and the “Floor turtle” (McNerney, 2004), physical objects and simplified programming languages are used to

teach programming to children. The development in the field continued and a wide variation of construction kits consisting of software and physical hardware exist by now, for instance the commercial “Lego Mindstorms” system. Here, we present “Amici” as a mixed programming language for children based on different construction kits we developed at the University Bremen. For Amici we continued to work on the Open Source Software “Arduino” (Mellis et al., 2007) - a C-based language to program “Atmel ATmegas” (www. atmel.com), which are inexpensive microchips. We extended the integrated development environment (IDE) with visual programming elements, and connected it to a web service to allow tagging and support communication among children using the software. These programmable physical objects (socalled programmable bricks) are necessarily embedded in the ‘real’ world as they use sensor inputs to get values from their environment and act through actuators like motors as output devices in the world; they serve as PaPert’s objects-to-thinkwith (2000) as well as tangible representations of programming code. In various robotics workshops with children aged between eight and thirteen using different

205

Social Tagging and Learning

Figure 3. The Amici interface: Tagging commands or sharing one’s project

programming software we observed that children had problems to understand the connection between the physical object and its representation in the IDE. In particular, the commands for the bricks in the programming language as well as the IDE were often too abstract and therefore frequently meaningless to children without former knowledge in programming. In order to address this problem in Amici, we extended it by social tagging features. Now, children can use tags to annotate commands (and their representing icons). This enables the creation of a connection between the real world and the IDE that makes their interaction with the IDE meaningful and embodied. Also often discussed in constructionism is the value of sharing experiences, interest, and codes. This can easily be done by using webbased services utilizing the tags as navigational elements. Figure 3 shows an example how to achieve this kind of embodiment in Amici. An icon represents a command as you can see with the “on/An” command. When a child adds tags to the command, then they appear in form of a tag cloud whenever the mouse moves over the icon (e.g., the term “auto”). Here as well as in other tagging systems, the font-size represents frequency of occurrence of a tag. This visualization of the respective, collaboratively generated folksonomy lets her understand her tagging action in terms of others.

206

She may wonder why someone tagged it “light sensor” and conclude that this is a function she had never thought of, but can make use of now. Tagging also serves to categorize and retrieve projects that are shared among different children via web-interface. In order to see other users’ projects, children can use a search window that shows a folksonomy of tags for the different projects. Tags can be of very different nature e.g., giving examples of how to use the command in a syntactical correct way like “if sensora > 56 [a,on]” in Figure 4, connecting to physical object for example the kind of sensor used as in “light sensor”, or referring to the behaviour that is the goal of the program (as you can see in the tag “line” used for a robot that is able to follow a line).

eVAluAtIon To reconsider: we argued that tags can serve as embodied conceptualizations and that they are public objects, although the publicity depends on the user’s awareness state. To support these theses we evaluated Amici by deploying different qualitative methods. When this research was conducted our database consisted of 126 users, fifty-five tags and thirty projects. The tag “LED” was the most popular tag with five projects; “driving” was used three

Social Tagging and Learning

Figure 4. From icon to code

times to tag projects. Ten projects were tagged with more than one tag. Blocks were tagged with twenty-one distinct tags. The numbers are small compared to popular social tagging systems but significant for the special domain of programming languages for children. We gained first insights on how children use the software by testing Amici in two workshops with 20 participants both male and female aged between nine and fourteen. The workshops were conducted as weeklong activities (40 hours), in which the children used the software. At the end of the workshop they uploaded and tagged their data with the help of a tutor. In the following part we take a look at two children who used the software in the experimental workshops. User “Bob” took part in a workshop where children were supposed to create their own smart objects - in most cases these were interactive bags. Bob uploaded and tagged his project, consisting of his source code, a photo, and a description of what he did during the workshop. He also tagged some commands of the programming environment. In particular, he used the following tags: • • •

“LED” for the command “On”, “sensor” for the command “If”, and “length of the break” for the command “Delay”.

The whole project he uploaded he tagged with “bag”, “spooky”, and “lights”. Another participant, “Marcel”, was working on a robot that played a role in a narrative. He

uploaded a project and tagged it with “mystery” while for commands he used: • • •

“Motor on” for “On”, “driving” for “On”, and “driving” for the command “Onfor”.

Looking at this tagging behaviour with Marlow et al.’s taxonomy of user incentives in mind both children used tagging for “Contribution and Sharing” (2006). When tagging commands both users referred to hardware parts and functions of the real world. The tags for the projects themselves are different and refer to the conceptual context and the personal meaning it had for the child. The tag “spooky” is connected to Bob’s design concept of the bag that glows in the dark and has attached blinking LEDs. Marcel’s “mystery” tag is similar. His robot checked whether people could solve a riddle within the adventure story they performed. The example shows how tags can add meaning to a command (in our cases the representations of “real world” hardware) in the personal context of the child through the connection between the command and the real world experience. The interaction with the programming language and IDE becomes embodied in the real world and personally meaningful. Our tagging examples show this kind of embodiment - however, the connection to the real world takes place on a different level of abstraction as you can see from the difference between the tag “sensor” and “mystery”.

207

Social Tagging and Learning

Whenever the users tag an object each personal tag becomes a part of Amici’s public folksonomy. The child had to reflect on her personal project during the tagging process and was confronted with various tags when searching for the right command. This confrontation forced the child to reflect on the different tags from other users and interpret their meaning – earlier we referred to this process as coupling process or “diving in and stepping out”. In addition to the workshop’s observations of the tagging behaviour we conducted in-depth interviews with the taggers and analyzed and coded their statements to get further impressions on the reflection process. We found evidence to support the thesis that the awareness of other users in the system influences the individual’s tagging behaviour. For example, even a novice tagger stated: If you tagged it yourself, you of course know, but if somebody looks at it later, then she think she can only use a motor. Or something like this. Clearly, this tagger is aware of the other users in the system and based his decision of which tag to use also on his assumption how others users understand a tag. His conclusion for a tagging system’s design therefore is: I would to it the following way: the one who created the project can make one [tag] for everybody, but everybody can make one for herself. And is allowed to change it, but only for herself. For an expert user the publicity of a tag can be even more important as for example for the following learner: Sometime I have the problem that I want to use tags that do not fit in to my identity, you know. Since I share my links on my blog I really think about it. The tagger is concerned about self-presentation through his tagging behaviour. On the other hand,

208

he also reported about using other people’s tags as navigation. When a user has the same tag I sometimes look at their user profiles and when I see that he used the tag often and has a more advanced classification of the topic I start to look at his bookmarks. You can see from this examples how the fuzzy line between private and public spaces influences the way learners use tags and also, perceive the learning environment. For the children and young people we interviewed, the software was clearly perceived as social place rather than a place. Even novice taggers were aware that there was a public level when they entered tags and thought about how other users would understand whatever tag they used. This is what we regard as the important phase of stepping out in the learning process.

conclusIon The challenge at the very heart of the design of e-Learning environments with the constructionist learning theory in mind is captured by “Users, not designers, create and communicate meaning” (Dourish, 2001). Hence, digital artefacts need to be affordable, i.e. flexible and able to adapt to different contexts. We argued that the fuzzy line between the private and the public - which is enabled by social tagging - represents (affordable) opportunities for social learning. With Amici we presented an integrated programming environment that supports these learning processes via social tagging and folksonomies as embodied conceptualizations. We are still in a prototyping phase using the system. We backed up our thesis with data from qualitative analysis. As soon as a substantial amount of learners tag commands and projects the data can be analyzed quantitatively as well, as it is the case with data from commercial systems in respect to social learning to further back up our thesis and provide empirical evidence.

Social Tagging and Learning

The data showed how the line between private and public influenced the user’s choice of tags but also the reflection and conceptualization process. The environment serves as a social place in which users are aware of other users in the system rather then as a mere space for learning or programming. This difference is what is special about social tagging systems: They not only generate learning spaces, they offer learning places. In particular, they offer a fuzzy line between public and private, turning a public space into a private place. We conclude that social tagging systems form a successful example for a design affording selfsteered creation and communication of meaning via embodied conceptualizations in a holistic constructionist scenario with tangible objects-tothink-with at its base.

Brown, J. S., & Duguid, P. (2000). The Social Life of Information. Harvard Business School Press.

Acknowledgment

Golder, S. A., & Huberman, B. A. (2005). The Structure of Collaborative Tagging Systems. Retrieved fromn http://arxiv.org/abs/ cs.DL/0508082

Thanks to Stephan Plassmeier for his work on Amici and all participating children and young people.

reFerences Ackermann, E. K. (2004). Constructing Knowledge and Transforming the World. In M. Tokoro & L.Steels (Eds.), A learning zone of one’s own: Sharing representations and flow in collaborative learning environments (Vol. 1, pp. 15–37). IOS Press. Beckett, D. (2006). Semantics Through the Tag. XTech 2006 “Building Web 2.0”. Bönnighausen, M., & Wilkesmann, U. (2005). E-Learning meets Wissensmanagement: Wie Qualifikations- und Kompetenzentwicklung in Betrieben zugleich erfolgen. Retrieved from http:// www.diezeitschrift.de.

Bruner, J. (1966, 1970). The Process of Education. Harvard University Press. Chi, E. H., & Mytkowicz, T. (2008). Understanding the efficiency of social tagging systems using information theory. In HT ‘08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia (pp. 81-88). New York: ACM. Davenport, T. H., & Prusak, L. (1998, 2000). Working Knowledge. Harvard Business School Press. Dourish, P. (2003). Where the Action Is: The Foundations of embodied interaction. MIT Press. Downes, S. (2005). e-Learning 2.0. eLearn Magazine. Retrieved from http://elearnmag.org/ subpage.cfm?section=articles\&article=29-1

Gordon-Murnane, L. (2006). Social Bookmarking, Folksonomies, and Web 2.0 Tools, 14(2), 26-38. Hammond, T., Hannay, T., Lund, B., & Scott, J. (2005). Social Bookmarking Tools – A Case Study. D-Lib Magazine, 11(4). doi:10.1045/ april2005-hammond Heymann, P., Ramage, D., & Garcia-Molina, H. (2008). Social tag prediction. In SIGIR ‘08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 531-538). New York: ACM. Kohlhase, A. (2006). The User as Prisoner: How the Dilemma Might Dissolve. See (Memmel et al., 2006), pp. 26 – 31.

209

Social Tagging and Learning

Kohlhase, A. (2008). Semantic Interaction Design: Composing Knowledge with CPoint. Dissertation thesis. Retrieved from http://kwarc.info/ako/pubs/ AKo_Promo.pdf.

McNerney, T. S. (2004). From Turtles to Tangible Programming Bricks: Explorations in Physical Language Design. Personal and Ubiquitous Computing, 8(5). doi:10.1007/s00779-004-0295-6

Lambiotte, R., & Ausloos, M. (2005).Collaborative Tagging as a Tripartite Network. arXiv:cs. DS/0512090 v2.

Mellis, D. A., et al. (2007): Arduino: An Open Electronics Prototyping Platform. alt.chi section of the CHI 2007conference in San Jose (CA).

Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation (Learning in Doing: Social, Cognitive and Computational Perspectives). Cambridge University Press.

Memmel, M., Ras, E., & Weibelzahl, S. (2006). 2nd Workshop on Learner Oriented Knowledge Management & KM Oriented e-Learning (LOKMOL). Retrieved from http://cnm.open.ac.uk/ projects/ectel06/pdfs/ECTEL06WS68d.pdf

Lee, K. J. (2006). What Goes Around Comes Around: An Analysis of del.icio.us as Social Space. Computer Supported Cooperative Work, 2006, CSCW ‘06. 20th Anniversary Conference (pp. 181-190). Lego(2006).RetrievedfromOnlineathttp://shop.lego. com/product.asp?p=B8527&cn=55&d=11&t=5. Marlow, C., Naaman, M., Boyd, D., & Davis, M. (2006). HT06, tagging paper, taxonomy, Flickr, academic article, to read. In Proceedings of the seventeenth conference on Hypertext and Hypermedia (pp. 31–40). Marotzky, W. (1990). Entwicklung einer strukturellen Bildungstheorie: Biografie-theoretische Auslegung von Bildungsprozessen in hochkomplexen Gesellschaften. Weinheim: Deutscher Studienverlag.

210

Miezkowski, K. (2005). Steal this bookmark! Retrieved September 15, 2006 from http://dir. salon.com/story/tech/feature/2005/02/08/tagging/ index_np.html Papert, S. (2000). What’s the big idea? Towards a pedagogy of idea power. IBM Systems Journal, 39(3-4), 720–729. Reichel, M., & Schelhowe, H. (2008). Between “Instructions” and “Diy”; Tagging in Learning Communities. Herczeg, Michael; Kindsmüller, Martin Christof (Hrsg): Mensch&Computer 2008. München: Oldenbourg 2008, p.307-315. Reinmann, G. (Ed.). (2007). E-Learning und Wissensmanagement. Zeitschrift

211

Chapter 12

Some Key Success Factors in Web-Based Corporate Training in Brazil: A Multiple Case Study

Luiz Antonio Joia Brazilian School of Public and Business Administration of Getulio Vargas Foundation and Rio de Janeiro State University, Brazil Mário Figueiredo Costa Brazilian School of Public and Business Administration of Getulio Vargas Foundation, Brazil

AbstrAct Brazilian companies are increasingly turning to Web-based corporate training by virtue of the fact that they need to train their employees within tight budget constraints in a country of continental dimensions. However, most of these companies do not know what the critical success factors in these endeavors are. Therefore, this article seeks to investigate some key success factors associated with such digital enterprises. In order to achieve this, the multiple case study method is used, whereby two cases, both conducted within the same Brazilian company, leading to opposite outcomes—a success and a failure—are analyzed in depth. The conclusions reached in this article were that goal orientation, source of motivation, and metacognitive support were the three critical dimensions in these two Web-based corporate training programs under analysis. Lastly, some managerial implications of these results are outlined.

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Some Key Success Factors in Web-Based Corporate Training in Brazil

IntroductIon Nowadays, market dynamics are becoming increasingly intense due to new strategic orientations and the pressing need for businesses to adapt themselves to new business models and regulatory frameworks. For this reason, it is of paramount importance for companies to become agile, as well as achieve low costs and high returns on investment associated with their employee training programs. On the other hand, the increasing speed of obsolescence in training content, plus the high costs of face-to-face training programs, as well as the logistic hurdles linked with their deployment—mainly in firms operating in countries of continental dimensions (like Brazil)—are major barriers to the implementation of such face-to-face training programs. Another aspect is that information technology (IT) is changing the way people search, locate, access, and retrieve available knowledge, as well as altering the learning process and the way training is conducted (Hodgins, 2000). While employees take charge of their own learning process and professional development, the employers face new challenges in training and retaining teams with in-depth knowledge about their business (Hodgins, 2000). It is in this context of rapid change, with massive information loads and the search for training programs, that Web-based corporate distance training comes into its own. Information technology can solve most of the problems associated with the hitherto existing employee training undertakings, enabling the implementation of corporate distance training programs (Rosemberg, 2001). Despite being a key factor for developing feasible training programs, information technology per se is not a guarantee of success for these endeavors. Most of the time, it must be linked to pedagogical and didactical issues related to them. The specific characteristics of each training program must be analyzed in depth and considered as

212

relevant as the implementation costs throughout the decision-making process (Clark, 1983). The structuring of Web-based training programs is no easy task, as according to several scholars, various critical success factors must be taken into consideration (see, for instance, Carey, Mitchell, Peerenboom, & Lytwyn, 1998; Penuel & Roschelle, 1999). In line with this, this article seeks to investigate what these critical factors are through the analysis of two distinct Web-based training programs conducted within the same Brazilian company. Hence, the research question in this article is: “What are the critical success factors associated with the implementation of these two Web-based corporate training programs?” In order to achieve this goal, this work is structured as follows. First, the first section addresses the theoretical references used in this article. Then, the research method is outlined. After that, the two cases under analysis are described, and in the next section, the results accrued from them are compared. In the last section, the authors present some final comments.

theoretical references According to Wilhelmsem (2005) and Huitt and Hummel (2003), there are four knowledge fields associated with distance training, namely psychology, social science, pedagogy, and computer science. Figure 1 depicts how these four areas are interlinked, pointing to the crucial importance of social science and psychology—and learning theories—as the theoretical support for the areas of pedagogy and computer science, in order to enable the development of an instructional design aiming to apply information technology in education. Behaviorism, cognitivism, and constructivism are all theories addressing the learning process, as well as the nature of knowledge and its main facets (Wilhelmsem, 2005). For behaviorists, knowledge is characterized as a passive process. Learning

Some Key Success Factors in Web-Based Corporate Training in Brazil

Figure 1. The creation process of instructional design in distance training (adapted from Wilhelmsem, 2005) psYcHologY

computer scIence InFormAtIon tecHnologY In educAtIon

leArnIng tHeorIes

socIAl scIence

is explained without reference to the mental processes, as its focus is on observable behavior and in the way individuals adapt themselves to the environment. For cognitivists, the learning process molds the individual’s mental construction. Finally, for constructivists, knowledge is seen as relative and socially built, varying according to time and space (Wilhelmsem, 2005).

behaviorism For behaviorists, psychology is the science of behavior, rather than the science of the mind. Behavior is correlated to external factors—the environment—instead of internal factors (Campos, 1982). The theory of classic conditioning, developed by Ivan Petrovich Pavlov (1849–1939), has a psychological basis, as learning is developed via the linkage between situation, stimulus, and reaction. As the organism stores a repository of answers, trends, and reactions, it is able to act and produce an answer through an adequate natural and not conditioned stimulus (Campos, 1982). Edward Lee Thorndike (1874-1949), in his connectionist learning theory, pointed out that the connection arises from the association between the impression of the senses and the impulses for action, namely from the linkage between the stimulus and the corresponding answer (Reinemeyer, 1999). Thorndike defined psychology as the science that studies the intellect, the behavior, and the characteristics of animals as well as hu-

InstructIonAl desIgn

pedAgogY

man beings. Human education is concentrated on the emergence of certain changes in intellect, characteristics, and behavior in a composition based on four topics: objective, content, meaning, and method (Thorndike, 1911). John B. Watson (1878–1958) presented a work in 1912 that is the basis for the development of behaviorism. He stated that humans and animals are complex machines that react to situations based on conditioned experiences, rather than on hereditary factors (Watson, 1929). For Skinner (1904–1990), man is neutral and passive with behavior that can be described from a mechanistic standpoint. In his theory of operant conditioning, it is important that the stimulus follows the answer, which is named “reinforcement” by Skinner (Graham, 2005). Skinner stated that tools must be used to control the human learning process better. Thus, he recommended the use of programmed teaching, namely a process that allows the students to go further via a string of stages developed according to the student’s own pace and reinforced immediately after each stage (Campos, 1982).

cognitivism In order to explain the development of cognitive processes, Piaget (1896–1980) revealed how organisms adapt themselves to their environments. This adaptation to the environment is controlled by the mental organization, namely schemes,

213

Some Key Success Factors in Web-Based Corporate Training in Brazil

which individuals use to represent a word and an established action. This adaptation is ruled by biological impulses in order to achieve equilibrium between the schemes and the environment (Huitt & Hummel, 2003). Through his psychology of development of the intelligence, Piaget established a theory about the four stages whereby individuals—from birth to adulthood—acquire skills for logical reasoning. The rationale in this theory is that thinking is not the automatic outcome of reflections and intuitions, but rather a flexible operation developed by processes of trial and error (Piaget, 1964). On the other hand, according to Lev Vygotsky (1896–1934), the impact of the external world on individuals’ internal world, based on their interaction with reality, must be examined. Thus, social interaction is of paramount importance in a person’s cognitive development. According to Vygotsky’s principles, the origins of the changes that occur in these persons lie in their interaction with society, culture, and their particular story, that is, in the social theory of learning (Huitt & Hummel, 2003). To the cognitivists, the thinking and previous knowledge of the students must be taken into account in the development of a course syllabus. Moreover, for them, the students move to new learning objectives in an increasing order of complexity, from the simpler to the more complex (Campos, Rocha, & Campos, 1998).

constructivism Studies in constructivist theory started with Jean Piaget, based on both an epistemological focus and an interdisciplinary perspective. His main question was: “How does one go from less developed knowledge to more developed knowledge?” A theory was then elaborated addressing the cognitive mechanisms of human beings (Huitt & Hummel, 2003). For Piaget, intelligence is an active and organized assimilation process. When exposed to

214

a challenge or stimulus, the individual experiences a disturbance in intellectual equilibrium, becomes curious, defied, and motivated. Then, through assimilation and accommodation, the individual recovers the former equilibrium, which is always dynamic as enabled by physical and/or mental actions. This equilibrium process is the central concept in Piaget’s constructivist theory (Wilhemsen, 2005). Lev Vygostky is another person who effectively contributed to building the conceptual basis of constructivism. He stressed the importance of language as a thinking tool, enabling the restructuring of several psychological functions, such as memory, attention, and the development of concepts. In this way, language acts decisively on the structure of thinking and is the basic tool for the construction of knowledge (Huitt, 2003). Vygotsky (1935) also argued that knowledge is built from the action of individuals vis-à-vis reality and that individuals are not just active persons but also interactive and social beings, as they develop their knowledge, which is in turn developed by intra and interpersonal relationships. This process goes from a social dimension based on interpersonal links to an internal dimension based on intrapersonal links, such that the subjects effectively take part in the construction of their own culture and story, which changes them while also provoking changes in the individuals who interact with them. Based on Piaget’s research, Bruner (1985) argued that learning is an active process, whereby students build new ideas and concepts based on their own knowledge, select and transform information, develop hypotheses, and make decisions in order to establish their own cognitive structure (Huitt, 2003).

teaching and learning theories The constructivist approach requires that the aims and targets of training must be clearly defined, as well as the process for measuring the outcomes

Some Key Success Factors in Web-Based Corporate Training in Brazil

accrued from it (Mergel, 2005). On the other hand, a course based on the cognitivist model must consider the lessons accrued from the student’s previous knowledge so as to achieve new learning objectives. This paradigm does not consider that the students will have the same prior experiences they once had, nor that they will learn in the same way they once did (Houser, 2005). There are concepts that are common to both constructivism and cognitivism, since both are based on the assumption that new knowledge is built upon prior knowledge. For the constructivists, the student rather than the content, program format, and instructor is emphasized in a learning process. The latter is no longer the center of the learning process, as the students play this role (Wilson, Jonassen, & Cole, 1993). In constructivism, knowledge is perceived as relative—nothing is absolute—varying according to time and space (Wilhelmsen, Stein, & Øyvind, 2005). According to Jonassen (1981), the cognitive tools are mental and computational devices that support, lead, and broaden the thinking process. In other words, the mind is in charge of knowledge acquisition via a linked cognitive process, and information plays the role of a stimulus that is perceived and recorded in the mind.

Instructional design Instructional design is the association of distinct learning theories with the development of pedagogical content, conveying to a certain training program. Its main objective is the application of learning theories in order to set up a concrete path that enables learning processes (Wilson et al., 1993). Rieber (1992) argues that there is no conflict between instructivism—based on behaviorism—and constructivism, such that training can incorporate features accrued from both paradigms without conflict. Rieber (1992) also states that the basic principles for the development of instructional design should include setting up the challenge to be proposed to the

student correctly, making it neither too easy nor too difficult. It should also offer elements of surprise in order to arouse the curiosity of the student, as well as provide a context that intrinsically supports motivation and autoregulation of the learning process. Malone (1999) argues that three characteristics increase the motivation and autoregulation of the student. The first is to provide a context whereby the students can enter into dialogue with their imagination and develop a personal state of fascination and intrigue. The second is to develop a context that arouses the students’ curiosity, and the third is to set up a pattern that allows the students to travel from the “known to the unknown.”

Assessment of web-based corporate training programs In many cases, the departments of a company need to develop corporate distance training programs via the Web. More often than not, these programs are oriented by technical imperatives, namely the obligation to use Internet technology. In some organizations, the Web-based training programs were designed specifically to justify the costs of the corporate intranet (Powell, 2000). However, the use of technology per se cannot be considered a justification for implementing any kind of training, as stated by Rosemberg (2001), Bregman and Jacobsen (2000), Bates (1995), and Kay (1970), to name a few. In order to develop a comparative analysis between Web-based training programs, it is necessary to adopt a specific framework. In this article, the model proposed by Reeves and Reeves (1997) will be applied to identify and evaluate the distinct dimensions involved in Web-based training as explained later. This model has applications in the research, implementation, and evaluation of Web-based training programs such as those analyzed in this article.

215

Some Key Success Factors in Web-Based Corporate Training in Brazil

It is important to stress that the model developed by Reeves and Reeves (1997) does not propose to evaluate either the outcome of a Webbased training program, or its success or failure. Indeed, the overriding purpose of this model is to assess the different aspects and facets of this kind of program (Reeves, 1997). The adopted model includes 10 dimensions of interactive learning on the World Wide Web, namely: (1) pedagogical philosophy, (2) learning theory, (3) goal orientation, (4) task orientation, (5) source of motivation, (6) teacher role, (7) metacognitive support, (8) collaborative learning, (9) cultural sensitivity, and (10) structural flexibility. Each of the 10 dimensions in this model is presented as a two-ended continuum with contrasting values at either end. Needless to say, the world is rarely dichotomous, and there is more complexity involved in training than any of these dimensions suggest. However, the individual dimensions themselves are not as important as the interplay among the 10 dimensions that represent the instructional designs of various Web-based training programs. These dimensions are detailed below. a.

Pedagogical Philosophy (Instructivist Constructivist)

The debate over instructivist and constructivist approaches to teaching and learning persists to this day (Kafai & Resnick, 1996). Instructivists stress the importance of objectives that exist separately from the learner. Little emphasis is placed on learners themselves, who are viewed as passive recipients of instructions or treated as empty vessels to be filled with learning (Sherry, 1996). By contrast, constructivists emphasize the primacy of the learner’s intentions, experience, and cognitive strategies. According to constructivists, learners construct different cognitive structures based upon their previous knowledge and what they experience in different learning environments. It

216

is of paramount importance for constructivists that learning environments be as rich and diverse as possible. Instead of an empty vessel, the learner is regarded as an individual replete with pre-existing motivations, experiences, aptitudes, and knowledge. Tasks to be accomplished and problems to be solved must have personal relevance to the learner. The constructivists believe that what we know is constructed—both individually and socially—based on prior experience. b.

Learning Theory (Behavioral Cognitive)

According to behaviorists, the critical factor in learning is observable behavior, and instruction involves shaping desirable behavior through the arrangement of stimuli, responses, feedback, and reinforcement. A stimulus is provided (e.g., a short presentation of content), then a response is elicited—often via a question. Feedback is given as to the accuracy of the response, and positive reinforcement is given for accurate responses. Inaccurate responses result in a repetition of the original stimulus, and the cycle begins again. Cognitive psychologists place more emphasis on internal mental states than on behavior. Cognitive taxonomy of internal learning states includes simple propositions, schema, rules, skills, mental models, and so forth. They claim that a variety of strategies—including memorization, direct instruction, deduction, drill and practice, and induction—are required in any learning environment, depending upon the type of knowledge to be created by the learner. c.

Goal Orientation (Sharp Broad)

The goals for education and training can range from sharply focused goals to general higher order goals. Hence, the goal orientation of Web-based training systems varies in degree of focus from sharp to broad (Cole, 1992).

Some Key Success Factors in Web-Based Corporate Training in Brazil

d.

Task Orientation (Academic Authentic)

The context of learning is enormously important to adults (Giardina, Oubenaissa, & Bhattacharya, 2002; Merriam, 1993). Academic design depends heavily on having the learners carry out traditional academic exercises, whereas authentic design engages adults in practical activities such as preparing job applications, thereby situating practice and feedback within realistic scenarios. If knowledge, skills, and attitudes are learned in a practical context, they will be used in that context in similar situations. e.

Source of Motivation (Extrinsic Intrinsic)

Motivation is a primary factor in any theory or model of learning (Amabile, 1993). All new educational technology promises to be intrinsically motivating. This dimension ranges from extrinsic (i.e., outside the learning environment) to intrinsic (i.e., integral to the learning environment). Motivation instruction is intrinsically elusive, irrespective of the delivery system. f.

Teacher Role (Didactic Facilitative)

The teacher role continuum ranges from didactic to facilitative. In the former role, the teacher presents information and asks learners to memorize information and recall it later in tests. The latter role assigns cognitive responsibility to the learners, for them to be responsible for recognizing and judging patterns of information, organizing data, constructing alternative perspectives, and presenting new knowledge in meaningful ways with the teachers as tutors of this process. g.

Metacognitive Support (Unsupported Integrated)

Metacognition refers to a learner’s awareness of objectives, ability to plan and evaluate learning strategies, and capacity to monitor progress and adjust learning behavior to accommodate needs (Flavell, 1979). The metacognitive support dimension is unsupported at one end of the continuum and integrated at the other. Recapitulation of the students’ strategies at any point in the problemsolving process, as well as construction of Webbased portfolios (Nevado, Basso, & Menezes, 2004), are examples of how support for reflection, and metacognition might be provided in Webbased corporate training. h.

Collaborative Learning Strategies (Unsupported Integral)

The collaborative learning dimension ranges from a complete lack of support for collaboration to the inclusion of collaborative learning as an integral feature. Cooperative and collaborative learning refers to instructional methods in which learners work together in pairs or small groups to accomplish shared goals (Kirschner, Strijbos, Karel Kreijns, & Beers, 2004). i.

Cultural Sensitivity (Insensitive Respectful)

All instructional systems have cultural implications. In an insensitive approach the training is developed irrespective of the culture and diversity of the learners it is intended to address. On the other hand, a respectful approach is based on the diversity in the populations in which the system will be used so that the overall learning environment is enhanced. It is unlikely that Webbased training can be designed to adapt to every cultural norm, but sites should be designed to be as culturally sensitive as possible (Brown & Voltz, 2005). j.

Structural Flexibility (Fixed Open)

217

Some Key Success Factors in Web-Based Corporate Training in Brazil

“Fixed” systems, still dominant in education, are usually limited to specific places, for example, a classroom or laboratory, at specific times, for example, 50-minute class period. Irrespective of time and/or location constraints the learner can use “open” systems. The World Wide Web pro-

vides opportunities for more asynchronous (open) learning, although some Web-based learning tools are temporally fixed (synchronous), such as chats, video-conferences, and so forth. Table 1 depicts the 10 dimensions defined for analyzing Web-based training programs, as

Table 1. Dimensions to evaluate the characteristics of Web-based distance training (adapted from Martin, 1998, and Joia, 2001) 0← Instructivist Knowledge is imparted by the instructor

Pedagogical Philosophy 0—10

→10 Constructivist Knowledge is constructed—both individually and socially—by the students

Behavioral Emphasis on observable behavior

Learning Theory 0–10

Cognitive Emphasis on internal mental states

Sharp Direct instruction focusing on desired behavior

Goal Orientation 0–10

Broad Simulations encompassing more than just a solution for the problem

Academic Emphasis on traditional academic exercises

Task Orientation 0–10

Authentic Emphasis on practical activities

Extrinsic Motivation lies outside the learning environment Didactic The teacher is considered to be a knowledge repository

218

Dimension

Source of Motivation 0–10

Intrinsic Motivation lies in the student and the learning environment

Teacher Role 0–10

Facilitative The teacher is a mentor and tutor for the students

Unsupported There are no student progress tracking mechanisms or adjustments to individual needs

Metacognitive Support 0–10

Integrated Student progress tracking mechanisms are implemented, as well as adjustments to individual needs

Unsupported Students work alone

Collaborative Learning 0–10

Integrated Students work together in pairs or in small groups

Insensitive Training is prepared regardless of the culture and diversity of the learners it seeks to address

Cultural Sensitivity 0–10

Respectful Training is based on the diversity of the populations where the system will be used

Fixed Program limited to specific places at specific times

Structural Flexibility 0–10

Open Program independent of time and/or location constraints

Some Key Success Factors in Web-Based Corporate Training in Brazil

supported by Reeves and Reeves (1997). For each dimension (in the central column of the table), the opposite poles of the adopted ratio scale (ranging from 0 to 10) are described and their meanings explained.

research method The multiple case study method as described by Yin (1994) was adopted in this research, in which two Web-based distance-training programs developed within the same Brazilian company were analyzed in-depth. Case studies are particularly suitable for answering “how” and “why” questions, and are ideal for generating and building theory in an area where little data or theory exists (Yin, 1994), as in this knowledge field. It also enables researchers to use “controlled opportunism” to respond flexibly to new discoveries made while collecting new data (Eisenhardt, 1994), as was done and is presented below in this work. Notwithstanding having a major exploratory facet, this study also presents explanatory characteristics, as a causal relationship between the dimensions of the programs analyzed (Reeves & Reeves, 1997) and the respective outcomes are pursued. Yin (1994, p. 46) argues that in the multiple case study method, each case must be carefully selected, so as to generate either similar or opposing results. In line with this, a Brazilian company was chosen (the identity of which is confidential), and two Web-based training programs it developed and staged were selected, each one generating contrasting final results. The first case—hereinafter referred to as Program A—was considered a success as it achieved its main objectives. The second case—hereinafter named Program B—developed by the same company, was considered a failure, as most of its targets were not accomplished. In order to comply with Yin’s (1994) ideas necessary to validate this case study method, the following four issues were cautiously taken into consideration, namely: construction validity, internal validity, external validity, and reliability, as revealed below.

construct Validity In order to validate the “key success factors in Web-based corporate training” construct, multiple data sources were used, and also a chain of evidence related to research questions was pursued. The existing records associated with these projects were analyzed in-depth. The managers of both programs were located in the company and interviewed—there was a single manager for the first case (Program A) and two managers for the second case (Program B). Questionnaires were circulated among the training users. These questionnaires sought to establish their perceptions relating to the 10 dimensions proposed by the Reeves and Reeves (1997) model. In addition to this, the users also revealed their perceptions about the rate of accomplishment of objectives of each program vis-à-vis the actual objectives proposed for the programs in their initial designs. In line with the ideas proposed by Reeves and Reeves (1997), the minimum value of the scale (0) indicates that a dimension is fully aligned with the instructivist/behaviorist paradigm, whereas the maximum value of the same scale (10) proves that a dimension is fully aligned with the constructivist/cognitivist paradigm (Joia, 2001). Moreover, the maximum value of the scale (10) associated with the “accomplishment of training objectives” indicates user perception of complete success for the training program, whereas the minimum value (0) points to user perception of total failure for the training program. The aforementioned questionnaires were answered by 32 users of the first case analyzed (Program A) and 31 users of the second case (Program B).

Internal Validity With a clear exploratory approach, this work addressed some explanatory elements used to verify the possible causal effects between the dimensions of the theoretical model and the training outcomes.

219

Some Key Success Factors in Web-Based Corporate Training in Brazil

This was done to support the internal validity of this research, in accordance with the recommendations of Morra and Friedlander (1999). The first analysis conducted sought to compare user perceptions about the rate of accomplishment of objectives for the two programs, in order to verify whether or not the respective average of these grades could be considered statistically distinct. Once the difference between user perceptions regarding the rate of accomplishment of objectives for each program was recorded, a statistical comparison of user perception averages associated with each dimension of the theoretical model applied was performed. Since it had already been seen that the two programs presented statistical differences with respect to their outcomes, namely success and failure, the dimensions that did not present statistically significant differences within the two programs were discarded as not being critical success factors. Thus, from this prior comparison, two dimensions of the Reeves and Reeves (1997) model were removed, leaving eight dimensions to be analyzed further. In order to achieve this, a multivariate linear regression was used, where the rate of accomplishment of training objectives was the dependent variable while the grades given by the users to each of the eight remaining dimensions of the model served as the independent variables. The significance level of each coefficient associated with these dimensions (independent variables) was then calculated and analyzed, while the dimensions whose coefficients did not present evidence of linear correlation with the dependent variable (accomplishment of objectives) were discarded. The above procedure highlighted three dimensions, which could be considered critical success factors for the training programs analyzed. Lastly, as a final quantitative validation, a simple linear regression was performed on each dimension removed from the study for not being related to the accomplishment of training objectives. The simple regressions supported that these

220

factors did not possess a fair linear correlation with the objectives of the training programs.

external Validity The external validity addresses whether or not the findings accrued from this research can be generalized for other similar cases not yet studied (Yin, 1994, p. 35). This work investigated the same factors related to two distinct cases developed by the same company so as to support the external validity of this research, enabling the results to be applied in other cases within the same firm analyzed.

reliability A protocol for documentation of the adopted procedures was developed to guarantee the reliability of this study. A digital data repository was also created to store all information gathered during the data collection stage. This repository stores the data set acquired during the field research for this investigation, as well as all the results accrued from the statistical analysis performed.

cAse descrIptIon the company The company under analysis is a major Brazilian firm in the information technology industry. It has more than 30,000 employees with offices throughout Brazil.1 In 2003, the company posted total revenue of US$865 million and net income of US$76 million. Due to its nationwide presence, this company faces an ongoing challenge to implement face-to-face corporate training programs, due to budget constraints. So, it is in this context that the two training programs, namely Program A and Program B were envisaged and implemented. The name of the company, as well

Some Key Success Factors in Web-Based Corporate Training in Brazil

as further details about it, are kept confidential, as agreed with its top executives.

program A Program A, considered a successful case by the company, is a mandatory corporate distance training program for all managers, namely its main target audience. Any employee who is promoted to a managerial function is obliged to take this course within a maximum timeframe of one year. This training program lasts 9 months and consists of three distinct stages that encompass distance and face-to-face training. The focus of this program lies in the development of leadership skills. Accordingly, the following issues are addressed: the attributes that make an effective leader; the different kinds of leadership styles that are best used under certain conditions; the various theories of leadership practice and the pros and cons of each; and the leadership responsibilities related to administrative and management tasks. The training program is based on the premise that, rather than being an isolated event, learning is a continuous process throughout the professional’s lifetime. Program A uses several information technology tools, such as intranet that is heavily deployed to provide information considered essential for the managers of the company. Stage I of this program (prelearning laboratory) is developed online in a distance-based training format. This stage lasts from 5 to 6 months and is an individual activity that demands between 48 and 56 hours of study. Stage II of this program (learning laboratory) is a face-to-face experience lasting 5 days. The professionals must have successfully completed Stage I before embarking on this second stage. This learning laboratory takes place in the Global Learning Center of the company, in the city of São Paulo. Stage III of this program (postlearning laboratory), like Stage I, is developed on a distancetraining basis. This stage focuses on collaborative

learning via the company’s intranet, as well as public forums and tools like instant messaging. Throughout the duration of the course, a mediator is previously assigned and available to take part in the program, both in person and online, in order to resolve any doubts the professionals may have, to supply the students with suggestions, and to help them solve general problems. According to an interview with the manager of Program A, this program is considered a success, having fully achieved its targets. Furthermore, 32 users of Program A, who attended the program during 2005, answered the questionnaire developed for this research and evaluated their participation on this training program as a highly positive experience (average of 8.5 and standard deviation of 1.32 on a ratio scale ranging from 0 to 10). Therefore, it may be considered that the objectives were achieved. All of the 32 respondents were managers of the company.

program b Program B started at the beginning of 2004, initially as an effort to provide and make information about the company’s productive and administrative processes available to employees located in the various offices of the company nationwide. The design and development of the program was organized by the company’s IT team, supported by the basic premise of using the corporate intranet to publish all the content considered relevant. The first version of the program gathered and consolidated the wealth of information about the company’s processes already published in the intranet under a single site with a unique index for conducting searches. For this purpose, a team of five employees from two different business units was formed to assist the IT area in the identification and classification of information. Once the information had been duly identified and classified, the IT area began to configure the program, so as to feature distinct courses categorized by subject. These courses could then

221

Some Key Success Factors in Web-Based Corporate Training in Brazil

be accessed by any employee via the intranet. Consequently, for each course implemented, a program manager was chosen to be in charge of developing the assessment questions (multiplechoice based), having privileged access to the answers given by the students. After an initial test period—based on just one course developed for a specific group of employees—three distinct courses were made available—two of them focusing on specific working processes of the firm (order fulfillment and customer service), and the third addressing administrative content (employee performance assessment and promotion). The main target of this training program was to reduce the costs involved in corporate training, as well as to speed up the adaptation and training time for newly hired professionals to become accustomed to the processes used by the organization. After less than one year, having failed to achieve its objectives, the program was redesigned. Thirty-one users of Program B who attended the program during 2005 answered the questionnaire distributed by the researcher. In essence, they evaluated the experience of taking part in this program as negative since the aims were not achieved (average of 4.52 and standard deviation of 1.15 on a ratio scale ranging from 0 to 10). This evaluation from these employees was tallied with the opinion of the program managers, as they stressed that the objectives of this program were not achieved.

comparison of results Initially, it is necessary to analyze the differences singled out by both the program managers and users concerning the achievement of objectives of the training programs. According to the assessment of the manager of Program A, the objectives of the training were fully achieved, and in his general evaluation, the program was rated as “very good.” Conversely, the managers

222

of Program B realized that the main targets of this program were not achieved, which led the program to be redesigned. Thus, according to the managers’ perceptions, the difference related to achievement of objectives between the two programs becomes clear. In order to analyze user perceptions related to the programs, it is necessary to evaluate the difference between the average grades given by the students to each one of the programs. The average user evaluation grade regarding the achievement of objectives in Program A was 8.5 (s=1.32; n=21, on a ratio scale of 0 to 10), whereas the same value concerning Program B was 4.52 (s=1.32; n=32, on a ratio scale of 0 to 10). This difference between the averages seems to tally with the opinion of the program managers. However, it is necessary to apply a statistical test (t-test) to compare the average of each program, so as to establish whether or not they can be considered different according to a statistical level of significance. Table 2 depicts the results accrued from the comparison of employee evaluation averages related to the achievement of objectives of the training programs. From the results presented in Table 2, it is clear that there is a significant statistical difference between user perception averages related to the achievement of objectives of the training programs (p < 5%). Furthermore, it can be observed that the interval of confidence does not encompass zero; that is, it is all positive. Thus, it is possible to support with a 5% level of significance that the averages are different and the average of Program A is greater than the average of Program B (Sincich, 1995, p. 532). It can be argued that with respect to “achievement of objectives,” Program A achieved better results than Program B. On the basis of this, the factors that influenced these results were researched, based on the theoretical model adopted in this article. Consequently, the evaluation averages of each dimension of the Reeves and Reeves’ (1997) model were analyzed in order to find out which ones actually had an

Some Key Success Factors in Web-Based Corporate Training in Brazil

Table 2. Comparison of averages related to “achievement of objectives” according to the users of the training programs Levene’s Test for Equality of Variances

F

Achievement of Objectives

.202

Sig.

.655

t-test for Equality of Means

T

df

12.752

Sig. (2-tailed) (p)

61

Mean Difference

.000

Std. Error Difference

3.98

.31

95% Confidence Interval of the Difference Lower 3.36

Upper 4.61

Table 3. Comparison of the averages of the samples’ dimensions of the model Levene’s Test for Equality of Variances

F

Pedagogical Philosophy

.010

Sig.

.919

t-test for Equality of Means

t

.511

df

61

Sig. (2-tailed)

.611

95% Confidence Interval of the Difference

Mean Difference

Std. Error Difference

Lower

.11

.23

-.34

Upper .56

Learning Theory

55.065

.000

2.470

61

.016

.52

.21

.09

.94

Goal Orientation

4.285

.043

6.239

61

.000

1.36

.22

.92

1.79

Task Orientation

16.813

.000

4.963

61

.000

1.03

.21

.61

1.44

Source of Motivation

8.686

.005

4.951

61

.000

1.15

.23

.68

1.61

28.837

.000

6.790

61

.000

2.56

.38

1.81

3.31

68.946

.000

9.747

61

.000

1.94

.20

1.54

2.33

129.092

.000

3.760

61

.000

.78

.21

.37

1.20

Cultural Sensitivity

20.583

.000

7.756

61

.000

1.00

.13

.74

1.26

Structural Flexibility

.943

.335

-.751

61

.455

-.19

.26

-.71

.32

Teacher Role Metacognitive Support Collaborative Learning

impact on the results depicted above. Similarly, the dimensions that presented statistical significant differences in the sample averages for each program were examined, as these are the dimensions that can be considered to be influential in the achievement of objectives of each Web-based

corporate training program analyzed. Table 3 compares the averages related to each dimension of the programs under analysis, according to the framework of Reeves and Reeves (1997). As can be seen in Table 3, there is no difference in the pedagogical philosophy and structural

223

Some Key Success Factors in Web-Based Corporate Training in Brazil

flexibility dimensions in the two cases, with a 5% level of statistical significance (p>0.05). Hence, these dimensions can be disregarded as critical success factors in Web-based corporate training. Based on this result, a multiple linear regression between the achievement of objectives (dependent variable) and the eight dimensions that presented significantly distinct averages (independent variables) was run. The intention was to verify which

variables could be considered truly influential in the outcomes achieved. It is important to stress that this regression seeks to verify the impact of each dimension on the outcomes of the programs under analysis, rather than to predict the outcomes of similar programs based on the dimensions of the model proposed by Reeves and Reeves (1997). Table 4 depicts the summary of results and the statistical values accrued from this multiple

Table 4. Summary of the linear regression of the dimensions of the model Model Summary (sample = 63 respondents; p-value=0.001) Model

R

1

R Square

.847(a)

Adjusted R Square

.717

Std. Error of the Estimate .675

1.34

a Predictors: (Constant), Cultural Sensitivity, Learning Theory, Source of Motivation, Goal Orientation, Teacher Role, Task Orientation, Collaborative Learning, Metacognitive Support

Table 5. Analysis of the statistical significance of the coefficients of the linear regression of the dimensions of the model Coefficients Unstandardized Coefficients

Model (Constant) Learning Theory

B

Standardized Coefficients

Std. Error

2.160

.547

-4.589E02

.230

t

Colinearity Statistics

Sig.

Beta

Lower Bound

Upper Bound

3.950

.000

1.063

3.256

-.017

-.200

.843

-.507

.415

Tolerance

.727

VIF

1.376

Goal Orientation

.486

.211

.226

2.299

.025

.062

.910

.541

1.849

Task Orientation

-.215

.256

-.088

-.839

.405

-.729

.299

.475

2.105

Source of Motivation

.845

.209

.388

4.046

.000

.426

1.263

.571

1.753

.100

.124

.084

.805

.424

-.149

.349

.486

2.058

Metacognitive Support

.645

.228

.342

2.833

.006

.189

1.101

.359

2.785

Collaborative Learning

.108

.271

.042

.399

.691

-.436

.652

.478

2.090

Cultural Sensitivity

.288

.384

.087

.750

.457

-.481

1.057

.387

2.587

Teacher Role

Dependent Variable: Achievement of Objectives

224

95% Confidence Interval for B

Some Key Success Factors in Web-Based Corporate Training in Brazil

regression. This summary supports the validity of using the eight dimensions of the theoretical model (predictors) to forecast the achievement of objectives for each case studied (in the summary, the “R” column represents the correlation coefficient and the “R Square” column represents the determination coefficient). From these data, it can be argued that nearly 70% (0.675) of the variance of the “achievement of objectives” variable can be explained by the dimensions included in this regression. After validation of the model, an attempt was made to verify which coefficients, namely the dimensions of the model applied, actually influenced the achievement of objectives of Web-based training programs. Table 5 presents the summary of the statistics related to the coefficients of the regression model. From the results depicted in Table 5, it can be deduced that with a 5% level of significance, the learning theory, task orientation, teacher role, collaborative learning, and cultural sensitivity dimensions did not reveal evidence of any statistically significant linear relationship with “achievement of objectives” (Sig. > .05). In order to strengthen the results accrued from this multiple linear regression, with respect to the lack of evidence of any linear relationship of the

learning theory, task orientation, teacher role, collaborative learning, and cultural sensitivity variables, simple linear regressions of each of these variables vis-à-vis the “achievement of objectives” were performed. Table 6 presents the summary of the results accrued from these five simple regressions, which was drawn up separately from Table 5 to make it easier for the reader to fully understand the influence of each discarded dimension in the “achievement of objectives.” As can be observed from analysis of the correlation coefficient (column “R”) and the determination coefficient (column “R Square”) of the five simple regressions, these variables did not effectively have any bearing on the “achievement of objectives” variable (“R Square” smaller than 0.3). Lastly, a final statistical analysis was performed. Analyzing the results of the multiple linear regression of the three variables selected as being influential in the achievement of objectives of the training programs—goal orientation, source of motivation, and metacognitive support—it can be seen that this model is very similar to the former multiple regression model (Table 4) which took eight variables into consideration. Table 7 portrays a summary of this model.

Table 6. Summary of the models of simple linear regression of the variables discarded in the multiple linear regression Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1(a)

.291(a)

.085

.070

2.27

2(b)

.494(b)

.244

.232

2.06

3(c)

.524(c)

.275

.263

2.02

4(d)

.462(d)

.213

.200

2.11

5(e)

.514(e)

.265

.253

2.04

(a) Predictors: (Constant), Learning Theory (b) Predictors: (Constant), Collaborative Learning (c) Predictors: (Constant), Task Orientation (d) Predictors: (Constant), Teacher Role (e) Predictors: (Constant), Cultural Sensitivity

225

Some Key Success Factors in Web-Based Corporate Training in Brazil

Table 7. Summary of the linear regression of the “metacognitive support,” “source of motivation,” and “goal orientation” dimensions Summary of the Regression (sample=63 respondents; p-value= 0.000) Model

R

1

.838(a)

R Square .703

Adjusted R Square

Std. Error of the Estimate

.688

1.32

a Predictors: (Constant), Metacognitive Support, Source of Motivation, Goal Orientation

conclusIon, reseArcH lImItAtIons, And FurtHer studIes concluding remarks Despite the fact that distance education has been around for over a century, the development of training programs has not achieved its full potential within organizations (Berge, 2002). Different technologies have been used since the creation of the first distance training program, though Web technology is considered a watershed in this realm. While the technological progress has been impressive, the implementation of Web-based distance training has only increased at a slow pace (Cross, 2004). Besides the hype around Internet technology and its use in the business arena, the first trials using the Internet in corporate training arose at the end of the 20th century. However, most of these initial applications either failed or fell short of the expected outcomes (Cross, 2004). For over a century, society has been trying to understand precisely how human beings learn. As with most problems in the social sciences, there is no single answer. However, it is clear that some rationale behind this research question must be developed. It must be remembered that Western society (mainly the USA) has been heavily influenced by the instructivist/behaviorist paradigm, upon which its educational system was designed (Criswell, 2000). On the basis of theoretical references and case research analysis, it became clear that the 226

deployment of Web-based training programs is not merely a technological issue. As in any training program, the inherent objectives and characteristics that it is seeking to achieve must be analyzed by the designers, so as to permit selection of the most adequate learning theory and define the instructional design, as well as develop and deploy the training program adequately. Based on the comparison of averages, it was concluded with 5% level of statistical significance that there was no difference between the pedagogical philosophy and structural flexibility dimensions in the two cases analyzed. The sample averages of the former dimension (1.96 for Program A and 1.85 for Program B) indicate that both programs were highly instructivist, namely most of the knowledge is imparted by the training, rather than constructed by the students themselves. In other words, most of the learners’ prior experiences were not taken into consideration in either case. This tallies with some authors who reveal the hurdles in developing a constructivist Web-based corporate training program in an environment where efficiency is pursued in order to be attained in a short time frame (see, for instance, Criswell, 2000; Joia, 2001; Joia & Casado, 2007). Likewise, the sample averages of the latter dimension (2.69 for Program A and 2.88 for Program B) pointed to the fact that “fixed” training programs are still dominant in corporate training, as in neither of the programs could the learners use the systems irrespective of time and/or location. Thereafter, applying a linear multiple regression between the dimensions of the model

Some Key Success Factors in Web-Based Corporate Training in Brazil

developed by Reeves and Reeves (1997) and the achievement of objectives of both training programs, it can be seen that five out of the eight remaining dimensions of the theoretical model did not have a significant influence on the results of either program. Actually, the dimensions that effectively had a major impact on the outcomes of training Programs A and B were goal orientation, source of motivation and metacognitive support. The low averages observed for the goal orientation dimension (2.94 for Program A and 1.58 for Program B) indicate that the objectives of both programs were more specific than generic. However, it is important to note that Program A aimed at achieving somewhat higher-order goals (namely leadership skills) than Program B. Conversely, Program B set out to address sharply focused goals (namely the firm’s processes). In other words, with respect to this dimension, Program A was less instructivist/behaviorist than Program B. This result duly corroborates the ideas of several authors who argue the need for a broader orientation for the success of a distance training program, that is, one that elicits more than the mere solution of specific problems (see, for instance, Dick & Carey, 1996; Kay et al., 1970; Mager, 1972; Sancho, 1998, to name just a few). Program B—with an average of 1.06—had hardly any metacognitive support, whereas Program A—with an average of 3.00—revealed a certain level of implementation of this dimension. Once again, based on data collected from informal interviews, the users of Program B declared that there was no tool for students to track their progression during this training program. Moreover, regarding metacognitive support, the actual description of the features available in Program B to students, from the program managers’ perspective, namely access via the intranet and multiple choice questionnaires, reveals and supports the lack of means for users to assess their learning strategies in a timely manner.

On the other hand, Program A did indeed provide some opportunities for students to develop the kind of assessment addressed above. The tool upon which this program was built allowed the users to track their outcomes at each stage of training, as well as the percentage of total time available to complete the course, and the estimated total time necessary to accomplish each stage of the program. Furthermore, Program A allowed the students to check back on content they had already studied on the course, thereby enabling them to control their learning process, as suggested, for instance, by Nevado et al. (2004), Campbell, Strijbos, Karel Kreijns, and Beers (2000), and Costa, Fagundes, and Nevado (1998). Lastly, Program B users’ assessment concerning the source of motivation dimension produced an average of 1.26, indicating that the source of motivation was mostly extrinsic. On the other hand, in Program A (average of 2.41), it becomes clear that there was at least some prior intrinsic source of motivation during the training program per se, probably due to the fact that these employees had just been promoted to managers. Thus, it can be considered that more than being motivated by the course, the students were supposed to be motivated by the company and their careers—a claim supported by interviews developed with five users of Program A. Conversely, the users of Program B did not appear to be motivated to take part in the training program, except for external motivation based on the mandatory nature of the program. Interestingly, this result complies with the ideas of Carroll (1968), Amabile (1993), and Keller and Suzuki (2004) about the importance of taking intrinsic motivation into account in any pedagogical model. Hence, from the comparison of the two cases, the following items can be considered key success factors in these Web-based training programs: •

Clear definition of training content, target employees and objectives of the program,

227

Some Key Success Factors in Web-Based Corporate Training in Brazil

• •

seeking more than merely the solution of specific problems; Development of a source of intrinsic, as opposed to extrinsic, motivation; Implementation of Web-based metacognitive support.

Figure 2 depicts the inter-relationship and influence of the critical success factors in the development of Web-based distance training programs in a concise way. As suggested in Figure 2, the three key success factors accrued from the analysis of the results of this research vis-à-vis the theoretical background enable the selection of the learning theory, the instructional design and the technologies to be used in this endeavor. It is also important to highlight that there is a feedback process in the model of Figure 2, namely the factors interact among themselves during the life cycle of the training program. It is interesting to note that according to Ertmer and Newby (1993) and Conole, Dyke, Oliver, and Seale (2004), the selection of a specific learning theory is not a key success factor by itself. Moreover, the realization that this dimension did not directly influence the outcomes accrued from selected programs A and B (as both presented

Figure 2. The structure of a Web-based training program Web-Based Training Program

Learning Theory

Goal Orientation

228

Instructional Design

Source of Motivation

Technology

Metacognitive Support

instructivist/behaviorist characteristics) complies with Reeves’ (1997) frame, as it does not support the allegation that an instructivist/behaviorist program is necessarily better than a constructivist/ cognitivist one and vice-versa. However, this is a point that must be the subject of in-depth investigation in future research addressing training in virtual environments. Program A presented a more constructivist/cognitivist approach than Program B, as witnessed by the fact that the averages of the three relevant dimensions in the former program were higher than the corresponding dimensions in the latter program. This tallies with some authors who have argued that the constructivist/cognitivist approach is best suited for Web-based distance training (see, for instance, Costa et al., 1998).

research limitations As with all research, this project has a few limitations now set forth. First of all, the number of respondents—32 users of Program A and 31 users of Program B—led to a sample size limitation, preventing the authors from running one multiple linear regression for each training program. According to Hair, Anderson, Tatham, and Black (1998, p. 166), there should be at least five observations for each independent variable. As there were eight remaining variables, a sample of at least 40 respondents for each training program was required. Accordingly, a linear multiple regression adding a dummy variable for Program A and Program B had to be run. The outcomes of this latter regression have shown the difference between the degree of accomplishment of objectives of either program (Hair et al., 1998, pp. 167–168). Moreover, as programs A and B are not exactly equal, some other factors associated with their corresponding content and modus operandi, just to name two aspects, can also have had an influence on their respective outcomes. For instance, in the second program, the fact that the IT team designed much of the content, a task that

Some Key Success Factors in Web-Based Corporate Training in Brazil

should more appropriately have been performed by domain knowledge specialists, may also have affected the outcome. Besides, there may have been motivational differences between the participants of the two programs, as well as differences regarding the participants’ IT literacy, since the degree of general familiarity with these technologies may be an important factor in determining success or otherwise. Furthermore, this article attempted to establish the value perceptions of the employees regarding the outcomes of the two Web-based training programs analyzed. There are some limitations in this approach, as some of the variables derived from the Reeves and Reeves (1999) model are not such simple variables as to be clearly understood by the respondents beyond all reasonable doubt, even after various meetings with the author. Indeed, a certain degree of subjectivity and bias from the employees may have occurred (Scandura & Williams, 2000). Lastly, this is not a cross-cultural research project. Therefore the aspect of whether or not there is any influence accruing from the Brazilian setting in the outcomes of this research is not analyzed. The reason for this lies in the very fact that there are as yet very few works about Webbased corporate training in Brazil in existence. In order that one can develop cross-cultural studies, it is important to have information about what is supposed to be compared.

Further studies This article naturally does not claim to be the ultimate research in this knowledge field. The subject deserves a great deal more study and investigation. Research involving a larger number of companies and focusing on each specific dimension involved in the development of Web-based distance training programs might reveal other important issues related to this realm, in order to allow the organizations to better understand, improve and measure the outcomes of these endeavors.

Lastly, as has been said earlier, it is important to fully understand whether or not the Brazilian environment influenced the outcomes presented. Moreover, it is also important to verify whether there are differences between Web-based corporate training programs conducted in developing countries (such as Brazil) and developed countries. Thus, there is still much ground to be covered in this area.

reFerences Amabile, T. (1993, Fall). Motivating creativity in organizations. California Management Review, 40(1), 39–58. Bates, A. W. (1995). Technology, open learning and distance education. London/New York: Routledge. Berge, Z. L. (2002). Obstacles to distance training and education in corporate organizations. Journal of Workplace Learning, 4(5), 182–189. Bregman, P., & Jacobson, H. (2000). Searching for answers: Yes, you can measure the business results of training. Training, 38(8), 68–72. Brown, A. R., & Voltz, B. D. (2005). Elements of effective e-learning design. The International Review of Research in Open and Distance Learning, 5(1). Retrieved June 24, 2008, from http:// www.irrodl.org/index.php/irrodl/article/viewArticle/217/300 Bruner, J. (1985). Models of the learner. Educational Researcher, 14(6), 5–8. Campbell, D. M., Melenyzer, B. J., Nettles, D. H., & Wyman, R. M., Jr. (2000). Portfolio and performance assessment in teacher education. Allyn & Bacon. Campos, D. S. (1982). Psicologia da aprendizagem. Editora Vozes Ltda, Petrópolis, RJ, Brazil Campos, F. C. A., Rocha, A. R. C., & Campos, G. H. B. (1998). Design instrucional e construtivismo:

229

Some Key Success Factors in Web-Based Corporate Training in Brazil

Em busca de modelos para o desenvolvimento de software. Retrieved June 24, 2008, from http:// www.niee.ufrgs.br/ribie98/TRABALHOS/250M. pdf Carey, T., Mitchell, S., Peerenboom, D., & Lytwyn, M. (1998). Effectiveness of learning technologies: The costs and effectiveness of technology-based approaches to teaching and learning. University of Guelph. Carroll, J. B. (1968). On learning from being told. Educational Psychologist, 5, 4–10. Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459. Cole, P. (1992). Constructivism revisited: A search for common ground. Educational Technology, 32(2), 27–34. Conole, G., Dyke, M., Oliver, M., & Seale, J. (2004). Mapping pedagogy and tools for effective learning design. Computers & Education, 43(1–2), 17–33. Costa, I. T., Fagundes, L. C., & Nevado, R. A. (1998). Projeto TEC-LEC Modelo de uma Nova Metodologia em EAD Incorporando os Recursos da Telemática. Proceedings of the Informática na Educação: Teoria e Prática, 1(1), 83–100. Porto Alegre. Criswell, E. (2000). The humanistic tradition: A vision for the future. Journal of Humanistic Psychology, 40(3), 74–82. Cross, J. (2004). An informal history of eLearning. On the Horizon, 12(3), 103–110. Dick, W., & Carey, L. (1996). The systematic design of instruction. New York: HarperCollins Publisher. Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. Ertmer, P. A., &╯Newby, T. J. (1993). Behaviorism, cognitivism, constructivism: Comparing critical

230

features from a design perspective. Performance Improvement Quarterly, 6(4), 50–72. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American. Giardina, M., Oubenaissa, L., & Bhattacharya, M. (2002). Designing a framework for the implementation of situated online, collaborative, problem-based activity: Operating within a local and multi-cultural learning context. International Journal on E-Learning, 1(3), 41–46. Norfolk, VA: AACE. Graham, G. (2005). Behaviorism. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Fall 2002 ed.). Retrieved June 24, 2008, from http://plato.stanford.edu/entries/behaviorism/#1 Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Prentice Hall. Hauser, L. (2005). Behaviorism. In The Internet encyclopedia of philosophy. Retrieved June 24, 2008, from http://www.utm.edu.research/iep/b/ behavior.htm Hodgins, H. W. (2000). Into the future: A vision paper. Commission on Technology and Adult Learning. Retrieved June 24, 2008, from http:// www.learnativity.com/download/MP7.pdf Huitt, W. (2003). Constructivism. Educational Psychology Interactive. Retrieved June 24, 2008, from http://chiron.valdosta.edu/whuitt/col/cogsys/construct.html Huitt, W., & Hummel, J. (1998). An overview to the behavioral perspective. Retrieved June 24, 2008, from http://chiron.valdosta.edu/whuitt/col/ behsys.html Joia, L. A. (2001). Evaluation of hybrid socioconstructivist model for teacher training. Journal of Technology and Teacher Education, 9(4), 519–549. Joia, L. A., & Casado, N. (2007, September 23–26). Fatores criticos de sucesso em treinamen-

Some Key Success Factors in Web-Based Corporate Training in Brazil

tos corporativos a distância via Web: Evidências empírico-exploratóriasa partir de um estudo de caso. In Proceedings of the 31st EnANPAD (Brazilian Academy of Management Meeting), Rio de Janeiro. Jonassen, D. H. (1981). What are cognitive tools? University of Colorado, U.S.A. Retrieved June 24, 2008, from http://www.cs.umu.se/kurser/ TDBC12/HT99/Jonassen.html Kafai, Y., & Resnick, M. (Eds.). (1996). Constructionism in practice: Designing, thinking, and learning in a digital world. Lawrence Erlbaum Associates. Kay, H., Dodd, B., & Sime, M. (1970). Iniciação à instrução programada e às máquinas de ensinar. São Paulo: IBRASA. Keller, J., & Suzuki, K. (2004). Learner motivation and e-learning design: A multinationally validated process. Learning, Media and Technology, 29(3), 229–239. Kirschner, P., Strijbos, J. W., Karel Kreijns, K., & Beers, P. J. (2004). Designing electronic collaborative learning environments. Educational Technology Research and Development, 52(3), 47–66. Mager, R. F. (1972). Objetivos para o ensino efetivo. Rio de Janeiro: SENAI. Malone, J. C., Jr. (1999, January). Operants were never “emitted.” Feeling is doing, and learning takes only one trial: A review of B. F. Skinner’s recent issues in the analysis of behavior. Journal of the Experimental Analysis of Behavior, 1, 115–120. University of Tennessee. Martin, K. (1998). WBI or not WBI? Issues of Teaching and Learning, 4(7). Retrieved June 24, 2008, from http://www.catl.uwa.edu.au/NEWSLETTER/issue0798/dimensions.html Mergel, B. (1998). Instructional design and learning theory. University of Saskatchewan. Retrieved June 24, 2008, from http://www.usask.ca/education/coursework/802papers/mergel/brenda.htm

Merriam, S. B. (1993). Adult learning: Where have we come from? Where are we headed? In An update on adult learning theory (pp. 5–14). San Francisco: Jossey-Bass. Morra, L., & Friedlander, A. C. (1999, May). Case study evaluations (Working Paper Series No. 2). OED (Operations Evaluation Department), World Bank. Nevado, R. A., Basso, M. V. A., & Menezes, C. S. (2004). Webfólio: Uma proposta para avaliação na aprendizagem: Conceitos, Estudos de Casos e Suporte Computacional. In Anais do Simpósio Brasileiro de Informática na Educação, ManausAM, Brazil. Penuel, B., & Roschelle, J. (1999). Designing learning: Cognitive science principles for the innovative organization. Center for Technology in Learning, SRI International. Piaget, J. (1964). The construction of the reality in the child. Retrieved June 24, 2008, from http:// www2.cddc.vt.edu/marxists/reference/subject/ philosophy/works/fr/piaget2.htm Powell, G. C. (2000). Are you ready for WBT? Wayne State University. Retrieved June 24, 2008, from http://it.coe.uga.edu/itforum/paper39/ paper39.html Reeves, T. C. (1997). A model of the effective dimensions of interactive learning on the World Wide Web. University of Georgia. Retrieved June 24, 2008, from http://it.coe.uga.edu/~treeves/ WebPaper.pdf Reeves, T., & Reeves, M. (1997). Effective dimensions of interactive learning on the World Wide Web. In B. Khan (Ed.), Web-based instruction (pp. 59–66). Englewood Cliffs, NJ: Educational Technology Publications. Reinemeyer, E. (1999). Edward Lee Thorndike. Psychology History. Retrieved June 24, 2008, from http://carbon.cudenver.edu/~mryder/itc_data/ idmodels.html

231

Some Key Success Factors in Web-Based Corporate Training in Brazil

Rieber, L. P. (1992). Computer-based microwords: A bridge between constructivism and direct instruction. Educational Technology, Research, and Development. Retrieved June 24, 2008, from http://www.sll.stanford.edu/projects/Wallenberg/ ateam/abstracts/Rieber.html Rosemberg, M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age. New York: McGraw-Hill. Sancho, J. M. (1998). Para uma tecnologia educacional (B. A. Neves, Trans.). Porto Alegre: ArtMed. Sherry, L. (1996). Issues in distance learning. International Journal of Distance Education, 1(4), 337–365. Sincich, T. (1995). Business statistics by example. Prentice Hall. Thorndike, E. L. (1911). Animal intelligence. Macmillan. Vygotsky, L. (1935). Thougth and languages. Retrieved June 24, 2008, from http://marxists.org/ archive/vygotsky/works/words/vygotsky.htm Watson, J. B. (1929). Behaviorism: The modern note in psychology. Retrieved June 24, 2008, from http://psychclassics.yorku.ca/Watson/Battle/ watson.htm

Wilhelmsen, S., Stein, I. Å., & Øyvind, M. (1998). Psychological theories: A brief survey of the changing views of learning. Retrieved June 24, 2008, from http://www.uib.no/People/sinia/ CSCL/Web_struktur-4.htm Wilson, B., Jonassen, D., & Cole, P. (1993). Cognitive approaches to instructional design. University of Colorado at Denver. Retrieved June 24, 2008, from http://carbon.cudenver.edu/~bwilson/training.html Yin, R. (1994). Case study research: Design and methods (2nd ed.). Sage Publications Inc.

endnote 1

Brazil is the largest country in South America and the fourth largest in the world in coterminous area, ranking after Russia, Canada, and China (the U.S. is larger with Alaska, Hawaii, and the dependencies included). Occupying nearly half of the South American continent, it covers an area of 8,511,965 sq km (3,286,488 sq mi), extending 4,395 km (2,731 mi) N–S and 4,320 km (2,684 mi) E–W.

This work was previously published in the International Journal of Web-Based Learning and Teaching Technologies, Vol. 3, Issue 4, edited by L. Esnault, pp. 1-28, copyright 2008 by IGI Publishing (an imprint of IGI Global).

232

233

Chapter 13

A Virtual World Environment for Group Work E. Brown Anglia Ruskin University, UK M. Hobbs Anglia Ruskin University, UK M. Gordon Anglia Ruskin University, UK

AbstrAct This article seeks to show that a virtual world can provide a useful addition in the use of computermediated learning tools. We discuss the underlying educational context and link this to the properties of virtual worlds and, in particular, that of Second Life. We report on the progress of a project for developing group work that seeks to link affordances in the environment to learning outcomes and employs a socially situated, constructivist, pedagogical framework. We found that a virtual world environment can enable autonomous, differentiated learning through the use of suitably structured tasks, and postulate that an individual’s depth of engagement with the environment may be linked to the learning style.

VIrtuAl leArnIng enVIronments Current practice in higher education is moving away from didactic content delivery, that is, the transfer of discrete, abstract concepts (Goodyear, 2002), toward constructionist, student-centred

models. The emphasis on the skills that support independent, self-motivated learning is increasing. This trend (Cullen, Hadjivassiliou, Hamilton, Kelleher, Sommerlad, & Stern, 2002) is increasingly facilitated through dedicated educational software in the creation of virtual learning environments (VLEs). Initially little more than document repositories, VLEs have been incorporated

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

A Virtual World Environment for Group Work

with tools to facilitate communication by providing areas to comment on, contribute to, and share understanding (Laurillard, Stratfold, Luckin, Plowman, & Taylor,2000;Mason,1998).Discussion forums and chat rooms, once peripheral, are now core to the learning process (Salmon, 2004). VLEs hosted by institutions are increasingly being supplemented by social software externally hosted, such as those for blogs and virtual social spaces such as MySpace. It is within this context of integrating new technologies to support learning and teaching that we are examining Second Life and its application to group work.

tHe structure oF leArnIng A recent detailed survey of blended learning by the Higher Education Academy (Sharpe, Benfield, Robers, & Francis, 2006) identified three ways of using technology to support teaching in a blended learning environment. 1.

2.

3.

The traditional mode, the most common, is used to provide access to lecture notes and supplementary material. The transformative mode, which is innovative and relatively rare, is when technology is used to radically change course design with emphasis on interaction and communication. The holistic mode, currently emerging in use, is when students exercise an informed choice over technology provided by the institution (e.g., dedicated VLE) or from external sources (e.g., online Web-based services). Learning from the institution, from practice, and from experience is viewed as a coherent whole that requires technological support before, during, and after enrollment on a particular course of study.

These different modes of delivery support different types of learning.

234

Social constructivism exports this concept to the ability of a group of collaborating individuals to create a shared model of knowledge as well as contributing to individual learning. The project on virtual worlds for group work presented in this article has elements of each of these delivery and pedagogic views but is grounded in the transformative (constructivist) mode. There are elements in this project that contribute to the holistic view and it is hoped that progress can be made toward a generic situative model suitable for learning activities both within institutions and for ongoing professional development (Hobbs, Brown, & Gordon, 2006).

onlIne VIrtuAl worlds For educAtIon Virtual worlds have been used in educational research since the early 1990s (Hughes & Moshell, 1997) but it is only recently that the communication and hardware technology has reached the level where they can be made available to a mass market. Online gaming environments (massively multiplayer online role-playing games, MMORPGs) such as World of Warcraft have led the way in themed virtual worlds with sophisticated graphics, interaction, and a narrative that allows significant contributions by individuals or groups of users. Second Life (SL; Linden Research, 2006) is a 3-D, online, virtual world where, contrary to other existing MMORPGs, content is built and owned by its users. Second Life software provides tools and guidance for manipulating the environment, allowing action scripting, object construction, and an economy that supports the creation of virtual businesses. This is important as students can ground their academic knowledge in meaningful practice and rehearse skills through interaction within a realistic environment (Jonassen, 1997). Users with experience of the strongly themed role-playing games can find that SL lacks depth

A Virtual World Environment for Group Work

Table 1. Modes of delivery mapped to types of learning Mode of Delivery (Sharpe et al., 2006)

Type of Learning (Mayes & de Freitas, 2004)

Comment

Traditional

Associationist

Learning is constructed through the application of explained concepts to illustrative problems.

Transformative

Constructionist

Learning is achieved through exploration, reflection, and collaboration. Students apply existing knowledge and experience to integrate new concepts in a personal way.

Holistic

Situative

Students develop through participation in communities of practice, typically within a real-world situation for professional development. The situation provides the context for observation, reflection, and opportunities for mentorship, both to help the student and for the student to share knowledge with others.

and detail, but it does allow a closer relationship between the virtual (SL) and real worlds. The activities of in-world commerce are significant enough to be covered by Business Week (Hof, 2006) and are measured in hundreds of thousands of U.S. dollars with the in-world currency, the Linden dollar, freely convertible to U.S. dollars. Real-world concerns from the media (BBC, Channel 4, Reuters), commercial organizations (Nike, Amazon, IBM), and a growing number of universities have a presence in SL. SL provides campus registration to assist universities in establishing virtual classes, campus constructs, and student enrollment. Some of the educational activities in SL tend to follow a traditional class-based approach with universities such as Harvard and San Diego having their own virtual campus with virtual lectures and demonstrations. Links to these and other SL educational resources can be found on the SimTeach Web site (SimTeach, 2006). New Media Consultants (2006) has opened a virtual campus where SL is used to support a range of educational activities. While these activities provide advantages for distance learning, this does not fully exploit the intrinsic properties of the virtual world. A better model to use for learning in SL would be a field trip where some tasks may be outlined but the detailed implementation is down to the student,

their classmates, resources they find, and other residents with whom they interact.

group work The provision of tools is no guarantee of success, and students often find online group work more difficult compared to face-to-face group work (Dillinger,2001), with negative outcomes (such as disagreements) despite the achievement of a successful group project (Berge, 1998; Druskat & Kayes, 2000). However, the collaborative use of online facilities can be of tremendous benefit: from enhanced learning to the development of higher level cognitive skills (such as reflection) and noncognitive benefits (such as interpersonal skills and self-motivation in learning; Johnson & Johnson, 1992; Slavin, 1992; Thorley & Gregory, 1994).

VIrtuAl worlds group work proJect Anglia Ruskin University funded this research project from the University Centre for Learning and Teaching (UCLT) with the specific brief to extend understanding and inform teaching prac-

235

A Virtual World Environment for Group Work

tice. Previous studies into virtual worlds (Dickey, 2005) have provided important insights into the pedagogical implications of these systems. This project builds on, and contributes to, the ongoing evaluation of such systems and seeks to start the process of establishing techniques for their effective use. We have chosen the area of group work as an initial target as it corresponds to the collaborative nature of Second Life. The core learning outcomes we sought to encourage were the following: 1. 2. 3. 4. 5.

Interact effectively with others. Maintain cooperative working relationships. Play a useful role in group and team activities. Feel confident in a group setting. Take a leadership role when asked to do so.

Within the context of the SL environment, the situative model of learning seems particularly appropriate to the development of group skills. The shared experience and group targets set by the project are designed to help develop independent and cooperative learning within the group.

metHodologY Interaction is fundamental to acquiring knowledge (Barker, 1994), and it needs to be flexible to

allow students to discover their own understanding for themselves (Jonassen, 2000; Sims, 1997). However, within this flexibility, strategies of encouragement should be put in place as interaction between students does not happen in the natural course of events (Hallett & Cummings, 1997). Laurillard et al. (2000) suggest the creation of learning activities best mapped to, and situated within (Seeley-Brown, Collins, & Duguid, 1995), the motivational aspects of the environment. Tasks were mapped to the anticipated motivations of SL (wonder, surprise, exploration, creativity, and social activity). The research was carried out as an evaluative case study using material recorded from the interactions of students carrying out a set of tasks. Tasks were structured according to Salmon’s five-stage model (2004) for computer mediated conferencing. The model comprises a framework of five stages, each stage building on the previous, to enable increasing student interaction through structured activities and decreasing levels of tutor support (“scaffolding”). Salmon (2003) says that, through use of this framework, students can “benefit from increasing skill and comfort in working, networking, and learning online.” Each stage is characterised by its identifying mode of interaction. Salmon (2003) continues that Stage 1 (Access and Motivation) may be likened to induction, where tutors ensure students can access the system, and provide timely welcome and help in the use of online facilities. Stage 2

Table 2. E-learning model levels and tasks Model Stage

236

In-World Task

1. Access and Motivation

Registration and orientation

2. Socialisation

Meet and join Anglia Ruskin Computing (ARC) group

3. Information Exchange

Choose group identity

4. Knowledge Construction

Treasure hunt

5. Development

Building competition

A Virtual World Environment for Group Work

(Socialisation) encourages the establishment of online identity by finding others with whom to interact without task-based concern. These initial stages provide opportunity to practice the use of environmental tools and is time well invested, as indeed Bower (2007) reports the success of group work may be affected by the level of virtual classroom competencies (that is, the effectiveness with which tools are used to collaborate). In contrast, Stage 3 has focus on the cooperative exchange of information with regard to a task. At Stage 4 (Knowledge Construction) interaction becomes collaborative (mutual learning), with the emphasis on building knowledge through online negotiation (Laurillard et al., 2000). Stage 5 (Development) enables students to become responsible for their own learning and that of their group, becoming self-critical and reflective.

project Implementation Twelve students (first-year undergraduates, 18 years of age) from the Level 1 Computer Gaming and Animation Technology degree program agreed to participate in the group work project. None had experience in Second Life, but all were computer literate and had experience in online game environments. An online resource for the project was set up in the Moodle VLE with project documents, SL- and group-work-related Web links, a news forum (only available to the project leader) and a discussion forum (open to all participants), details of the tasks and instructions for using SL snapshots, and conversation logging. The Moodle blog system was used by students and groups to record and share their experiences.

Registration and Orientation: Stage 1, Access and Motivation Once a student chooses an avatar, he or she undertakes 1 to 2 hours of individual orientation using

the standard SL resources of Orientation Island for basic interaction skills (moving, communicating, personalizing avatars, etc.) and Help Island for introductory tutorials for building and editing objects. At this stage, students are unable to meet in-world mentors and are not guaranteed that they will even see others from their class. This session was principally facilitated in the classroom.

The Anglia Ruskin Computing Group: Stage 2, Socialisation Students were directed to the ARC group meeting point. Groups met together and could see each other’s avatars for the first time. By this time, some students had already explored and brought examples of things they had found and demonstrated the skills they had learned. Joining the ARC group permitted access to build on the ARC site, and students were awarded a note card with the treasure hunt task details.

Choose Group Identity: Stage 3, Information Exchange The students divided themselves into three groups of four, with the group task to choose an identity and devise a visual clue in their avatar appearance. The groups were asked to post messages to their blogs to demonstrate their visual identities and send emails to the project leader to identify the students behind the avatars.

Treasure Hunt: Stage 4, Knowledge Construction The group treasure hunt task was to locate objects specified on a note card. The idea was to facilitate the development of searching, traveling, and teamwork skills. To show that they had found the object, a snapshot of the group was taken with the object.

237

A Virtual World Environment for Group Work

Figure 1. The Secret Service group demonstrating their group avatar theme

Figure 2. A boathouse under construction during the building task

Building Competition: Stage 5, Development The ARC group’s land was divided into three waterfront plots. The groups were given the brief to build a jetty for a sailboat and a lodge. These were to be judged and the best construction would win a prize. To support this activity, students were directed to in-world tutorial resources and were 238

given a workshop and tutorial session on the building and editing tools. Figure 2 shows two of the building plots side by side under construction.

Feedback and Recording Feedback from students was recorded in three ways: observation in class, individual blogs, and reflective reports written shortly after the end of

A Virtual World Environment for Group Work

the activity. Evidence for activities was recoded by students using the SL snapshot tool and shared with the group by adding them to blogs. The snapshots of in-world activities were uploaded to Snapzilla or Photobucket and linked to the Moodle VLE where the individual blogs were hosted.

results Results were gathered from direct observation, both in-world in the laboratory and from the recording activities of the participating students. We found evidence that some learning objectives were well supported by the environment. We also found behaviour that we had not anticipated.

skill and knowledge differentiation Students quickly showed a marked differentiation in both the expertise and type of skills learned. Within the first few hours, it was clear that the majority were gaining wider and deeper skills than was necessary for the basic task completion. However, keeping them focused on the current activity was difficult and a few students did not gain sufficient skills to complete tasks on their own. The resources used by students in SL were closely aligned to their initial interests. We observed that those students who were already interested in technical aspects found there were resources that catered directly to those needs, as in the library of the prim (in-world) and the LSL (Linden Scripting Language) Wiki online. Those who were more interested in the look of what they were doing spent much more time finding suitable textures. Some students spent considerable time in avatar customisation that would have been better spent on developing building skills. Others not quite so engaged in the main building event were easily distracted by opportunities for unusual artefacts, exploding penguins being just one. One student was so engaged in the commercial aspects

of SL (setting up his own business and trading on the SL stock exchange) that he failed to complete the required tasks, a case of successfully running before attempting to walk. The level and sophistication of the student activities that were not related to the task at hand illustrate both the potential and the problem of using SL as a learning environment. Students are engaged in independent learning but not always directed at the task at hand.

real and Virtual Integration Most of the group work was done in a laboratory where it was easy to communicate face to face and interact with the environment. In other work, this cohort of students would typically be talking and interacting about the exercises they had been given, so it was unsurprising that there was considerable interaction about the project tasks (and other things). However, it was surprising to see how the ability to interact in the virtual world complemented and enhanced real-world communication. Students interacted seamlessly between the real and the virtual worlds, particularly during the more complex building tasks. Discussion and communication was typically done in the classroom but demonstration, knowledge discovery, and sharing were done in-world. This was an entirely unprompted, emergent behaviour enabled by the environment.

groupworking Analysis of the reflective reporting showed that most found the experience useful and interesting. Table 3 shows a summary of some of the evidence that the project provided to support the skills developed for group tasks.

level and nature of engagement Related to the use of technology for any task is the question of engagement. Students quickly get

239

A Virtual World Environment for Group Work

Table 3. Group work goals achieved Goal

Evidenced by

Supported in SL by

1.

Interact effectively with others

Completion of tasks requiring collaboration, i.e., negotiation of group theme, choosing group identity, treasure hunt as a group, building a single but jointly owned artifact

Comprehensive: Basic communication tools, e.g., chat, instant messaging, and the presence of other residents with whom to communicate

2.

Maintain cooperative working relationships

Aspects of tasks that are ongoing and require significant engagement, e.g., the building task

Comprehensive: Group artefacts created by individual contributions; persistence of artefacts; ability to send messages to group members who are not online; finding, saving, and giving objects to each other

3.

Play a useful role in group and team activities

What is useful in this context can be seen as finding and fulfilling a role or task as required by the group

Informal/Flexible: Having a variety of learning resources and formats, facilitating specialisation in knowledge areas that align with students’ learning styles thereby enabling them to bring different aspects of knowledge to the task

4.

Feel confident in a group setting

Confidence as evidenced by making individual contributions in-world and on the reporting blog

Partial: Allowing individuals ownership of their contributions, e.g., allows test objects to be made and demonstrated to others, positive reinforcement, and the ability to actively challenge others’ information (as in Veerman, Andriessen, & Kanselaar, 1999)

5.

Take a leadership role when asked to do so

Leadership as evidenced by blog accounts (building process and decision making)

One with Domain Specialists: Avatar representation, i.e., the embodiment of the leader is made visible to the group; group dynamics were supported by spatial relationships between avatars and body language, e.g., the proximity of group members and the act of paying attention

used to even the most exotic environments, and in order to hold their attention there must be a correspondence between the technology and what they perceive as being useful to them (Laurillard et al., 2000). In our own practice (Brown, 2006) we have previously identified five criteria that motivate students to engage in the use of online tools within a course. Table 4 summarises the alignment of SL with these criteria for motivation. When reflecting on observations in class with a number of different student groups, it was apparent that there were different attitudes to the virtual world, which expressed themselves in different behaviours. These behaviours can be related to the underlying character and learning styles of the students. For example, Honey and Mumford’s (1992) classifications of activist, reflector, pragmatist, and theorist can be linked to form a conceptual grid

240

as shown in Table 5. Descriptions of preferred learning activities are mapped against the most likely associated combinations. While there is a lack of concrete evidence for this model, it does give a clue to the design of learning activities. A scale of increasingly sophisticated skills in the virtual world can provide a simple measure of confidence and experience. Monitoring the rate of and depth of skills acquisition might provide evidence for different learning styles.

summary Experience, research, and preliminary findings all point to the need to devise carefully planned learning activities to produce the desired learning outcomes. Veerman et al. (1999) find that the constructiveness of a discussion is dependent on task

A Virtual World Environment for Group Work

Table 4. Level and nature of engagement Criteria

Finding

Evidence

1.

Sense of fun and novelty insufficient to motivate use of technology

True. Particularly for students used to interactive gaming technology

Some students failed to engage beyond assessment guidelines

2.

Assessment (necessity) a strong motivation for using technology

True. Other work took precedence until linked to assessment

Reporting and reflection linked to PDP assessment

3.

Structured navigation preferred by less confident students

Not proven. Unstructured nature of task criticised by some students

Some students ignored tasks and did their own thing

4.

Match between technology and task

True. Shared environment facilitated group building activity

Groups worked in both virtual and real environments

5.

Perceived value of activity

True. Depends on engagement threshold for activity

Students carried on developing in SL beyond end of project

Table 5. Showing connections between Honey and Mumford’s (1992) learning styles and interaction in SL Preferred Activity

Virtual World Attitude

Theorist: Prefer to analyse and synthesise, assimilating disparate facts into coherent theories; perfectionist

Theorists learn best from activities where they have time to explore associations thoroughly and methodically.

Superficial. User does not engage and does not find the SL environment of interest.

Reflector: Prefer thorough collection and analysis of data about experiences and events; cautious

Reflectors learn best from activities where they may observe, assimilate, research, and produce carefully considered analyses.

Realistic. User acts and behaves in SL as they would in real life, regarding other avatars as people in social situations.

Pragmatist: Keen to try out ideas, theories, and techniques in practice; practical

Pragmatists learn best from activities where there are techniques for obvious practical advantage, and have opportunity to implement what is learned.

Empowered. User acts in SL as they would in real life, but feels empowered to be more adventurous in initiating activity and social situations.

Activist: Enjoy new, immediate experiences; thrive on challenge; gregarious

Activists learn best from diverse activities where they are thrown in at the deep end and can have a go.

Fantastic. User regards SL as a game where other avatars have little connection to real people, and show bold social behaviour with less social responsibility than in real life.

characteristics and interface affordances rather than the tutor. Therefore, with careful mapping of the task to the motivational aspects of the interface (Salmon, 2000) and the suitability of the tool for the task (Brown, 2006), the tutor has a powerful facilitation tool for collaboration. Although Second life is not a magic wand, it does have accordance with transformative educational goals by providing a rich environment for individual exploration. Open-ended learning tasks and field-trip activities can be devised. However, the sophistication of the environment makes this a more, rather than less, challenging

task, as does the wider range of possible styles of interaction in a virtual world compared to classroom or traditional VLEs. The following list outlines some of the ways in which Second Life enables students to take more responsibility for their learning.

Blending Real and Virtual Virtual online environments lend themselves to distance learning, but this project shows that environment scan add a new dimension to group

241

A Virtual World Environment for Group Work

activities that can seamlessly move from face to face to virtual.

Task-Based Learning The skills needed to complete the tasks were principally learned from the virtual environment and perceived as part of the task rather than being external.

Varied Autonomous Learning Students gained skills in different areas by accessing different learning resources and experiences.

Peer-to-Peer Learning The shared environment made it easy to demonstrate applied knowledge. Students would take on a tutoring role to disseminate skills.

Mobile Group Structures The core leadership role in groups did not change, but group members would lead subtasks where they had particular skills. The affordances of the environment support autonomous differentiated learning, which we believe provides for richer interactions than a more traditional uniform class exercise approach. Students have to identify and utilise appropriate learning resources within a diverse environment, which is one of the core skills for independent learning.

Future work Having seen the nature of the environment in a real learning situation, we are now in a position to design a larger scale project to provide more detailed evidence for the effects we have seen. Second Life will provide a component to several new courses to be delivered in the 2007 and

242

2008 academic year at Anglia Ruskin University. By monitoring the acquisition of skills in the virtual environment in these and in courses without a virtual component, we hope to gather more detailed information on the transference of knowledge between students in virtual, blended, and traditional environments.

reFerences Barker, P. (1994). Designing interactive learning. In T. de Jong & L. Sarti (Eds.), Design and production of multimedia and simulation-based learning material. Dordrecht, the Netherlands: Kluwer Academic Publishers. Berge, Z. (1998). Differences in teamwork between post-secondary classrooms and the workplace. Education and Training, 40(5), 194-201. Bower, M. (2007). Groupwork activities in synchronous online classroom spaces. In Proceedth ings of the 38 SIGCSE Technical Symposium on Computer Science Education, Covington, KY (pp. 91-95). Brown, E. (2006). Discussion board or discussion bored? Paper presented at the Seventh UCLT Conference, Anglia Ruskin University. Cullen, J., Hadjivassiliou, E., Hamilton, E., Kelleher, J., Sommerlad, E., & Stern, E. (2002). Review of current pedagogic research and practice in the fields of post-compulsory education and lifelong learning. Tavistock Institute. Retrieved July 2006 from http://www.tlrp.org/pub/acadpub.html Dickey, M. D. (2005). Three-dimensional virtual worlds and distance learning: Two case studies of active worlds as a medium for distance education. British Journal of Educational Technology, 36(3), 439-451. Dillinger, M. (2001). Learning environments: The virtual university and beyond. In F. Tschang & T. D. Senta (Eds.), Access to knowledge: New

A Virtual World Environment for Group Work

information technologies and the emergence of the virtual university (pp. 1-14). Pergamon Press. Druskat, V. U., & Kayes, C. (2000). Learning versus performance in short-term project teams. Small Group Research, 31(3), 328-353. Goodyear, P. (2002). Psychological foundations of networked learning. In C. Steeples & C.Jones (Eds.), Networked learning: Perspectives and Issues (chap. 4, pp. 49-76). London: Springer. Hallet, K., & Cummings, J. (1997). The virtual classroom as authentic experience. In Proceedings of the Annual Conference on Distance Teaching and Learning: Competition, Connection, Collaboration (pp. 103-107). Madison, WI: University of Wisconsin-Madison. Hobbs, M., Brown, E., & Gordon, M. (2006). Using a virtual world for transferable skills in gaming education. ITALICS, 5(3).

Jonassen, D. H. (2000). Toward a meta-theory of problemsolving. Educational Technology: Research & Development, 48(4), 63-85. Laurillard, D.M., Stratfold, M., Luckin, R., Plowman, L., & Taylor, J. (2000). Affordances for learning in a non-linear narrative medium. Journal of Interactive Media in Education, 2. Linden Research. (2006). What is second life? Retrieved from http://secondlife.com/whatis/ Mason, R. (1998). Models of online courses. ALN Magazine, 2(2). Mayes, T., & de Freitas, S.(2004). Review of e-learning theories, frameworks and models: Stage 2 of the e-learning models desk study. JISC. Retrieved July 2007 from http://www.jisc.ac.uk/ uploaded_docu ments/Stage%202%20Learning%20Models%20(V ersion%201).pdf

Hof, R. (2006, March 28). A virtual world’s real dollars. Business Week.

New Media Consultants. (2006). NMC Campus Observer. Retrieved from http://www.nmc.org/ sl/

Honey, P., & Mumford, A. (1992). The manual of learning styles. Maidenhead.

Salmon, G. (2003). eTivities: The key to active online learning. Kogan Page Limited.

Hughes, C., & Moshell, E. (1997). Shared virtual worlds for education: The ExploreNet experiment. ACM Multimedia, 5(2), 145-154.

Salmon, G. (2004). E-moderating: The key to online teaching and learning. Routledge.

Johnson, D. W., & Johnson, R. (1992). Positive interdependence: Key to effective cooperation. In R. Hertzlazarowitz & N. Miller (Eds.), Interaction in cooperative groups: The theoretical anatomy of group learning (pp. 174-199). Cambridge University Press. Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology: Research and Development, 45(1), 65-95.

Seely-Brown, J., Collins, A., & Duguid, P. (1995). Situated cognition and the culture of learning. Sharpe, R., Benfield, G., Robers, G., & Francis, R. (2006). The undergraduate experience of blended e-learning: A review of UK literature and practice. The Higher Education Academy. Sheard, J. (2004). Electronic learning communities: Strategies for establishment and management. ACM SIGCSE Bulletin, 36(3), 37-41. Sims, R. (1997). Interactivity: A forgotten art? Retrieved July 2007 from http://www.gsu. edu/~wwwitr/ docs/interact/

243

A Virtual World Environment for Group Work

SimTeach. (2006). Information and community for educators using multi-user virtual environments. Retrieved from http://www.simteach.com/wiki/ index. php?title=Top_20_Educational_Locations_in_Second_Life Slavin, R. E. (1992). When and why does cooperative learning increase achievement? Theoretical and empirical perspectives. In R. Hertzlazarowitz & N. Miller(Eds.), Interaction in cooperative groups: The theoretical anatomy of group learning (pp.145-173). Cambridge University Press.

Thorley, L., & Gregory, R. (1994). Using groupbased learning in higher education. London: Kogan Page. Veerman, A. L., Andriessen, J. E. B., & Kanselaar, G. (1999). Collaborative learning through computer-mediated argumentation.

This work was previously published in the International Journal of Web-Based Learning and Teaching Technologies, Vol. 3, Issue 1, edited by L. Esnault, pp. 1-12, copyright 2008 by IGI Publishing (an imprint of IGI Global).

244

245

Chapter 14

Reading Comprehension as a Competence to Digital Media Expert Performance Maria Cristina Rodrigues Azevedo Joly São Francisco University, Brazil Ronei Ximenes Martins São Francisco University, Brazil

AbstrAct The information and communication technologies (ICTs) present in the Brazilian education system determine the development of technology literacy among teachers and students, which can be measured by ICT performance. The Technology Performance Scale (EDETEC) is a self-reporting psychometric instrument to verify what the students’ conceptions are about ICT and their performance in using technology tools. Considering the necessity of the acquisition of both technology literacy and reading comprehension skills to use ICT resources, this study aimed to know the ICT performance, reading comprehension achievement, and the possible relations among them. The participants were 63 Brazilian students from K10 and K11. The EDETEC and Cloze Test with options were applied by school and grade. The best ICT performance referred to the concept and productivity tools factor (F2), and the ANCOVA (analysis of covariance) statistic test identified the influence of the grade and genre in it. There was positive correlation between reading comprehension and EDETEC.

leArnIng, tecHnologY And reAdIng compreHensIon The inclusion of digital media in daily life both as a strategy for teaching-learning and a resource for gaining access to information determines that teachers and students need to develop skills to use Information and Communication Technologies (ICT) DOI: 10.4018/978-1-60566-938-0.ch014

(Joly & Silveira, 2003; Leu, Mallette, Karchmer & Kara-Soteriou, 2005; Jones, 2006; among others). The use of ICT as media in education requires from the user high level cognitive abilities such as attention, memory and reasoning. This has to be done because it is necessary to identify, characterize and understand the media technical information (Hobbs, 2002) and then apply it in different situations with specific goals and tasks (Penuel, Korbak & Cole, 2002).

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Reading Comprehension as a Competence to Digital Media Expert Performance

With the presence of ICT there happens a change in the way students relate to one another and to information, as well as to what regards the time and place of study and realization of learning activities (Anderson & Elloumi, 2004; Pallof & Pratt, 2003; Martins, 2008). Snow and Yalow, in 1988 when revising literature regarding the interaction among teaching models using ICT and cognitive skills already observed that the relations among these measures and the learning results are more intensely present in teaching contexts where ICT is present and the responsibility to process information depends more on the student than on the teacher’s method (Martins, 2008). Besides these abilities, reading comprehension is seen as a basic skill (Solé, 1996; Leu et al., 2005) to acquire technological literacy, because the base of communication is given by the printed language (Joly, 2004; Leu, Kinzer, Cairo & Cammack, 2004; Joly et al, 2005). In the realm of reading specifically, there is a textual and/or hypertextual base (Hug & Hirumi, 2004), thus the relevance of the analysis of users’ reading skills in relation to ICT resources, through decoding (recognition and attribution of meaning to words) and comprehension (interpretation of meaning of written language) as proposed by Flanagan et al (2002). Reading comprehension is to produce relations among known and new information that has been acquired by means of inferences during the reading process. These inferences are defined by Adrián (2002) as verbal elaboration strategies in order to organize the printed information in a text by means of bonds of recuperation of the previous knowledge. According to Téllez (2005), the inferences that the reader carries through are intimately related to the reasoning processes that allow handling the ideas offered by the text searching coherence between what is known and what the author says, conditioning the reading comprehension to reasoning. Hence, the reading requires from the user both high level cognitive abilities and the use of digital medias.

246

Reading comprehension has been evaluated according to approaches in the content, by means of reading tests consisting of questions about text excerpts with several possibilities of answers, or in the associated cognitive processes with basis in the human processing theory of information (Téllez, 2005; Marini & Joly, 2006). The evaluation based on the subject matter does not frequently capture the subject’s comprehension level, offering extreme indexes for the test items (right or wrong) related to text details and not to the author’s intention and to the global understanding of what has been read (Koslin, Zeno & Koslin, 1987). The alternative, whose paradigmatic focus, in the last four decades, has been assisted in the cognitive psychology (Téllez, 2005), uses as a method the Cloze Technique and has the objective to evaluate the level of reading comprehension comparing it with other cognitive skills, previous knowledge, acquired after the teaching-learning process (Joly, 2006). The Cloze technique was created by Taylor, in 1953, and consists of omitting words in a given text, substituting them by blanks that will be filled by the reader with the word that he thinks will be adequate to give meaning to what he has been reading (Joly, 2006). It is used as a trustful instrument to evaluate the reading comprehension level in students from the elementary teaching until the superior level because it requires from the reader many abilities such as the establishment of relations among the elements of the text; the association between the previous knowledge and the printed information; recognition of when it has been understood and when it has not (Joly, 2006). It must be pointed out that Cloze technique is efficient for developing and implementing reading comprehension. Nonetheless, few studies have been made available since the past decade, in Brazil with 1st to 4th grade elementary school students related by Joly and Marini (2006), high school students (e.g Marini, 2006; Dias, 2008) and with universities students (e.g Joly & Guerra, 2004; Joly, Santos & Sisto, 2006).

Reading Comprehension as a Competence to Digital Media Expert Performance

reAdIng compreeHensIon And Ict perFormAnce The nature of reading comprehension competence is undergoing a process of redefinition which results from the use of electronic multimedia (Joly, 2002). Since the 1990s, the concept of literacy has been expanded. Not only calculus, reading and writing skills, but also the abilities associated with the use of different media and technologies are being considered for such definition. Electronic reading requires new modes of conceptualization (Reinking, 1997). While in the printed text the physical space is determined by the page, with stable content and controlled uniquely by its author, in the electronic text, the space can be dynamic and fluent, allowing mutability. It is not a matter of considering the digital version superior to the printed one, but a differentiated option capable to create and communicate messages (Luke, 2003). As a consequence, fundamental changes are taking place in the creation of texts whose models are dynamic and creative, in reading strategies and in the formation of new readers (Haggod, 2003). They need to accomplish the task of reading through electronic media. Since young people communicate more often through these media than by using traditional ones, there is need for further research on how the comprehension process of electronic messages and texts takes place (Joly et al., 2005). Furthermore, it is necessary to identify a baseline on the relation between reading comprehension of printed texts and the use of ICT resources for further comparison with reading through digital media. In a recent investigation on this theme, Joly et al (2005) have found there that are differences in reading comprehension performance for printed and electronic texts. The participants were 80 freshman psychology students, 79% of which were female and 69.1% studied at night. The age varied between 17 to 56 years of age (M=24.05, DP=7.13). 59.3% of the students

were younger than 22 years old and 30.9% were more than 25. It was observed that, according to the ranking proposed by Bormuth, participants showed a level of frustration in comprehension (score < 44% of total expected) for the printed text, which revealed little success in the task. The independent comprehension level (score > 58% of total expected) was observed for digital texts which indicates reader autonomy for this type of text. The analysis of the effect of gender (male x female) and age (lower than 25 x 25 or older) on the reading comprehension test performance for both printed and digital text, showed significant comprehension differences both for printed and digital texts, as a function of participant gender (p >

>

Manage tests and questions

> Sign answered test

> >

Log

Configure and a assemble test

> > >

Certificate

Initialize >

Insert/ delete*/ modify* user

> > >

Manage users and subjects

Insert/ delete*/ modify* subject

Register a teacher to a subject

Enroll a student in a subject

Register

Certificate Enrollment

End Entity

Coordinator

Teacher

Created with Poseidon for UML Community Edition. Not for Commercial Use.

ers: teachers and students. Some teachers have extra privileges because they are coordinators of subjects. Thus, coordinators perform the task of system administrators: They manage users (students, teachers) and subjects. As they have responsibility on the subjects they coordinate, they are in charge of registering any additional teachers to the corresponding subjects and enrolling students to their respective subjects. Teachers and coordinators registered to a subject can manage the subject’s tests and question pool, and can also consult the system’s database. Any teacher associated with a subject can add questions to its question pool, and modify and delete them if they are not used in any test. Teachers also control the time a test is available online for authorized students. Then a student has access to a test only after a process of authentication and to those tests teachers want. When a student finishes answering, he or she must submit the answered test to the system. Before submitting

268

it, the student must sign it. Once the test has been answered, signed, and submitted, it is stored and assessed in the server, and the student receives a signed confirmation of his or her submission and the obtained grade. Therefore, students operations are just related to performing tests and consulting grades. Obviously, both teachers and students can consult previous test grades but, in the case of students, only their own grades.

Architecture EVAWEB v1 has a three-tier architecture (see Figure 2) composed by a Web application running on a servlet container on the server side, and a Web client supported by some processing capabilities on the client side. On the server side, three main modules can be identified that have been implemented with three servlets: the assessment servlet, the database management servlet, and the PKI servlet. The assessment servlet is in charge of

EVAWEB V2

Figure 2. Architecture of EVAWEB v1 (González-Tablas et al., 2004; González-Tablas et al., 2007) SERVER SIDE Web Application Server Tomcat 5 Servlet.JSP Container

CLIENT SIDE Key pair generation & certificate management tool

Web Client Keystore

Signing Applet

JavaScript

http/ssl

Assesment Servlet

signature process Signing is performed outside the Web browser, similar to a smart-card signature in which the signature is generated inside the card and the private key never leaves the card. The process is as follows. First, a signed applet shows the student his or her answered test. The use of a signed applet allows the recipient to verify the authentication and integrity of the code. Furthermore, it provides

Data Base

Database Management Servlet PKI Servlet

serving students’ test requests and processing the test answers. The database management servlet is used for consulting, inserting, modifying, and deleting users, subjects, and tests in the database. The PKI servlet is in charge of providing the basic functionality of a PKI. In addition, there is also a database with users and test information, and a repository that contains both the certificate authority (CA) keys and the student public-key certificates (needed to verify the signatures).On the client side, JavaScript is used to perform local form validations and to improve interactivity. The signature on the client side is performed via a signing applet, which communicates with the user through Java graphical interfaces and with the browser via JavaScript. Further details on the implementation of EVAWEB v1 can be consulted in González-Tablas et al. (2004).

Users

Keystore

Tests

Key pair generation & management tool

a way for identifying which code is authorized to execute with special permissions not given by default inside the Java sandbox (such as reading local files). Second, after revising his or her answers, the student indicates where the private key is stored (e.g., student’s pen drive) and types the password that protects the key. Finally, the signing applet performs the signature, appends it to the answered test, and submits it to the server.

other security Features Users authenticate with a user name and password mechanism, and the student’s photo can be seen on the test page while the test is being answered. This biometric measure reduces the risk of a physical impersonation attack. To provide confidentiality, every communication with the Web application server relies on the SSL protocol. Role-based authorization (teacher, coordinator, and student) is enforced in order to access Web application components. Furthermore, the IP’s range and time-window access control is enforced when students perform tests. Non-repudiation of students’ submissions is achieved by X.509/PKIbased signatures. Finally, the use of cookies and servlet context variables contribute to preserve session security.

269

EVAWEB V2

evaluation of eVAweb v1

Architecture

EVAWEB v1 has been assessed by the students of Universidad Carlos III de Madrid in the context of an innovative education experience (GonzálezTablas et al., 2004; González-Tablas et al., 2007). The experience turned out an above-average success, but results and a further analysis highlighted the need for improvements in the system, which are described next.

EVAWEB v1 does not follow any concrete architectural design pattern; furthermore, presentation is mixed with the system’s logic and access to the database is spread throughout all code. This kind of design is maybe valid for an initial demonstrator, but the maintenance and portability of the system is highly hindered.

Functionality EVAWEB v1 has a restricted set of functionalities related to the management of the database. Particularly, it is not possible to modify or delete data. In addition, EVAWEB v1 requires the realization of annoying manual processes for issuing each student certificate as the PKI servlet only allows one to upload certificate requests and download issued certificates. This limited functionality reduces usability from the point of view of teachers and increases the probability of errors during the certificate issuance process.

Interface Form data validation is not complete in EVAWEB v1, so there is a high risk of system breakdown because of unexpected errors during the processing of input data. Furthermore, the communication with the user is done through HTML (hypertext markup language) pages, which are generated one by one in the servlets. This deficiency reduces system maintenance because if a modification in the interface is needed, each piece of code that generates HTML pages has to be changed, which, besides being a tedious process, has a very high probability of error.

database EVAWEB v1 presents some minor deficiencies in the design of the database that make the scalability of the system difficult. 270

security A security vulnerability in EVAWEB v1 is that users’ passwords are stored clearly within the database. Furthermore, in the session initiation process, the user is not asked if he or she wants to access the system as a teacher or as a student. As the system first checks if there is any student with a specific identification, a teacher with such identification would not be able to access it (at least, as a teacher).

logs EVAWEB v1 presents annotations of actions done in the system through the Web application server console. This annotation mechanism is insufficient as the console may be closed leading to the loss of system actions. In addition, using the Web application server console as a log mechanism makes it difficult to have separate logs concerning different aspects of the application.

portability This characteristic is quite restricted in EVAWEB v1. First, the database is implemented with Microsoft Access, which restricts deployment to hosts using Microsoft Windows. Second, all the system’s configuration information (directory paths, passwords, etc.) is defined within the code, meaning that if the system needs to be deployed in a different context, code must be changed and rebuilt.

EVAWEB V2

eVAweb V2 As An enHAncement oF eVAweb V1 A second version of EVAWEB has been implemented to address the deficiencies of EVAWEB v1. System users remain the same: teachers and students. Changes introduced in the system are described as follows.

Functionality Functionality of EVAWEB v2 has been first restructured (see Figure 3), incremented, and enhanced. Modification and deletion of data (users, subjects, tests, and questions) is now possible. Furthermore, EVAWEB v2 now allows teachers to issue students’ digital certificates with a very simple and automated process accessible from the Web interface. Student enrollment has been enhanced. Now it is possible to enroll several students at the same time to a subject. In addition, data queries now can be done based upon filters such as the date for tests or the enrolled subject for students, and from the query results, further related data may be accessed.

Interface Navigability through interface Web pages has been refined, mainly aspects related to form validation. Usability has also been enhanced by redesigning the aesthetics of Web pages at the same time that modularity and maintainability have been greatly improved with the use of Web style sheets (see Figure 4).

database The database in EVAWEB v2 has been redesigned following the entity-relationship model shown in Figure 5. In this case, a teacher can be associated with or collaborate in one or more subjects, while a subject must always have a teacher associated with it. In addition, a teacher can coordinate none or several subjects, but a subject always has a unique coordinator. A subject can have several tests, which are restricted to a single subject. A test can be composed of several questions and questions can be part of several tests, so questions can be reused. Questions, as well as tests, are restricted to a single subject and may have from one to four possible answers (with only one

Figure 3. Use-case diagram showing functionality of EVAWEB v2

271

EVAWEB V2

Figure 4. Use of Web style sheets in EVAWEB v2

being true). Students may be enrolled to several subjects and subjects may have several students enrolled. A student can perform a test belonging to a certain subject whenever he or she is enrolled to that subject. Under this condition, a test may be performed by several students.

Architecture The most important change in EVAWEB v2 affects its software architecture, which has been redesigned to use the model-view-controller design pattern in the server side to enhance its modularity and to ease its maintenance. The enhanced architecture can be seen in Figure 6. The client side remains the same as in EVAWEB v1. In EVAWEB v2 presentation, business logic and controller processes are separated by using JSPpages, servlets, and normal Java classes. Furthermore, access to the database is encapsulated through a Java class that acts as an interface.

272

security To solve EVAWEB v1 problems, users are forced to choose if they want to access the system as teachers or as students. Besides this, user passwords are not stored clearly anymore; instead, the database contains a hash of the password of each user. When a user attempts to log in, the hash of the password sent by the user is computed and compared with the one stored in the database in order to grant access.

logs In EVAWEB v2, a log framework has been integrated that allows the registration of all actions taken in the system. Furthermore, the results of the tests submitted by the students are doubly logged in one file and then in a separate file to ease its query. In Figure 7, partial content of the main log is shown.

EVAWEB V2

Figure 5. Entity-relationship model used in EVAWEB v2 Answer 1...4

0...n

performs

has

Student 0...n

1...1 Question

0...n 0...n

has

0...n

0...n

Test

0...n creates

0...n 1...1 Teacher 1...1

1...n

coordinates

is associated to

0...n has

1...1

1...n Subject

0...n

is enrolled to

1...1 has

Figure 6. Architecture of EVAWEB v2

portability To enhance the portability of the system, first, the database management system has been migrated from Microsoft Access to MySQL. Second, con-

figuration files have been created allowing the specification of installation variables, directory paths, passwords, and so forth. Examples of these configuration files are shown in Figure 8.

273

EVAWEB V2

Figure 7. Main log file in EVAWEB v2

Figure 8. Configuration files in EVAWEB v2

conclusIon One of the main security problems in online assessment is making students’ submissions non repudiable. The authors have developed EVAWEB, a Web-based assessment system that focuses on

274

non-repudiation requirements through the use of digital signatures. Furthermore, the developed system aims to enhance students’ learning of digital signatures by providing them with a real context to practice this technology. Thus, students

EVAWEB V2

learn the concepts involved in digital signatures, using them in their own assessment process. A first version of EVAWEB was evaluated successfully by some students of Universidad Carlos III de Madrid. However, the evaluation highlighted also the need of some improvements in the system. These enhancements have been incorporated in a second version of the EVAWEB system and are mainly focused on architecture, functionality, interface, database, portability, and security aspects. Main future work includes the evaluation of EVAWEB v2 by students in order to assess the effectiveness of the changes that have been introduced. In addition, new functionalities, such as the generation of statistics and printable documents (e.g., tests and reports) will be added. Up to now, only multiple-choice questions with one valid answer can be defined. EVAWEB should allow in the future multiple-choice questions and other features (such as fill in the blank, drag and drop, numeric entries, etc.). Security features can also be enhanced. For instance, smart cards could be used to sign the tests, and digital certificates or biometric technology would improve student authentication process. Implementation of the modelview-controller design can be further enhanced if some Web application framework, such as Struts, is used. Finally, EVAWEB should be adapted to comply with Web accessibility guidelines (World Wide Web Consortium [W3C], 2006).

reFerences

Acknowledgment

ISO/IEC 7498-2. (1988). Information processing systems: Open systems interconnection. Basic reference model: Part 2. Security architecture.

The authors acknowledge the work done by David Sánchez Torre and Javier Rodríguez Gandía in the implementation of, respectively, the first and second versions of EVAWEB while being students of Universidad Carlos III de Madrid. This work was partially supported by Universidad Carlos III de Madrid under 1ª Convocatoria de Apoyo a Experiencias de Innovación Docente Curso 2003-2004.

Chan, Y.-Y., Leung C.-H., & Li, J. K. (2003). Evaluation on security and privacy of Web-based rd learning systems. In Proceedings of the 3 IEEE International Conference on Advanced Learning Technologies: ICALT’03, Athens, Greece (pp. 308-309). Dartmouth College PKI Lab. (2001). PKI applications in academic computing. Retrieved from http:// www.cs.dartmouth.edu/~pkilab/acapps. shtml González-Tablas, A. I., Wouters, K., & Ramos, B. (2004). Teaching X.509/PKIX based digital signatures while enhancing non-repudiation of a Web based assessment system. In Proceedings of the IADIS International Conference WWW/ Internet, Madrid, Spain (Vol. 1, pp. 43-51). González-Tablas, A. I., Wouters, K., Ramos, B., & Ribagorda, A. (2007). EVAWEB: A Web-based assessment system to learn X.509/PKIX digital signatures. IEEE Transactions on Education, 50(2), 112-117. Hsu, C., & Backhouse, J. (2002). Information systems security education: Redressing the balance of theory and practice. Journal of Information Systems Education, 13(3), 211-218. ISO/IEC 13888-3. (1997). Information technology: Security techniques. Non-repudiation: Part 3. Mechanisms using asymmetric techniques.

Lee, K. C., et al. (1997). Design and implementation of important applications in a Java-based multimedia digital classroom. IEEE Transactions on Consumer Electronics, 43(3), 264-270. Lister, R., & Jerram, P. (2001). Design for Webbased on-demand multiple choice exams using

275

EVAWEB V2

XML. In Proceedings of the IEEE International Conference on Advanced Learning Technology: Issues, Achievements and Challenges, Madison, WI (pp. 383-386). Pain, D., & Le Heron, J. (2003). WebCT and online assessment: The best thing since SOAP? Educational Technology and Society, 6(2), 62-71. PGP. (n.d.). The International PGP Home Page. Retrieved from http://www.pgpi.org Shafarenko, A., & Barsky, D. (2000). A secure examination system with multi-mode input on the World-Wide Web. In Proceedings of the IEEE International Workshop on Advanced Learning Technology: Design and Development Issues (IWALT 2000), Palmerston North, New Zealand (pp. 97-100). Shepherd, E. (2003, October 20). Delivering computerized assessments safely and securely. The e-Learning Developers’ Journal, pp. 1-9.

Steinemann, M.-A., Zimmerli, S., Jampen, T., & Braun, T. (2002, May 20-22). Global architecture and partial prototype implementation for enhanced remote courses. In Proceedings of Computers and Advanced Technology in Education (CATE 2002), Cancun, Mexico (pp. 441-446). Sura, P. K., & Mukkamala, R. (2003, June 2326). A PKI architecture for academic institutions: Design and prototype. In Proceedings of the International Conference on Security and Management (SAM’03), Las Vegas, NV (Vol. 1, pp. 205-212). Warren, M., & Hutchinson, W. (2003). Information security: An e-learning problem. In Proceedings of the Second International Conference on Advances in Web-Based Learning (ICWL 2003), Melbourne, Australia (LNCS 2783, pp. 21-26). World Wide Web Consortium (W3C). (2006). Re-Zhou, J. (2001). Non-repudiation in electronic comquirements for WCAG 2.0 (W3C Working Group merce. Norwood, MA: Artech House Publishers. Note).

This work was previously published in the International Journal of Web-Based Learning and Teaching Technologies, Vol. 3, Issue 1, edited by L. Esnault, pp. 21-32, copyright 2008 by IGI Publishing (an imprint of IGI Global).

276

277

Chapter 17

SOA-Frameworks for Modular Virtual Learning Environments: Comparing Implementation Approaches Fredrik Paulsson Umeå University, Sweden Mikael Berglund Umeå University, Sweden

AbstrAct A general SOA framework for Virtual Learning Environments, based on the VWE Learning Object Taxonomy, is suggested in this chapter. Five basic and general services are suggested for implementation of modular Virtual Learning Environments. The design of the service framework was tested by implementation in two prototypes, using two different approaches where a Java-RMI based implementation was compared to a Web Service (SOAP) based implementation. By implementing the VWE Learning Object Taxonomy and the VWE SOA framework, the prototypes showed that a level of modularity, similar to the level of modularity of Learning Objects, could be achieved for the Virtual Learning Environment as well. Using the VWE Learning Object Taxonomy, this was accomplished by including the learning content and the Virtual Learning Environment into the same conceptual space. The comparison of the prototypes showed that the Web Service approach was preferred in favor of the Java-RMI approach. This was mainly due to platform neutrality and the use of the http-protocol. The study was supplemented by an analysis of the two approaches in relation to a third, REST-based approach.

IntroductIon One of the greatest challenges for Technology Enhanced Learning (TEL) is to achieve flexibility in the use and implementation of learning technology. One of the most obvious needs for teachers is pedagogical flexibility; that is flexibility to choose DOI: 10.4018/978-1-60566-938-0.ch017

pedagogical methods, to choose (or not to choose) functionality and “tools”, as well as the flexibility given by the ability to change, and modify the Virtual Learning Environment (VLE) in a way that is responsive to how the pedagogical context develops – that is, the ability to modify the VLE at run-time. This kind of pedagogical flexibility depends on technical flexibility in terms of adaptability and adaptivity.

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

SOA-Frameworks for Modular Virtual Learning Environments

The current practice of using “learning platforms” such as Learning Management Systems (LMS), does not handle flexibility very well. One problem is that the functionality is restricted to what is currently available in the used product, and even though many LMS implement standards for learning technology, they are still often considerably proprietary, and monolithic, which make them act as information silos. The use of standards is often limited, and commonly restricted to standards for digital learning content (DLC) rather than standards for system architecture, and infrastructure, such as API’s, protocols and data formats. A consequence of this is a heterogeneous infrastructure, consisting of several isolated islands, and each new tool that is not a part of the LMS, easily becomes a new isolated island. This phenomenon is sometimes referred to as the “silo-effect” where each system owns and maintains its own data and functionality, and where no consideration is taken to the overall infrastructure, or to reciprocal interaction and reuse of information and services in a local or a global perspective. The silo-effect is especially troublesome for LMSes since it makes the development of VLEs approach in an opposite direction than the rest of the web – compared to development trends such as represented by Web 2.0, where modularity and non-proprietary exchange of data, information and services are essential characteristics and carriers of the very concepts themselves. However, this situation is slowly changing as architectural standards that facilitate modularity and openness are slowly maturing and LMS vendors have started to adopt them. Such leading examples are the Sakai and the eFramework (see below), as well as specifications such as the IMS General Web Services (IMS, 2006), and the recent Common Cartridge specification from IMS, that supports packagings and exchange of data, learner information and learning content, as well as the notion of (still rudimentary) mash-ups in order to form simple composite applications the “Web 2.0 way” (IMS, 2008).

278

Paulsson (2008) argue for an approach emanating from a learning architecture and a learning infrastructure point of view, rather than focusing on LMS systems that are packaged as, more or less, monolithic products, which is currently a common practice. In order to achieve this there is a need for standardized architectural frameworks and reference models that support modularity and bring a holistic perspective to the learning infrastructure. Previous experiences show that modularity is an efficient approach to achieving enhanced flexibility, as well as other advantages, such as reusability, and better support for evolutionary development that, in the long run, lead to better stability and sustainability of the infrastructure. Component-Based Software Engineering is one area where modular approaches have been used for a long time and where such benefits are experienced, as described (See e.g. Williams, 2001; Szyperski, 2002; and Erl, 2007). Modular approaches have been tested within TEL as well, and Learning Objects, that addresses modular learning content, is by far the most referred modular approach. Learning Objects are based on the idea of small, context independent, “chunks” of digital learning material that can be aggregated (to later be disaggregated again) to form larger units of learning content, sometimes referred to as Learning Modules or Sharable Content Object as in SCORM (Thropp & Dodds, 2006), for use in a specific learning context. Learning Objects have been around for more than a decade, and is a well-established concept. However, in (Paulsson, 2008), (Paulsson & Naeve, 2006b), and (Paulsson & Naeve, 2006a), we argue that even though the concept is well-established, it is still not sufficiently defined in functional or in technical terms, to be useful in a way that give Learning Objects the characteristics that they are usually ascribed. In (Paulsson & Naeve, 2006a), we argue that Learning Object definitions must be narrowed down, and that technical properties must be made explicit. The Learning Object community needs to turn back to the original

SOA-Frameworks for Modular Virtual Learning Environments

source of inspiration, and once again adopt ideas and solutions inspired by object-orientation and component-based software engineering. This, combined with some basic software architecture considerations, would not only help in solving some of the technical contradictions, but some of the pedagogical issues as well (Paulsson & Naeve, 2006a). A couple of interesting projects that address modularity exist on the architecture level as well. Most of the projects address modularity through the use of SOAi. Some of the more influential initiatives are briefly described in the following section. The IMS General Web Service (IMS GWS) specification, described in (IMS, 2006), focuses on providing solutions for some of the most common issues. As the name implies, it is a part of the IMS specification family. The IMS GWS is not a complete architecture framework or reference model and it is in that respect not directly comparable to the architecture model and frameworks presented and discussed in this paper. Another relevant project is the MIT OKI, and the associated Open Service Interface Definitions (OSID:s) (“The Open Knowledge Initiative”, 2005), which are being implemented by Sakai, (Counterman et al., 2004). Sakai uses the uPortal portal framework to tie together and expose its functionality. The e-Frameworkii, (Olivier, Roberts, & Blinco, 2005), is another project that was initiated by the British organization JISCiii and that has now turned into an international project. The e-Framework specifies services for learning on a quite detailed pedagogical level, while the OKI OSID:s are more general and by that also more similar to the Virtual Workspace Environment (VWE) architecture framework presented below. The OKI OSID:s, and the e-Framework approaches were compared by Norton, in (Norton, 2004). Even though Norton’s comparison addresses earlier versions of Sakai and e-Framework it is still relevant and illustrative in many respects.

The primary objective of this article is to show how a modular VLE can be implemented based on the same modular ideas and concepts as Learning Objects. We suggest a SOA framework that corresponds to the VWE Learning Object Taxonomy, presented in (Paulsson & Naeve, 2006b), where the VLE and digital learning content are incorporated into the same conceptual space by the application of a common abstract model, expressed by a taxonomy. As a proof of concept, two different reference implementations of fully working prototypes of the VWE architecture framework were made, using two different SOA-approaches. The two reference implementations were compared within the context of the VWE architecture framework, and the objective was not to make a general comparison of Java RMI versus Web Service technology. Such work has been done by others, such as by Gray, (Gray, 2004), and Juric et al, (Juric, Kezmah, Hericko, Rozman, & Vezocnik, 2004).

An Abstract model and taxonomy for modularity In (Paulsson & Naeve, 2006b) we suggest an abstract model and taxonomy for Learning Objects, based on the modular concepts described in the previous section: the VWE Learning Object Taxonomy. The VWE Learning Object Taxonomy is compatible with many of the existing taxonomies and definitions, such as the Atomic Taxonomy by Wiley (Wiley, 2002). Beside the architectural issues that were described in the previous section, the main difference is that the VWE Learning Object Taxonomy originates from the hypothesis that there is a need for VLEs based on a similar, modular concept as Learning Objects. That is modular VLEs composed of small chunks of “functionality” that can be aggregated (and disaggregated) to form VLEs that are adapted to specific pedagogical contexts, and that are able to function in symbiosis with Learning Objects. hence, the rationale behind the VWE Learning Object Taxonomy is based on two assumptions:

279

SOA-Frameworks for Modular Virtual Learning Environments

Figure 1. A concept-map outlining the abstract model of the VWE Learning Object Taxonomy

1)

2)

There is a need for modular VLEs that are adaptable, in the same manner as modular digital learning content, in order to match the potential flexibility created by modular digital learning content. It is usually hard, often impossible, (in terms of functionality) to establish where the content ends and where the VLE starts. The effect of this is that application logics, data and presentation are mixed in a way that often makes it impossible for Learning Objects to have the alleged characteristics. Adding application logics that is isolated to the context of a specific piece of learning content that is unable to act together with the VLE renders the effect of binding data to the context of a specific piece learning content through dependencies between the three layers (data, application logics and presentation). Those problems were discussed in detail in, (Paulsson & Naeve, 2006a).

The VWE Learning Object Taxonomy facilitates the separation of application logics, data, and presentation by imposing some basic architectural principles, such as layering of the architecture. Figure 1, (Paulsson & Naeve, 2006b), shows that the VWE Learning Object Taxonomy introduces a couple of new concepts. The two differ-

280

ent types of Resource Objects are of particular relevance. The purpose of the Resource Objects is to add application logics in a way that separates logics from data and presentation. The first type of Resource Object is called Helper Resource Object, and its purpose is to render and “support” data and content with application logics. The task of the Helper Resource Object is similar to the task of a web browser plug-in, for example a browser plug-in for displaying a graphical format, such as VRMLiv, SVGv or similar. Helper Resource Objects are used to make the fundamental building blocks of a Learning Object, such as a Fundamental Data Object (Fundamental Learning Object according to Wiley (Wiley, 2002), usable in the context of a Grouped Learning Object or Learning Module, by providing the application logics needed for data processing, interactivity and presentation. The Creator Resource Object is the second type of Resource Object and it is primarily used to add functionality (i.e. application logics) to the VLE, or to Grouped Learning Objects. Creator Resource Objects are basically small, stand-alone pieces of “software” that can be used as building blocks, for both the creation of digital learning content and the learning environment, in a modular learning infrastructure. Besides acting as building blocks, Creator Resource Objects are responsible

SOA-Frameworks for Modular Virtual Learning Environments

for “keeping the VLE together” in terms of managing service pools and create a loosely coupled integration of the different components, as well as for managing the VLE’s internal and external interaction.

modularity and service orientation The VWE Learning Object Taxonomy mainly addresses aspects of modularity on the client. However, in order to achieve modularity on the client, there is a need for a corresponding modular architecture on the server as well. In this article such corresponding architecture, based on a Service-Oriented Architecture (SOA) on the server(s), is presented. This means that each Creator Resource Object may have one or more corresponding server-side services. The main reason for exploring SOA approaches is that SOA, and the associated concept of using loosely coupled services, is suitable for achieving modularity as well as for achieving system independence and distribution of services and application logics. The basic idea of SOA is that software functionality is exposed and delivered on a network as services, using certain standard interfaces, protocols and data models, disregarding internal implementation details of the underlying systems providing the services. In this way, functionality from software components in the architecture can be exposed to, and utilized, by other systems, software components (including other services), or even clients within different context, in order to access data and logics. SOA is often discussed in technical terms, and on the basis of implementation standards, such as the Simple Object Access Protocol (SOAP) and the Web Service Description Language (WSDL). This has led to that SOA is often associated with Web Service technology. SOA and Web Services are sometimes even used as synonymous terms. There are however other SOA-alternatives where services are implemented using other technologies such as CORBA, Java RMI or REST. In this article SOA is regarded to

be a design paradigm rather than a technology platform. As a design paradigm, SOA has different associated design characteristics, design principles, standards and best practices (preferably expressed as designed patterns) depending on the used approach and technology platform (see e.g. Erl, 2007). A service in a SOA should be provided as encapsulated and loosely coupled software component. Loose coupling is one of the most important characteristics of SOA, since this is the property that separates data from the processing of data, and by that the type of service from the software providing the service, (He, 2003). That is, hiding the implementation of a service from the service consumer. Loose coupling is also important for modularity, as services can be “pooled” (or aggregated) together through to form composite applications that provides new functionality in new contexts – such as in the context of a VLE. In VWE such pooling is accomplished by combining different resource objects that form grouped objects, which in turn are grouped into components, called VWE Tools, which are grouped into one or more VWE Workspace(s) – i.e. a VLE containing personal and shared learning spaces. Every VWE Workspace that is aggregated is in fact a composite application consisting of services that may be distributed on the network together with software components that runs in the web browser. Each component utilizes a pool consisting of one or more services from the VWE service framework (figure 2) or “tool-specific” services, provided by a tool-specific- or third party server, which, in turn, may very well be composed out of, and utilize, other VWE services. Modularity creates the flexibility of dynamically adding and removing functionality and in VWE this works at run-time. One important effect of the achieved flexibility is exchangeability, which is an important condition for adaptability. The exchangeability makes it possible exchange one component in favor of another, such as for example exchanging a calendar service in favor

281

SOA-Frameworks for Modular Virtual Learning Environments

Figure 2. A bird’s view of the SOA framework

of another calendar service that is more suitable within a specific context, such as different calendars for different age groups or uses, for example. The remainder of this article describes the VWE SOA architecture framework, and compares and discusses different approaches to implementing a modular VLE based on the VWE Learning Object Taxonomy and the corresponding SOA architecture framework. Chapter 2 describes the implementation of the two prototypes, using two different approaches to SOA: Java/RMI and Web Services. The result of the work is presented and discussed in chapter 3, where future directions are also suggested and compared to the RMI and web service approaches, based on experience from other projects.

jects. The VWE SOA architecture framework, described below, was used as the “blueprint” for the implementation of two VLE prototypes, using two different technology frameworks: the first prototype (VWE 1) was implemented using a Java/RMI based approach, while the second prototype (VWE 2) was implemented using Web Service technology. The overall architecture model is basically the same for both implementations, but there are some minor differences that are illustrated by figure 2 and figure 4. Those differences were the effect of two things: for enhancing the framework by learning from the first implementation, and by necessity in order to adapt to the different technology platforms. The VWE architecture and the two prototype implementations are described in the following sections where the differences of the architecture model are also discussed.

metHodologY And ImplementAtIon

A common Architecture Framework

A matching architecture framework was needed in order to make use of the modular approach suggested by VWE Learning Object Taxonomy. SOA was found to be suitable for implementing modular architectures in the sense that a service in a SOA can be regarded as the equivalent to a module that provides certain functionality and data – very well corresponding to Resource Ob-

282

The purpose of the VWE architecture framework is not to determine exactly which services are needed in an e-learning architecture. Focus is instead placed on defining a general SOA framework that addresses general needs, without adding the limitations (discussed earlier) that are often the case when approaching the VLE using concepts such as LMS, LCMS and similar. Our thesis is that a general SOA framework can be used as a com-

SOA-Frameworks for Modular Virtual Learning Environments

Figure 3. Outlines the general design for each Tool-Service implementation. Even though figure 3 shows the Web Service implementation (using SOAP), the basic design principle, with Tool/System(/proxy)/ Service, is the same for both prototypes

mon basis for implementing additional, and more TEL-specialized services, preferably through the use of different standard frameworks, intended for this specific purpose, such as the e-Framework, (Norton, 2004) or the OKI (Thorne, 2003). The VWE SOA framework is currently implemented using Java for both of the prototype reference implementations. Figure 2 outlines the VWE SOA framework. As this is an experimental prototype implementation of the VWE SOA framework, there is a certain Java focus for practical and efficiency reasons, emanating from the fact that the use of Java made it possible to reuse code developed in other projects, such as the SCAM project (Palmér, Naeve, & Paulsson, 2004). In our opinion, this does not affect the results - in terms of comparing two approaches to SOA and providing a “proof of concept” - of the work presented in this article.

tools and workspaces A Tool is the metaphor used to describe the functionality modules that are composed by one or more Resource Object–service pairs. A tool can make use of more than one service, and can be composed by pooling existing (and new) services through the aggregation of resource components.

A service that corresponds to a specific tool commonly uses services exposed by the general SOA framework and/or by other tools as well. Tools can for example use the File Service for storing and managing files, the User Service for managing users and permissions and so on. Figure 3 outlines the general design pattern that was used for implementing VWE Tools. A Workspace is the metaphor used for the part of the system that users interact with, including the GUI. The workspace can in a way be compared to a computer desktop in the browser. It is the interface that keeps personal and shared environments together and it is the place from where all tools are managed and interacted with. Hence, all interaction with the VLE is carried out using a web browser. A user can have any number of associated workspaces, shared as well as personal. Workspaces, or rather tool-instances used in workspaces, can be shared by several workspaces. A small Java application, called the Kernel, is downloaded to the browser when a workspace is accessed and initialized. The Kernel works as a “broker”, and manages the interaction between the workspace representation in the web browser, and the system, represented by the server-side services. As an example, in VWE 2, workspaces are created using a tool called Workspace Editor, by the method

283

SOA-Frameworks for Modular Virtual Learning Environments

Figure 4. outlines the architecture of the Java RMI implementation in VWE 1

String createWorkspace (WorkspaceDTO ws. The Workspace Editor generates the workspace and all corresponding data, including metadata and a RDF-based structure for the workspace, which is also compliant to the IMS Content Packaging specification, described in (IMS Global Learning Consortium, 2004), but expressed using a binding to RDF (Berners-Lee, 1998). VWE workspaces use the Workspace Service as their “corresponding service”, and the Workspace Service handles everything that is related to creating and managing workspaces. Both VWE tools and Learning Objects are added at the time of creation, but a workspace can be modified at any time, and functionality and content can be added or removed at any time. Tools are dynamically loaded, which means that a tool (in this case a jar-file) is downloaded first when it is requested. Dynamic loading has some advantages; the latest version is always loaded, it decreases initial download, and the system is not limited to server execution. However, dynamic loading has some disadvantages as well: the system relies on a working broadband connection and it can be sensitive to browser-specific implementations. Users may also experience situations that they find confusing where one version of a tool is downloaded at one occasion and another, newer version, is downloaded at the next occasion. The prototype implementations uses Java on the client, but tools could very well be implemented using any

284

browser-based technology, such as Ajax, Flash or ActiveX. The main requirements for a tool to be implemented in VWE are that it must work in the web browser and that it must implements the needed service interfaces. The VWE SOA framework has five basic services, illustrated by figure 2 and figure 4. The User Service is responsible for everything that is related to users, such as access permissions (through the use of ACL:s), user-workspace relations and so on. The User Service is able to store user related information in an ordinary SQL database or in a LDAP catalogue. The Message Service handles all asynchronous messaging through channels. Each tool that implements the Message Service creates a specific type of channels. A channel is created when a user initiates a tool. The main reasons for designing a common Message Service is to avoid the complexity of several different messaging implementations. The level of complexity is also decreased, by hiding messaging complexity for tool implementers who can then focus on implementing core functionality instead. The Workspace Service keeps the VLE together. It is used to create new workspaces, assigning workspaces to users and user groups, load workspaces at log-in, associate tools with workspaces, and so on. The Workspace Service interface is used for the interaction between tools and workspaces. The structure of a workspace is

SOA-Frameworks for Modular Virtual Learning Environments

expressed using an RDF representation of the IMS Content Packaging format and when a workspace is launched, a Content Package is downloaded to the browser by the VWE kernel. The Workspace Service uses a WorkspaceDTO as its data transfer object and this type of DTO is also used as the data type for representing workspace data in the SOAP communication between tools (ToolDTO) and workspaces (Workspace DTO) in the Web Service implementation of VWE. The Tool Service is responsible for locating, loading, running, managing, and deploying tools and content. Every component in the system has its associated metadata, which means that extensive amounts of metadata have to be managed by the Tool Service. In general, the more modularized a system becomes, the more sophisticated metadata is needed in order to describe artefacts, services and relations. For this reason all metadata in the system are managed using RDF and stored in a SCAM-repository (Paulsson, 2003). RDF can be used for representing very simple data models, as well as for representing very complex data models, which means that the use of RDF helps in making the system flexible. Another reason for using RDF is the machine-readable and machine-processable semantic capabilities of RDF. The semantic capabilities of RDF create a foundation for adaptivity, as metadata can be used as input for automatically modifying the VLE based on input from users, input from the system, or the outcome pedagogical activities. VWE primarily uses metadata for three things: to add data used for locating resources, for describing properties for humans, and for describing properties for system use. The ToolDTO is the carrier of metadata for a specific tool or object and it manages the interaction with the Workspace Service. Tool metadata are cached the first time they are accessed for performance reasons. If the tool is a Helper Resource Object, the ToolDTO manages the metadata for its associated Learning Objects as well. Even though the systems native data format for metadata is RDF, the metadata for Learning Objects and VWE Tools are still

compliant with the Learning Object Metadata (LOM) standard (IEEE, 2002), with the exception of three system specific metadata fields that are only used internally. The LOM compatibility was chosen for interoperability reasons and was achieved through the use of the LOM-RDF binding (M Nilsson, Palmér, & Brase, 2003). Every tool must implement the Tool API in order to function with the system. The general use and the role of RDF in TEL is further discussed in (Paulsson & Engman, 2005; Daconta, Obrst & Smith, 2003; M Nilsson, Palmér, & Naeve, 2002). The File Service is used for storing and managing files in a distributed “file system”. As metadata are expressed using RDF, files must be identified using an URI, pointing at the resource. As with all other metadata used within VWE, A SCAM repository (Paulsson, 2003; Palmér, Naeve, & Paulsson, 2004) is used to manage file metadata and the files can be stored in any WebDav enabled storage. VWE 2 uses a SCAM ePortfolio as WebDav storage, while VWE 1 uses a more traditional database store. The different services are accessed via their corresponding “system” which provides an API that allows access to the service. See figure 3. The two reference implementations are bundled with a few “pedagogical” example tools (such as a language training tool), a couple of “production” tools (such as text editing simple graphics), a couple of generic tools for collaboration (such as shared whiteboard and chat), a couple of tools for editing and deploying new tools, and couple of administrative tools for creating and managing workspaces, administrating users etc.

Vwe 1: the Java-rmI Implementation The first reference implementation of the VWE architecture framework, called VWE 1, was implemented using the Java-RMI (Java Remote Method Invocation) API. Java-RMI is the Java API for remote procedural calls (RPC). As an alternative

285

SOA-Frameworks for Modular Virtual Learning Environments

to Java RMI, CORBA and RMI-IIOP were evaluated. RMI-IIOP is especially interesting from an interoperability point of view, as it would make the implementation less Java specific. RMI-IIOP was however found to be too complex to implement, and at the same time it created unwanted dependencies in terms of bindings to CORBA Object Request Brokers (ORB), which made the implementation of VWE tools, especially on the client, more complicated and dependent of an existing ORB implementation. The development of VWE 1 started in 1998, and the availability of Open Source ORB-implementations was limited. The result was that a “clean” Java RMI approach was chosen instead. Java JINI technology for distributed system was used for client- server lookup, in order to make the system more transparent from a modularity perspective. The use of JINI gave the system the flexibility needed for handling the adding and removing of tools and services without the need of manually managing a registry. This made the overall system reasonably independent of single services and when a non-critical tool service disappears from the network, the rest of the system stays unaffected. The same goes for the opposite, as non-critical services are added the rest of the system stays unaffected, apart from that the system can make use of a new service that it becomes aware of through JINI.

Initialization of Vwe 1 The VWE Tools were developed as Java applications, implementing the vwe.tool.Tool interface, which contains methods that enable the kernel to interact with VWE Tools. The VWE 1 Service Architecture, illustrated by figure 4, shows how the tools run in a separate layer above the different systems. The systems are the API:s that are used by VWE Tools to access VWE Services. Each system has a corresponding proxy, in the same way that each service has a corresponding system. The Proxies implement references to the

286

corresponding RMI Servers, implementing and exposing their respective services. VWE Tools function as clients to the different systems in the Kernel, which in turn works as consumers of the services provided by the VWE framework. Each system holds a remote reference to a corresponding service, and each VWE Tool is implemented as a Java Applet. When a certain VWE Tool is needed it is downloaded on demand, using the client class “LoaderApplet”. The LoaderApplet is initialized at the beginning of a VWE session as the browser downloads the LoaderApplet. The LoaderApplet starts by sending a query to the JINI Lookup Service, creating a reference to the “LogIn User Service” and the “Tool Service” created for the LoaderApplet. A ticket is created that is used for authentication. User permissions and access rights are setup and handled by the User Service, using Access Control Lists (ACL). The ACL(s) are kept by each service in order to make services as independent as possible. Services that reside in the Service Layer are implemented as Java RMI applications. The VWE Kernel, which contains the core classes (figure 4), is downloaded to the browser using the tool service. The VWE Kernel contains all classes that are used by the system on the client side, with an exception of VWE Tools, which are downloaded when they are requested. The discovery and initialization of the remaining services are carried out in the same way as described above for the User Service. The RMI applications of the VWE serviceframework implement the Activation-framework, which causes each service to register itself to the RMID (RMI Activation System Daemon) (Grosso, 1998), pp 375. The services are instantiated and activated when they are requested and each service registers itself to the JINI lookup service in order to make it possible for clients to discover them. A configuration file is associated with each service, which points out class-files and other resources that are vital for the service to work. VWE 1 implements five general services: the User Service, the Tool Service, the Workspace

SOA-Frameworks for Modular Virtual Learning Environments

Service, the Message Service and the File Service. Each of the services is designed to fulfill general tasks needed within a VLE, and sometimes also outside a VLE, rather than tasks that correspond to specific to pedagogical requirements. The idea is that VWE SOA Framework can be used as a basic infrastructure, upon which pedagogically oriented services can be implemented in a modular fashion, utilizing the VWE service framework, and additional services can be developed as consumers of the infrastructure provided by the general VWE SOA Framework.

Vwe 2: the web service Implementation The VWE 2 implementation was also implemented using Java. However, the implementation was based on Enterprise Java, and mainly Enterprise Java Beans (EJB), instead of RMI Servers. There were two reasons for this change. The main reason is that RMI Servers are rarely used anymore in the way that they were used in VWE 1 since Enterprise Java can be used to make much more efficient implementations. This brings us to the second reason: Enterprise Java has some features that hide some of the complexity of implementing this kind of distributed systems, and furthermore, Enterprise Java has some support for implementing Web Service technology. However, the most important changes, from the perspective of this article, are in the System/Service layers where the old RMI-based Service API was exchanged to a SOAP-based Service API. Technically, some services may consist of more than one service, even though the VWE SOA framework only sketches one core service. This is for example the case with the user service, which is poled with a permission service, in the soap-implementation, while the permissions were handled on a per service basis in VWE I, which meant a duplication of functionality. One important reason for choosing a Web Service approach was that Web Service technology

has quickly arisen to become a de facto standard for SOA on the Web. The success of Web Service technology is most likely due to its programming language and platform neutrality, as well as the fact that it is relatively simple, compared to previous platform neutral approaches for distributed systems, such as CORBA, which is discussed in (Szyperski, 2002), pp 231. Web Service technology is associated with a number of standards and specifications that are all essential for Web Services to work properly. Table 1 gives an overview of the Web Service protocol stack. A couple of the most important specifications and their role in the implementation of VWE 2 are described below.

the use of soAp in Vwe 2 SOAP, which is an XML-based protocol for exchanging messages, is used for distributed services in VWE 2 instead of the RMI-based servers used in VWE 1. SOAP works by exchanging messages between the different components involved in the system interaction. The primary purpose of SOAP is to provide an envelope, where the header and the body of the SOAP message are put. The header contains metadata about the message (such as date), whereas the body contains the actual message. A SOAP message may also contain a fault section for managing exceptions. One of the advantages of SOAP is that it can be used together with all of the most common Internet protocols. The most frequent practice is of course to use SOAP together with http, but SOAP can also be used together with other Internet protocols as well, such as SMTP. Similar to the RMI-based implementation, all services use a common design pattern, illustrated by figure 3, which consists of three parts: a Tool, which runs on the client (in the web browser), a System, which is a Java API for interaction with services, and a Service, which is the SOAP service that is exposed externally. This means that the core

287

SOA-Frameworks for Modular Virtual Learning Environments

Table 1. The core Web Service protocol stack. The table is based on the table by Szyperski, in (Szyperski, 2002), page 229. Usage

Specification/protocol

Comment

Discovery

UDDI

Itself a Web Service, UDDI serves as a directory for Web Services.

Description

WSDL, WSFL/XLANG, others to come

Given one or more Web Services, describe properties at a meta level.

Access

SOAP, SOAP with attachments

Given a Web Service instance, access it via messages.

Transfer

HTTP, SMTP, others

Transfer SOAP-messages, incl. attachments.

Transport

TCP/IP, UDP, others

Transport data.

part of the design is the same as for the RMI-based VWE 1. This similarity is an advantage in the comparison between the two implementations, even though it is not a conclusive factor.

the use of wsdl in Vwe 2 Web Services Description Language (WSDL) is used to describe the interface of a web service. The WSDL description makes it possible for third party to consume the web service. WSDL is usually used to describe properties, such as the location of a web service, message formats, protocol bindings, available operations provided by the web service, available ports, and so on. A WSDL description works much like an abstract view of an API and its data types, and it is in some ways comparable to the role of IDL. The service implementation is separated from the definition and WSDL files are used to create stubs and skeletons for different programming languages, such as The VWE client libraries that are created from the WSDL specifying the services.

the use of uddI in Vwe 2 VWE Resource Objects, services, and tools can locate services by asking a directory service. Universal Description, Discovery, and Integration (UDDI), a general registry for description and discovery of Web Services, is used for this

288

purpose. The main purpose of UDDI is to provide a catalogue for Web Services and their interfaces, protocol bindings and message formats. UDDI uses a tModel to point to a service specification, as an alternative to keeping the data within the registry. UDDI stores descriptions, such as pointers to a service access point and the tModel, which represent technical specifications and type of service. VWE 2 uses UDDI as a naming service for registration and discovery of SOAP services, and every VWE 2 service has its own unique tModel. Using Universal Unique Identifiers (UUID) ensures the uniqueness of each tModel. VWE 2 has no automatic way of registering an installation in UDDI, and the registration has to be made manually, which is not currently a problem since the it is only a reference implementation. When VWE is initialized, the LoaderApplet is downloaded and started, and the user can login. During the login, the LoaderApplet searches the UDDI registry for instances that implement all five of the VWE services. Different VWE instances are presented in the login screen, where the user can choose between them.

results And dIscussIon In this article we have presented an architecture framework that corresponds to the VWE Learning Object taxonomy, presented in (Paulsson & Naeve,

SOA-Frameworks for Modular Virtual Learning Environments

2006b). The two reference implementations clearly show that it is possible to use a modular approach, similar to the Learning Object approach, for the VLE as well, and that a quite simple architecture framework can be used to accomplish this using a SOA approach. The VWE SOA framework and the two reference implementations can be regarded as a “proof of concept” that clearly confirms that it is possible, as well as realistic, to approach digital learning content and the VLE using a common modular model. The SOA approach is suitable for modular architectures, as well as it is suitable for systems that are distributed and delivered as services on the web. Similar conclusions are also made by Szyperski (2002), Sim et al. (2005), and by Makola et al. (2006), as well as by many others. Even though it seems clear that SOA is a reasonable approach for implementation of the VWE architecture framework, there are still a number of design considerations that need be made, mainly related to implementation strategy and implementation details. There are number of different ways in which SOA can be approached and we have compared and evaluated two of them on the path to demonstrating that a modular VLE can be implemented based on the VWE Learning Object taxonomy, as well as implemented according to the architecture framework presented in this paper. A conclusion that can be drawn by comparing the two implementations is that there are several reasons for choosing an approach based on web service technology, over an approach based on Java/RMI. One of the most important reasons is that the web service approach is more platformand programming language neutral. This allows new VWE tools to be developed using other client technologies than Java. SOAP also neutralizes some of the problems with firewalls. Firewalls stopped the VWE communication in many cases in the RMI-based implementation. This is a serious problem when targeting educational institutions, as they are usually behind firewalls and those

firewalls are not always well configured. The Web Service approach enables functionality from legacy systems to be exposed as Services, and implemented as VWE Tools. This is important, as many educational organizations have a heavy legacy that needs to be considered. By enabling the inclusion of legacy there is a better chance for acceptance of a modular approach, as the change will be less revolutionary. In a way, this concludes one important part of the rationale for modularity: to enable evolution instead of revolution. The IT infrastructure must be made living and dynamic, and for this to work, information silo type system must successively be dismantled. In addition, Web Service technologies are catching on fast as being de facto standard, as well as the formal (de jure) standard for distributed services on the Internet. This enhances the interoperability and adaptability to other systems. There are especially two attributes that give SOAP such characteristics: SOAP is non-binary and XML-based and it can use HTTP (and other common Internet protocols) for transport. The non-binary format of SOAP can be considered as being both an advantage and a disadvantage. It is an advantage as it uses XML, which is easily read by humans and parsed using standard XMLparsers. It is a disadvantage as the non-binary XML-format contains a lot of redundancy that becomes processing overhead, which considerably slows down the performance of a system that exchanges large amounts of messages. According to Govindaraju et al. (2000), SOAP is generally about ten times slower than similar, binary protocols, such as RMI. However, our tests show that this is not a real issue in VWE 2, since, not even with synchronous collaborative tools, such as the shared whiteboard, have any serious performance issues been experienced or perceived by users and minor latency is usually acceptable. In spite of this, SOAP performance issues could turn out to be a potential problem for other tools in the future. There are however other bottlenecks that is far more influential, such

289

SOA-Frameworks for Modular Virtual Learning Environments

as the poor performance of client-side Java and poor networks with questionable quality and low bandwidth. One reason for comparing two different approaches to SOA is the fast developmental pace of technology. The Java/RMI approach was among the best available when the development of the first prototype started, but Web Service technology arose as a strong competitor during the project and there was a need to evaluate alternative approaches. Implementing the abstract model and the VWE framework using different approaches was at the same time a way of getting a proof of concept that illustrates that they are technology neutral in terms of technology platforms for SOA. The developmental pace of technology continues to be very fast and future research will be made to evaluate other approaches. One quite recent and alternative SOA approach that seems promising is the Representational State Transfer (REST). REST is an architecture style that was first described by Fielding (2000). The RESTful approach is well adapted to the nature of the web, and the model is less complex than the common, SOAP-based, Web Service approach (see e.g. Richardson and Ruby, 2007). These are some characteristics of REST that may facilitate the development of VWE Tools, as they, potentially, will become less complex, and easier to develop, and at the same time more straightforward to integrate with VWE as well as with other services. Our recent experiments indicates that an environment such as the VWE can be implemented with less complexity and better stability by choosing a REST approach in favour of a Web Service approach. Some experiments are currently being done to evaluate REST for this purpose and besides the properties previously described, the REST approach facilitates the integration of services from different providers, without the need of complicated standards and protocols that has too much room for interpretation when it comes to implementation. So far, some tests have been carried out using the Restlet framework that con-

290

firms this perception, even though the framework is not as well developed (yet) as many of the SOAP-based frameworks are. The advantages of a REST approach are also confirmed by others, such as Fielding (2000), Pitner & Drášil (2006) and Schaafft & Gray (2008). Our recent experiments are still work in progress though. Another development area, with relevance for VWE, is the development of Semantic Web Services. Semantic Web Services can be described as a combination of Semantic Web technology and Web Service technology, in order to make Web Services self contained and self-describing, using semantic annotations that facilitates the use of semi-automatic discovery and use of Web Services. The basics of Semantic Web Services and semantic annotation of Web Services are discussed by Carduso & Sheth (2006) and Nagarajan (2006). The Semantic Web Service approach is still in its infancy, but it looks very promising for using together with a developed version of the VWE SOA architecture framework. Most of all, Semantic Web Services is a technology with the potential of adding adaptivity to an already adaptive infrastructure. In fact, the VWE 2 prototype already implements elementary semantic descriptions of VWE Tools, digital learning content, and services through the use of SCAM. So far Semantic Web Services are mainly addressed with SOAP and RPC based Web Services in mind, however, recent research have started to combine RESTful Web Services and Semantic Web technology, such as in Manouselis et al. (2009) and Lathem, Gomadam & Seth (2007). One of the drawbacks of both of the VWE prototypes is the dependency of Java at the client. This is a problem for several reasons: the use of Java increases download times, it makes the client performance slower, and it creates a dependency to the Java plug-in. There are now technologies available that eliminate the plug-in and Java dependency on the client. One such technology that is promising is AJAX (Asynchronous Java and XML). Ajax can be used to

SOA-Frameworks for Modular Virtual Learning Environments

develop rich and dynamic user interfaces for the browser. Future implementations of VWE will explore the use of Ajax, and if Ajax can be used to implement VWE-tools without too much overhead and complexity. While Ajax can be used to develop rich, browser-based clients, Portlets and other portal technologies can be used to integrate VWE functionality into VWE specific or existing TEL portals, similar to Sakai’s use of uPortal1. Therefore, future research also needs to evaluate implementation of tools, using other technologies than Java as well. Such tools are already possible to implement in theory, but need to be tested against the prototype implementation. There is also a need to consider the inclusion of other, general frameworks for modular learning architectures, such as the e-Framework. A possibility would be to implement the e-Framework SOA on top of the existing VWE architecture framework, as VWE addresses general services, while e-Framework addresses pedagogical services as a high level of detail. The e-Framework could be used as a “blueprint” for implementing VWE Tools that corresponds to e-Framework, but that utilizes the VWE general service framework. However, our resent experiments (described above) indicate that such work could benefit from being implemented on top of a RESTful architecture instead of the current SOAP and RPC based architecture.

reFerences Berners-Lee, T. (1998). Why RDF model is different from the XML model. Retrieved January 9, 2009, from http://www.w3c.org/DesignIssues/ RDF-XML Carduso, J., & Sheth, A. P. (2006). Semantic Web Services, Processes and Applications. New York: Springer Science+Business Media, LLC. Counterman, C., Golden, G., Gollub, R., Norton, M., Severance, C., & Speelmon, L. (2004). Sakai Architecture.

Erl, T. (2007). SOA Principles of Service Design (1st ed.). Boston, MA: Prentice Hall. Fielding, R. T. (2000). Architectural Styles and the Design of Network-based Software Architectures. University of California, Irvine. Govindaraju, M., Slominski, A., Choppella, V., Bramley, R., & Gannon, D. (2000). Requirements for and Evaluation of RMI Protocols for Scientific Computing. Paper presented at the IEEE/ACM SC2000, Dallas, Texas, USA. Gray, N. A. B. (2004). Comparison of Web Services, Java-RMI, and CORBA service implementations. Paper presented at the Fifth Australasian Workshop on Software and System Architectures (AWSA 2004), Melbourne, Australia. Grosso, W. (1998). Java RMI. O’Reilly. IEEE. (2002). Final 1484.12.1-2002 LOM Draft Standard. Retrieved from http://ltsc.ieee.org/ wg12/files/LOM_1484_12_1_v1_Final_Draft. pdf IMS Global Learning Consortium. (2004). IMS Content Packaging Best Practice and Implementation Guide. Version 1.1.4 Final Specification Implementation guidelines. Retrieved from http://www.imsglobal.org/content/packaging/ cpv1p1p4/imscp_bestv1p1p4.html IMS Global Learning Consortium. (2006). IMS General Web Services Specification - Version 1 Final Specification. 1.0 Final. Retrieved January 7, 2009, from http://www.imsglobal.org/gws/ index.html IMS Global Learning Consortium. (2008). IMS Common Cartridge Specification. Version 1.0 final. Retrieved January 7, 2009, from http://www. imsglobal.org/cc/index.html Juric, M. B., Kezmah, B., Hericko, M., Rozman, I., & Vezocnik, I. (2004). Java RMI, RMI tunneling and Web services comparison and performance analysis. Software, Practice & Experience, 36(14), 1543–1562. 291

SOA-Frameworks for Modular Virtual Learning Environments

Lathem, J., Gomadam, K., & Sheth, A. P. (2007). SA-REST and (S)mashups: Adding Semantics to RESTful Services. Paper presented at the International Conference on Semantic Computing (ICSC 2007), Irvine, CA.

Palmér, M., Naeve, A., & Paulsson, F. (2004). The SCAM Framework: Helping Semantic Web Applications to Store and Access Metadata. Paper presented at the European Semantic Web Symposium 2004, Heraclion Greece.

Makola, D., Sim, Y. W., Wang, C., & Gilbert, L. S, G., & Wills, G. (2006). A Service-Oriented Architecture for a Collaborative Orthopedic Research Environment. Paper presented at the WWW2006, Edinburgh, Scotland.

Paulsson, F. (2003). Standardized Content Archive Management – SCAM. IEEE Learning Technology newsletter, 5(1), 40-42.

Manouselis, N., Kastrantas, K., Sanchez-Alonso, S., Cáceres, J., Ebner, H., & Palmér, M. (2009). Architecture of the Organic.Edunet Web Portal. [IJWB]. International Journal of Web Portals, 1(1), 7–19. Nagarajan, M. (2006). Semantic Annotations in Web Services. In J. Cardoso & A. P. Sheth (Eds.), Semantic Web and Beyond: Computing for Human Experience (1st ed., pp. 35-61). New York: Springer Science+Business Media, LLC. Nilsson, M., Palmér, M., & Brase, J. (2003). The LOM RDF Binding - Principles and Implementation. In Proceedings of the Third Annual ARIADNE conference. Nilsson, M., Palmér, M., & Naeve, A. (2002). Semantic Web Meta-data for e-Learning - Some Architectural Guidelines. Paper presented at the 11th World Wide Web Conference (WWW2002), Hawaii, USA. Norton, M. J. (2004). A Comparison between the JISC and Sakai Frameworks. 3rd PDF. Retrieved from http://sakaiproject.org/tech/S040405N.pdf Olivier, B., Roberts, T., & Blinco, K. (2005). The eFramework for Education and Research: An Overview. R1 PDF. Retrieved from http:// www.e-framework.org/Portals/9/Resources/ eframeworkrV1.pdf

292

Paulsson, F. (2008). Modularization of the Learning Architecture: Supporting Learning Theories by Learning Technologies. Doctoral Thesis, Royal Institute for Technology (KTH), Stockholm. Paulsson, F., & Engman, J. (2005). Marking the National Curriculum - a new model for semantic mark-up. In P. Cunningham & M. Cunningham (Eds.), Innovation and the Knowledge Economy: Issues, Applications and Case Studies (Vol. 2, pp. 1731-1738). Amsterdam: IOS Press Amsterdam. Paulsson, F., & Naeve, A. (2006a). Establishing technical quality criteria for Learning Objects. In P. Cunningham & M. Cunningham (Eds.), Exploiting the Knowledge Economy: Issues, Applications, Case Studies (Vol. 3, pp. 1431-1439). Amsterdam: IOS Press. Paulsson, F., & Naeve, A. (2006b). Virtual Workspace Environment (VWE): A Taxonomy and Service Oriented Architecture Framework for Modularized Virtual Learning Environments - Applying the Learning Object Concept to the VLE. International Journal on E-Learning, 5(1), 45–57. Pitner, T., & Drášil, P. (2006). An E-learning 2.0 Environment - Principles, Technology and Prototype. Journal of Universal Computer Science, 2006, 543–550. Richardson, L., & Ruby, S. (2007). A Service Implementation. In RESTful Web Services. Sebastopol, CA: O’Reilly.

SOA-Frameworks for Modular Virtual Learning Environments

Schaaff, A., & Gray, N. (2008). REST in the VO. Retrieved January 9, 2009, from http://www. ivoa.net/internal/IVOA/InterOpOct2008GridAndWebServices/GWS-REST-28October08. pdf Sim, Y. W., Wang, C., Carr, L. A., Davis, H. C., Gilbert, L., Grange, S., et al. (2005). A Web/ Grid Services Approach for a Virtual Research Environment Implementation. Paper presented at the Fourth e-Science All Hands Meeting (AHM 2005), Nottingham, UK. Szyperski, C. (2002). Object and component “wiring” standards. In Component Software - Beyond Object-Oriented Programming (2 ed., Vol. 1, pp. 229). New York: ACM Press. 2005The Open Knowledge Initiative. Retrieved from http://www.imsglobal.org/okiosids/index. html

Wiley, D. A., & Wiley, D. A. (Eds.). (2002). Connecting Learning Objects to Instructional Design Theory: A Definition, a Metaphor and a Taxonomy. Bloominton: Agency for Instructional Technology and Association for Educational Communications & Technology. Williams, J. (2001). The Business Case for Components. In G. T. Heineman & W. T. Councill (Eds.), Component-Based Software Engineering: Putting the Pieces Together (1st ed., Vol. 1, pp. 79-97). Addison-Wesley.

endnotes i

ii

Thorne, S. & Kahn, J. (2003). O.K.I.™ Architectural Concepts. Thropp, S. E., & Dodds, P. (Eds.). (2006). Sharable Content Object Reference Model (SCORM)® 2004 3rd Edition Sharable Content Object Reference Model, Content aggregation Model (3rd ed.). Alexandria, VA: ADL.

iii iv

v

vi

SOA is here used in the sense of being a software design paradigm The SOA-model of the e-Framework (http:// www.e-framework.org/) was previously a part of the eLearning Framework (ELF), but is now incorporated into the e-Framework initiative. http://www.jisc.ac.uk/ Wikipedia on VRML: http://en.wikipedia. org/wiki/VRML Wikipedia on SVG: http://en.wikipedia.org/ wiki/SVG http://www.uportal.org/

293

294

Chapter 18

Towards Automated Specifications of Scenarios in Enhanced Learning Technology A. Rius Gavidia UOC Open University of Catalonia, Spain M. A. Sicilia Urbán UAH University of Alcalá, Spain E. García-Barriocanal UAH University of Alcalá, Spain G. Macarro Palazuelos UAH University of Alcalá, Spain

AbstrAct Recent standardization efforts in e-learning technology have resulted in a number of specifications, however, the automation process that is considered essential in a learning management system (LMS) is a less explored one. As learning technology becomes more widespread and more heterogeneous, there is a growing need to specify processes that cross the boundaries of a single LMS or learning resource repository. This article proposes to obtain a specification orientated to automation that takes on board the heterogeneity of systems and formats and provides a language for specifying complex and generic interactions. Having this goal in mind, a technique based on three steps is suggested. The semantic conformance profiles, the business process management (BPM) diagram, and its translation into the business process execution language (BPEL) seem to be suitable for achieving it.

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

IntroductIon The IMS digital repositories interoperability (DRI) specification (IMS Global Consortium, 2003) suggests recommendations for the interoperation of the most common repository functions. This specification acknowledges that a wide range of content formats, implemented systems, technologies, and established practices already exist; it is tied to IMS specifications on the contents, but it does not assume the heterogeneity of the systems and formats, and does not provide a language for specifying complex, generic interactions. In order to achieve interoperability among systems and formats, the eduSource Canada project has designed and implemented a standard communication protocol, ECL (Eap, Hatala, & Richards, 2004). The ECL protocol is flexible with respect to metadata schemas and repository contents, and it allows new and existing repositories to communicate and share resources across a network. It conforms to IMS DRI specifications and implements its main functions, and, furthermore, it extends the IMS protocol with some definitions based on the OAI harvesting protocol. Also conforming to IMS DRI specification, the PAWSEL project proposes an architecture that intends to facilitate the heterogeneous conversational patterns among participants of the scenario (Macarro et al., 2006). The intermediary level provides the orchestration skills for the rest of the components through service interfaces and the relation among learning services. Their users and intermediaries are understood as scenarios. The SleD2 project presents another architecture that facilitates the integration of learning services instead of Web services. The CooperCore engines such as the IMS LD service are integrated into a workflow engine and satisfy the automation of scenarios but are limited to the delivery of activities (Vogten, Martens, Nadolski, Tattersall, Van Rosmalen, & Koper, 2006).

The term scenario in the e-learning area, and the reutilization of learning objects (LOs) as a scenario-based approach were introduced in an attempt to present the learning-objects metadata as required infrastructure to support some LMS (learning management system) functions (Sicilia & Lytras, 2005). Also, in relation to scenarios, it has been proposed to use the semantic conformance profile (SCP) toward the automation processes (Sicilia, Pagés, García, Sánchez-Alonso, & Rius, 2004). Taking into account this previous work (Macarro et al., 2006; Sicilia & Lytras; Sicilia et al.), and acknowledging the need for having a language for processes specification to describe the fundamental processes in the LMS and all the interactions among the participants,we propose BPMN (business process modeling notation) as the most suitable one. Subsequently, we state the main advantages that BPMN brings about to e-learning. •

•

It supports the exchange formats between applications: The serialization of BPMN is done for XML (extensible markup language) exchange. A comparative study (Mendeling, Neumann, & Nüttgens, 2004) of about 15 different XML-based specifications for BPM (business process management) concludes that BPMN and BPEL (business process execution language) are the languages that satisfy the majority of the items that assure the exchange formats and the interoperability. It reduces heterogeneity among LMS: BPMN is accepted by the business community to describe the processes’ workflows. It can also be used in an e-learning context as a specification language in order to reduce the heterogeneity among the specification techniques and bring interoperability between different systems (Object Management Group[OMG], 2006).The justification can

295

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

•

•

be found in Helic, Hrastmik, and Maurer (2005). It provides elements to specify complex and generic interactions: To achieve dynamic and adaptive LMSs, their processes have to be specified in a language that will be able to (a) describe in a standard graphical and understandable notation, (b) define abstract modeling elements by means of a metamodeling notation, and (c) offer different techniques of modeling processes for one participant or more that are connected through the flow messages (OMG, 2006). It provides support to the generation of executable specifications: BPMN has an internal model that enables the generation of BPEL executable specifications with automated support. It contributes to reduce the number of errors introduced during the translation phase, decreasing costs of the development and increasing productivity. In a general sense, it can be considered in a normative way as a bridge for the gap between business process design and process implementation (OMG, 2006; Stephen, 2004)

This article proposes to address the specification of learning technology processes. Taking a concrete scenario that is not present as a user case in the IMS DRI recommendations, we will describe the steps to obtain their executable specifications. The rest of the article is organised as follows. The second section refers to the specification of learning processes in BPMN and its mapping to BPEL, and an example presents the advantages of using it. The third section presents the integration of the BPMN processes with IMS DRI recommendations and other learning technology specifications. Finally, the fourth section presents conclusions.

296

specIFYIng leArnIng tecHnologY processes wItH bpmn In order to obtain the executable specification of processes that implement a scenario, three steps are proposed: (a) the definition of the SCP of the scenario, (b) the construction of the BPM diagram corresponding to the previous SCP, and (c) the BPEL specifications obtained from the BPM diagram. We want to note that the steps we propose are a particular case of the methodology to develop e-learning scenarios proposed by Hamburg, Busse, and Marin (2005). The scenario type of acquisition of a learning object will be taken as an example. This scenario describes the automated or semi-automated purchase of a reusable learning object to carry out a given learning objective inside the LMS (Sicilia et al., 2004).

The Semantic Conformance Profile In order to do a first analysis of the scenario in a consistent way orientated toward the automation, we generate the SCP (Sicilia et al., 2004). The description of the scenario must be clear so that it can be used to create its semantic conformance profile. This means that it is necessary to define the scenario in terms of its precondition (the initial conditions that have to be satisfied in order to execute the process), the restrictions (conditions to be check throughout the process), and the postcondition (the objective of the scenario or the condition that is true or apparent when the process is finished). The preconditions and postconditions are more or less easy to determine because they are the starting and ending points of the scenario. However, the restrictions are more difficult to establish because they imply analyzing the different states through which the scenario passes and the conditions to be taken into account in order to reach the final goal. In Table 1, the preconditions, the restrictions, and the postconditions of the LO

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

acquisition scenario are shown. As anyone can see, the scenario proposed is simplified to the utmost. Table 1 shows only one repository and only one vendor system. It would be more usual to search the LOs in a federated way, so the search to find some LO satisfying some pedagogical characteristics was done in several repositories. Also, only one system vendor is considered and it would be more realistic to have many vendor systems providing the LO selected to purchase according to the LMS preferences.

bpm diagram process The second step is the construction of the BPM diagram. To achieve it, a deeper analysis of the SCP, defined in the previous phase, is needed. The BPM diagram requires identifying the participants or actors involved in the scenario, the determination of the interactions between all the participants, the tasks to be performed, and their execution order. All this kind of information about the scenario is easily expressed using the BPM diagrams as shown in the first section. The BPM diagrams use a notation widely accepted by the business process community to specify workflows and exchange messages among

participants in business scenarios. Therefore, they allow representing collaborative processes in a notation easy to be understood by all kinds of users, from the most specialised in the analysis to the final users. Before presenting a BPM diagram as an example, it is interesting to know which are the essential elements used to represent the flow of activities and the exchange message between actors and activities. Activities are represented as rectangles with rounded corners. The control flow of the collaborative processes is indicated by arrows with a discontinuous line from one activity to another. The message exchanges are also represented as arrows but with discontinuous lines. Every participant or actor taking part in the scenario is represented in a pool diagram and inside of it there are the activities each participant carries out. The arrows within the pools represent the interactions between two participants. Figure 1 shows the BPM diagram for the LO acquisition scenario. Three pools are represented in the diagram, one for each actor identified: (a) the repository allocating the learning objects, (b) the LMS as the buyer of the reusable learning object, and (c) the vendor system as the supplier. The scenario begins with the LO purchase request

Table 1. SCP of the LO acquisition scenario PRE (Required elements)

Pedagogical characteristics of the LO required. Buying conditions. Payment information.

Restrictions (Idioms)

The repository allocating the metadata LO of interest is available. The pedagogical characteristics must be expressed in terms of the repository language. There are some LO satisfying the requirements. The purchase conditions are accepted. The copyrights are met. The legal conditions are satisfied. The vendor system identifier must be part of the LO Metadata. The vendor system is available to establish communication with the LMS. The vendor system must carry out the economic transaction. The purchase receipt is available. The transfer of the url of the LO is done correctly. The purchase process is audited.

POST (Run-time commitments)

Vendor system running Availability of the learning object from the LMS.

297

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

as the initial event. It is represented by the empty circle. The scenario ends when the post condition holds; it is when the LO has been bought and it is available to be used by the LMS for the learning needs. It is denoted with a dark circle. Between the initial and the final events, in order to achieve the goal of the scenario, the sequence of activities to carry out has been designed. The best way to do this consists of dividing the problem into smaller problems that are easier to solve. Thus, the LO acquisition scenario could be seen as composed of three subprocesses to execute sequentially: (a) the search for the LO, (b) the selection of the most suitable LO, and (c) the purchase of the LO. These subprocesses at the same time can split between a set of activities to refer to the most basic processes with a concrete functionality. Let us see the sequence of activities needed to implement the scenario in a usual case. The scenario begins when the LMS receives the initial purchase request. This request includes the pedagogical characteristics of the learning object to be bought, the buying conditions, and the payment information. In order to search the set of LO according the pedagogical requirements, the LMS has to prepare a query to ask the repository for the LO satisfying the requirements

to be able to translate the received request into a query expressed in terms of a language that can be understood by the repository. Once the query is formulated, it is sent to the repository for searching the LO metadata. As a result of the search, the repository returns a list of LO metadata satisfying all the pedagogical characteristics required. Later, this list is transferred to the LMS and the system begins checking other issues in order to select the most suitable LO metadata: the price, the satisfaction of legal aspects, and the copyright are met among others. This checking activity is complex and it can be decomposed into simpler processes. In order to represent this collapsed process, BPMN proposes to use a small square with a plus sign inside. After the selection of the most suitable LO, the communication with the vendor system must be established. When the vendor system is available to communicate with the LMS, the purchase order is sent and the economic transaction begins. If the transaction has been successful, the vendor system sends to the LMS the purchase receipt and this sends the delivery order to the repository. Then, the repository delivers the URL of the LO to the LMS, so that it has access to the learning resource. Finally, the purchase is audited by the LMS and the scenario is finished as the goal has been achieved.

Figure 1. BPMN diagram of LO acquisition scenario

Repository

Searching metadata LO

List of LO metadata

Transferring LO

url LO

Delivery order Query expression

LMS

Preparing query

Selecting suitable LO metadata +

Transferring receipt of purchase

Auditing LO purchase

Inital purchase request Purchase Order Vendor system

298

Receipt of purchase Executing purchase transaction

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

The goal of the scenario (the postcondition) is achieved when all the subprocesses composing it finish, meaning that all the activities composing a subprocess must have finished too. Notice that in Figure 1, there is only one possible sequence of activities in order to hold the postcondition; there may exist others to implement the LO acquisition scenario. From the BPM diagram, it is easy to realise that the LMS acts as the backbone of the scenario. It interacts with all the other participants and connects them using peer-to-peer interactions.

of scenarios of interest in the LMS context and their boundaries. Once we have the BPM diagram of the scenario, it is time to decide which processes are going to be needed and how they are going to be specified. Some of these processes will be implemented as BPEL processes and others as Web services. In order to do it, the BPMN specifications (OMG, 2006) help to recognise three basic submodels (the private, the abstract, and the collaboration) and propose a mapping to BPEL if possible. Private processes are those internal to the organisation, and they are usually called workflow or BPM processes. All their tasks are contained in one pool and cannot cross the boundaries of the pool. The BPMN specifications (OMG, 2006) that propose this type of process may be mapped to one or more BPEL documents. In the LO acquisition scenario there, are no private processes because the context is out of the scope of the organisation. This kind of processes of this scenario is very simple in contrast to organisational processes. Abstract processes are used to represent the interactions between a private process and another process or participant. Thus, the abstract process shows the world the sequence of messages required to interact with that business process. BPMN spec-

BPEL Specifications The third step consists of mapping from BPMN to BPEL in order to obtain executable specifications of the scenario. BPEL4WS (business process execution for Web services), or BPEL for short, is a language focused on the orchestration of SOAP Web services and based on XML, so it facilitates the exchange formats and brings about interoperability. BPEL is based on the idea of peer-to-peer processes, purpose-based interactions between BPEL orchestrator processes and individual Web services. These characteristics seem suitable for the kind

Figure 2. Abstract processes in the LO acquisition scenario Searching meatadata LO

Transferring LO

url LO Query

metadata LO

Delivery order

LMS

Purchase order

Receipt of purchase

Executting purchase transaction

299

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

ifications (OMG, 2006) suggest that every single abstract process should be mapped to a single BPEL abstract process, usually implemented by means of Web services. Figure 2 shows how the LMS interacts with the repository for searching the LO metadata and for delivering the LO. The LMS also interacts with the vendor system in order to do the purchase transaction. Therefore, it seems that three Web services could be useful in our case. Although the search and delivery of the LO are carried out by the repository, we have decided to implement only one Web service with both operations. We have also taken into account that the LO purchase is an economic transaction commonly used in the e-learning context, so it seems reasonable for it to be implemented as another Web service. The last submodel identified by the BPM specifications (OMG, 2006) is collaboration. It depicts the interactions between two or more business entities and can be seen as two or more abstract processes communicating with each other. These interactions are defined as a sequence of activities representing the message exchange patterns among the entities involved. For these cases, the

specifications should be mapped to collaboration models such as ebXML, BPSS, Rosetta-Net, and so forth. In the LO acquisition scenario, the interaction between the LMS and the vendor system could be an example. The economic transaction involves many exchange messages and usually is guided by an established protocol in order to carry out the purchase. But as the study of collaborative processes is not the purpose of this article, the LO purchase has been considered like a unique interaction and has been modeled as an abstract process using a Web service. Figure 3 presents a schema of the implementation of the LO acquisition scenario. The LMS is the orchestrator process of the scenario and, hence, it is designed as a BPEL process, which is activated by the purchase LO request as the initial event. It is adequate for implementing the LMS as a BPEL process because it consists of a sequence of invoke and receive operations and, as it has been mentioned in the previously, BPEL is suitable for programming peer-to-peer interactions between participants. Therefore, the interactions between the LMS and the repository or the LMS and the vendor system have been treated as Web services

Figure 3. LO acquisition scenario processes LMS process

Pedagogical characteristics



WS1: Query Formulator Query

Query WS2: Repository - Search LO - Transfer LO

Buying conditions

Metadata list

Delivery order



WS3: Purchase advisor Id_LO.Id_vendor Id_LO & Payment Information WS4: Purchase

Url LO



300

Receipt of purchase

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

as well as the BPMN specifications suggest. Another point to consider, in order to implement the LMS as a BPEL process, is that preparing a query and selecting the suitable LO metadata may need complex processing. The first activity must build a query to search the LO metadata, matching the pedagogical characteristics of the user; this implies some kind of translation of expression to a format understandable by the repository. The second one is supposed to select the most suitable LO metadata record from a list of possible LOs. This is a collapsed activity that has to carry out several checks about the price and other buying conditions, some legal conditions, and the copyright, among others. In both cases, the complexity of these activities and the possibility to reuse them many times has encouraged us to use other Web services. So we used the query formulator Web service and the purchase advisor Web service. Consequently, the specifications of the acquisition scenario are obtained through the specifications of four Web services and a BPEL central process. The Web services implement some external functions that can be reused in other processes. Also, they have a predefined interface in order to facilitate the use of this function by means of other actors. The four Web services proposed have two different reasons to exist. Two of them (the repository and vendor system Web services) are used to model the interactions between the LMS and other actors according BPMN specifications (OMG, 2006). The other two Web services (the purchase advisor and purchase Web services) have been used in order to isolate complex code in reusable functions. Figure 4 presents a fragment of the BPEL process implementing the LMS in the LO acquisition scenario. We can see how the initial purchase request is received by the LMS process. This event sends the required data to carry out the LO purchase. Hence, the pedagogical characteristics are taken from the initial request and a variable called client_info is prepared. This

variable contains the information needed to invoke the query formulator Web service. This Web service is invoked to obtain the query expression for searching the LO metadata in the repository. The query is formulated by the Web service and, considering the format, is accepted by the repository. Once the query expression is returned to the LMS process, the LMS search_query variable is informed to be used in the invocation of the repository Web service. This is invoked later by the search_LO_metadata operation and it returns a list of LO metadata satisfying the query. The LMS process continues as a sequence of other invoke and receive operations, which are omitted in the example.

IntegrAtIng bpmn wItH exIstIng leArnIng tecHnologY specIFIcAtIons In the e-learning area, the research has developed a number of specifications and standards in the recent years, usually related to learning objects and their reuse. Our proposal aims at the integration of these specifications and standards in the process of executable specification processes. The IMS DRI specification (IMS Global Consortium, 2003) addresses the interoperation of the most common repository functions. It recommends implementing these functions through Web services in order to define a common interface to promote their reusability. One of the two generalised implementations of different repository functions proposed by IMS DRI suggests the use of xQuery and SOAP- (simple object access protocol) based recommendations to achieve the full implementation of the core functions and functional architecture. Our proposal would include these recommendations using this kind of technology and implementing the repository Web service, which includes the search_LO_metadata operation that could be implemented as the IMS DRI federator function if it is decided that the

301

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

Figure 4. BPEL specifications as part of LO acquisition scenario <sequence> $client_info.client_info/ns0:pedagogicalCharacteristics $pedagogical_characteristics.parameters/pedagogicalCharact $resultant_query.parameters/return

scenario should be generalised with a federated search of LO metadata into several repositories. On the other hand, the translator function of the IMS DRI could also be used inside the query formulator in order to achieve the query expression in a language understandable by the repository. In this sense, the IMSDRI specifications permit us to take into account the wide range of content formats, implemented systems, technologies, and established practices existing in the area of digital repositories. Learning-object metadata (LOM; IEEE Learning Technology Standards Committee, 2002), the standard of metadata, could also be integrated easily. It could be used to annotate the learning objects. This annotation would be useful to make the search in the repositories more efficient.

302

Furthermore, once the system has acquired the learning object, it could be annotated depending on the needs of the LMS so other LMS functions or scenarios can take advantage of it. Other specifications such as LD (IMS Global Consortium, 2001) or IMS QTI (IMS Global Consortium, 2006) could be integrated in order to design the learning object. The IMS LD could be useful if the LO is a learning resource and the IMS DRI if the LO is an evaluation test. Following the specifications to define the LO would help to do it correctly and would facilitate the interoperability of LO among the LMS and other systems. Another important standard to integrate is SCORM (Advanced Distributed Learning [ADL], 2004). It is clear that the scenario consists of the contents or metadata exchange among processes,

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

so SCORM specifications would be useful to pack and unpack contents in the first instance. Also, it could be used instead of LOM in order to create a metadata application profile.

conclusIon In order to automate or semiautomate some functions that usually occur in the LMS and between it and other systems or repositories, there is a need to specify learning processes in such a way that it gives some support to their implementation. This article has presented a technique based on three steps to obtain executable specifications of learning processes. The semantic conformance profiles are proposed to be used to describe the scenario in a learning-object-oriented way geared to the automation. The BPMN has been selected as the best language to describe the scenarios and so the BPM diagram is useful to consider all participants involved in the scenario, their interactions, their workflow of activities, and messages sent among them. One of the main advantages of BPMN to be highlighted is that it promotes the interoperability, accessibility, and reusability of Web-based learning content and between systems and tools, promoting the sharing and reusing of learning objects. Furthermore, it is attractive for all kinds of users and is very easy to understand, so it reduces the confusion among all kinds of e-learning users (Stephen, 2004). Later, the recommendations of BPMN specifications guide us to transfer the BPMN diagram to a BPEL code. Finally, we considered the integration of the other specifications of the e-learning area.

Acknowledgment This article has been partially supported by project PERSONALONTO (Personalizing the Learning Process in Virtual Environments by Means of Adaptive Formative Itineraries Based

on Reusable Learning Objects and Ontologies) funded by the Spanish Government Grant No. TIN2006-15107-C02-01.

reFerences Advanced Distributed Learning (ADL). (2004). Sharable content object reference model (SCORM) overview. Author. A comparison of XML interchange formats for business process modelling. (n.d.). In The Proceedings of EMISA: Information Systems in E-Business and E-Government, Luxembourg. Eap, T., Hatala, M., & Richards, G. (2004). Digital repository interoperability: Design, implementation and deployment of the ECL protocol and connecting middleware. In Proceedings of the th 13 International World Wide Web Conference on Alternate Track Papers & Posters. New York: ACM Press. Hamburg, I., Busse, T., & Marin, M. (2005). Using e-learning scenarios for making decisions th in organizations. In Proceedings of the 6 European Conference on E-Learning, E-Business, E-Government, E-Work, E-Health, E-Democracy, E-Mediary, Virtual Institutes, On-Line, BB Services, ERA and their Influences on the Economic and Social Environment, Bucharest, Romania. Helic, D., Hrastmik, J., & Maurer, H. (2005). An analysis application of business process management technology in e-learning systems. In Proceedings of the World Conference on ELearning in Corporate, Government, Healthcare, and Higher Education (e-Learn 2005), Vancouver, Canada. IEEE Learning Technology Standards Committee. (2002). Learning object metadata (LOM) (IEEE 1484.12.1). IMS Global Consortium.(2001).IMS learning design information model, Version 1.0. Author.

303

Towards Automated Specifications of Scenarios in Enhanced Learning Technology

IMS Global Consortium. (2003). IMS digital repositories interoperability: Core functions information model IMS DRI. Author. IMS Global Consortium. (2006). IMS question and test interoperability, Version 2.1. Author. Object Management Group (OMG). (2006). BPMN v1.0: OMG final adopted specification. Author. Semantic orchestrated interactions in learning th technology. (2006). In Proceedings of the 9 International Conference on Interactive Computer Aided Learning, Villach, Austria. Sicilia, M. A., & Lytras, M. (2005). Scenariooriented reusable learning object characterizations. International Journal of Knowledge and Learning, 1(4), 332-334.

Sicilia, M.A., Pagés, C., García, E., SánchezAlonso, S., & Rius, A. (2004). Specifying semantic conformance profiles in reusable learning metath data. In Proceedings of the 5 International Conference on Information Technology Based Higher Education and Training, Istanbul, Turkey. Stephen, A. (2004). Introduction to BPM. IBM Corporation. Vogten, H., Martens, H., Nadolski, R., Tattersall, C., Van Rosmalen, P., & Koper, R. (2006). Integrating IMS learning design and IMS question and test interoperability using Copper Coreservice integration. In Proceedings of International Workshop in Learning Networks in Lifelong Competence Development, TEN Competence Conference, Sophia, Bulgaria.

This work was previously published in the International Journal of Web-Based Learning and Teaching Technologies, Vol. 3, Issue 1, edited by L. Esnault, pp. 68-77, copyright 2008 by IGI Publishing (an imprint of IGI Global).

304

305

Chapter 19

Bridging the Gap between Instructional Design and Double-Loop Learning Howard Spoelstra Open University of The Netherlands, The Netherlands Ellen Rusman Open University of The Netherlands, The Netherlands Jan van Bruggen Open University of The Netherlands, The Netherlands Rob Koper Open University of The Netherlands, The Netherlands Maristella Matera Politecnico di Milano, Italy

AbstrAct The implementation of double-loop-learning-based educational scenarios in instructional design in workflow-like e-learning systems appears to be showing a gap; whereas the former assumes that processes can be reflected upon and can be modified or amended by the learners, the latter only predefines a limited set of rigid instructional processes. However, an important advantage of instructional designs implemented in workflow-like e-learning systems using modeling standards is the ease with which they can be exchanged with other (educational) institutions. The workflow environment described here aims to make learner reflection and change to instructional processes feasible while maintaining portability. We present a description of the implementation of the educational scenario of the virtual company in our workflow environment that makes use of dynamic workflow processes. Learners are provided with process building blocks, called “atomic actions,” which they can use to create and revise processes on the fly, thus supporting double-loop learning. Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Bridging the Gap between Instructional Design and Double-Loop Learning

IntroductIon The COOPER project (Collaborative Open Environment for Project-Centered Learning) aims to deliver a learning and working environment for virtual teams whose members are geographically dispersed. The members have different backgrounds and competencies, which they use to work and learn together on projects that aim to solve complex, ill-structured problems. We want to achieve this through the use of a standards-based workflow system and an educational scenario that uses double-loop learning extensively. The COOPER environment is a Web-based working and learning environment that is created with the Webratio computer-aided software-engineering tool that uses standards like the Web markup language and business process modeling notation. The resulting environments like the COOPER environment (including its educational scenarios) can be easily exchanged with other (educational) institutions. The educational scenario we focus on is the virtual-company scenario (Bitter, Sloep, & Jansen, 2003; Westera & Sloep, 1998), which previously has only been implemented using non-workflow-based project support tools, due to lack of support for flexible processes in workflow systems. An example of an implementation of the virtual-company educational scenario is InCompany Milieuadvies: InCompany Milieuadvies is a virtual environmental consultancy in which it is attempted to fully integrate learning and working in a distance education environment. This is unlike case-based and problem-based approaches in higher education, where the “working” aspect generally is lacking. In InCompany Milieuadvies we try to generate a networked learning environment that resembles an authentic professional situation.

306

Students working in InCompany Milieuadvies address real orders on behalf of real external customers, and deliver real products. Therefore the virtual environmental consultancy is not an extended role-playing game, which usually represent self-contained business simulations. (Open Universiteit Nederland, 2006) InCompany Milieuadvies has been running multiple projects a year since 1997. It is now a required course at the end of the bachelor phase at the School of Science of the Open University of The Netherlands. The main educational aims of the virtual company are (a) personal professional development through rapid and efficient transfer of acquired knowledge and skills to professional practice, (b) the development of domain knowledge and skills, combined with the social, communicative, and organizational skills required for teamwork, and (c) an explicit and critical reflection on the working and learning processes and a transfer of valuable experiences to organizational memory. The COOPER collaboration environment described in this article is currently used by these institutions: ALaRI, a master’s programme offered by the Università della Svizzera Italiana; ASP (Alta Scuola Politecnica), a school for young talents founded by Politecnico di Milano and Politecnico di Torino; and CoWare, a leading supplier of electronic design automation software and services. Like other workflow systems, the COOPER environment is modeled by a designer while users work with the model as it is running. However, to be able to support double-loop learning(Argyris&Schön,1996),we introduce the novel concept of “atomic actions.” Atomic actions are small independent process building blocks students can use to build, modify, or rearrange work processes while the system is running.

Bridging the Gap between Instructional Design and Double-Loop Learning

InstructIonAl desIgn In e-leArnIng sYstems The support of the processes of collaborative work in instructional-design-based e-learning systems is an important challenge. Instructional process design for e-learning systems can be approached in analogy to the design process in workflow systems. These workflow systems deal with collections of tasks that are organized to accomplish some business process, tying processes, people, and resources together in dependent process steps (Georgakopoulos, Hornick, & Sheth, 1995). In the educational domain, an influential model following this approach is IMS learning design. These workflow systems seem suitable candidates for managing the modeling of collaborative work processes. In general, workflow management systems strictly separate the design and execution of a workflow and they do so for good reasons: One would rather avoid users tampering with, for example, financial transaction flows. In educational environments, however, this separation prevents learners from learning to improve processes that they are involved in. As our aim is to support highly dynamic processes, for example, the solving of ill-structured problems, we are challenged to model processes that can hardly be completely predefined and/or exhibit an explosive number of alternatives, thus escaping the ability to be fully modeled (Mangan & Sadiq, 2002). Consider, for example, how one could support the various stages and topics of solving ill-structured problems, and the different conversational and representational demands associated with each of them, where users may switch between stages and topics they address (Van Bruggen, Boshuizen, & Kirschner, 2003). A current solution to the inability to fully model a process in, for example, workflow systems or IMS learning design is to put the current process on hold, and then defer the users to a computersupported collaborative work environment in which to solve the problem, returning control to

the workflow system when done (Leo, Perez, & Dimitriadis, 2004). This approach, however, only circumvents the actual problem of not enabling users to solve ill-structured problems inside the workflow system.

ImplementIng double-loop leArnIng The notions of single- and double-loop learning originate from organizational management and learning theory (Argyris, 1999). Single-loop learning emphasizes the detection and correction of errors within a given set of governing variables, for example,the mission, vision,strategy, and systems used in a company. As is shown in Figure 1, double-loop learning, however, also involves questioning the governing variables themselves and can result in radical changes such as the revision of systems, alterations in strategy, and so on (Smith, 2007). With the implementation of double-loop learning principles in workflow systems, the picture becomes even more complicated because double-loop learning entails that the (pre)defined processes should be adaptable or amendable. Unfortunately, common practice in workflow design separates design time (analysis and implementation of the process to be modeled) from run time (the instantiation of the workflow for the actual users). Usually, process changes can only be accomplished by modifying a corresponding workflow schema in design time. However, as argued, to be able to implement double-loop learning, it is important that such changes can be conducted inside the run-time environment without causing inconsistencies and errors because of unfulfilled dependencies between process steps (Rinderle, Reichert, & Dadam, 2004). So, in order to implement double-loop learning, we provide students with an environment that supports the evaluation of working and learning processes of individuals, their team, and the orga-

307

Bridging the Gap between Instructional Design and Double-Loop Learning

Figure 1. Single- and double-loop learning Governing variables

Action Strategy

Consequences

Single-loop learning

Double-loop learning

nization in which they operate (Argyris & Schön, 1974). The results of these evaluations should be allowed to be fed back into the different levels of the organization, changing work processes and organization aspects. The virtual-company educational design implements these double-loop learning features in that students have an organizational role to fulfill in the processes in the virtual company. In that role, they solve ill-structured problems from real clients in a real, but virtualized, company in order to expand their (collective) expertise in a professional setting. In doing so, they gain competence in the form of personal learning, team learning, organizational learning, knowledge management, and the development of organizational competencies (Westera & Sloep, 1998; Westera, Sloep, & Gerrissen, 2000). The virtual-company design consists of several phases that students, teams, and the virtual company go through: 0. 1. 2.

3.

308

Design the virtual company. Project development: Projects are acquired and developed. Project start and planning: Students apply for jobs and state their learning goals in a personal development plan. The coach defines teams based on available projects and student preferences. Project execution and quality control: Based on a project work plan, students per-

4.

5.

form the project in a team, but also work on personal learning goals. Project end and delivery of results: Project results are delivered to the customer and to the virtual company (as lessons-learned reports). Consolidation of results: The personal, team, and company performance is reviewed, and, if needed, actions are defined to improve performance.

The phases 0, 1, and 5 can be removed from the virtual-company design, which leads to an environment that supports virtual projects that are not embedded in a company setting. Although this limits the possibilities for double-loop learning, it still retains valuable learning opportunities for reflection and process changes on the personal and team levels. Although the company environment is missing, the project teams still deliver lessons learned, thus enabling the educational institution to fine-tune the educational environment the projects run in. In the virtual-company design, we discern three double-loop learning cycles: the personal development cycle, the team development cycle, and the company development cycle. These development cycles are depicted in Figures 2, 3, and 4. Activities depicted in Figures 2 and 3 are the following:

Bridging the Gap between Instructional Design and Double-Loop Learning

Figure 2. Personal development cycle 3. Actions a. First phase b. Next phase

improvements

2. Personal Development Plan

4. Results (Intermediate)

6. Results (end)

evaluate job talk

feedback

specify 5. Reflection (intermediate)

7. Reflection (end)

1. Learning goals

Figure 3. Team development cycle 3. Actions a. First phase b. Next phase

improvements

2. Project/Team workplan

4. Results (Intermediate)

6. Results (end)

evaluate feedback specify 7. Lessons learned

5. Review (Intermediate)

1. Client needs

1.

2. 3.

4. 5.

Learning goals are defined, also based on possible learning opportunities in the client needs. The personal development plan is made in conjunction with the team work plan. Actions are performed based on the initial personal development plan and team project plan. Results are delivered to the virtual company and the customer. Reflection on both personal development and team development can lead to adjust-

6. 7.

ments in the personal development and team work plan, thus changing the actions to be performed. The project end result is delivered. Final personal and team reflections on the product and process are performed; lessons learned are made available for other project teams and the company to reflect on.

Activities depicted in Figure 4 are the following:

309

Bridging the Gap between Instructional Design and Double-Loop Learning

1. 2. 3. 4. 5.

An analysis of stakeholders needs is performed. A company is designed to meet those needs. Procedures and actions are defined that will guide performance and quality. Results are achieved by workers following procedures. Audits are organized to review the envisioned company, actions, and results in relation to the stakeholders’ needs and performance standards.

For Figures 2, 3, and 4, it is in the cycle of Steps 2, 3, 4, and 5 that double-loop learning takes place and students can decide to adjust, for example, the personal development plan, team project plans, or assessment criteria, thus leading to changes in their working and learning environment. For Figure 4, the results of Activity 7 on the team development cycle (describing lessons learned) are, together with other stakeholders’ needs, the basis of a company audit and the subsequent improvements in company design and its actions. Please note that within a project, the cycle is performed multiple times for the student and the team (as often as the project plan prescribes) while the cycle is only performed once per project on the company level. Figure 4. Company development cycle 3. Procedures/ actions

improvements

4. Results ({Performance)

2. Company Design

evaluate feedback specify 5. Audit

1. Stakeholders needs

310

Until now, the virtual-company design was implemented with readily available project support tools, like document sharing environments. A major drawback of this approach is that it does not allow for automating processes: There is no system support for automatically checking whether process steps are completed, or what tasks are up next for the team or the individual student, and there is no automatic logging of activities. Therefore, the student and teamwork processes and the progress had to be monitored by hand. In such a setting, the virtual company’s work processes were described in documents or static Web pages and not automated, so each instantiation of a virtual company had to be designed from the bottom up. Table 1 sums up the discrepancies between the characteristics of instructional design in workflow systems and the required characteristics to support double-loop learning. We have described the phases we distinguish in the workings of a virtual company, how these were implemented in the past, and what we envision is required for implementing double-loop learning in a virtual-company educational scenario using workflow-based systems. We will now describe our implementation using the COOPER environment.

tHe cooper enVIronment The COOPER working environment is developed with the use of Webratio, a computer-assisted software-engineering tool that allows the visual modeling of applications with the Web modeling language. Webratio enables the automatic generation of code starting from the visual schemas generated during design. Up to now, the Web modeling language and Webratio could only cope with the design of static work-flows, that is, processes that are specified at design time and are then delivered to the users by means of a Web application supporting the execution of the

Bridging the Gap between Instructional Design and Double-Loop Learning

preplanned process activities (Brambilla, Ceri, Fraternali, & Manolescu, in press). Webratio and the Web modeling language allow designers to visually specify workflows at a high level of abstraction using the business process modeling notation. It also provides a set of model transformations from business process modeling notation workflow diagrams to Web modeling language hypertext diagrams that allow the fast generation of Web site skeletons implementing the specified business process (Brambilla, 2006). As argued earlier, static workflows alone cannot support the virtual company’s educational design in full because in this design, students should be presented with a collaboration environment that is adaptable while running. In fact, once the application supporting the static workflow execution is produced and deployed, it becomes difficult (or even impossible) to modify the process. Therefore, we introduce a more flexible mechanism, called atomic actions, which allow students to define and / or adapt their dynamic cooperation processes at run time. We developed this mechanism by first analyzing the project execution phase in several project methodologies for reoccurring activities. Second, we broke down these reoccurring activities into atomic actions, from which we developed an atomic-action library.

Atomic actions can be described as follows: 1. 2.

3.

4. 5.

They are performed on a regular basis. They may involve individual or group activities and may be started by an individual or a group. They have clear starting and ending points, serving a (very) small goal in the project process. They use one or more of the services that are integrated in the COOPER platform. They can easily be composed into dynamic processes supporting the completion of cooperative tasks involving several actors.

The atomic-action library consists of atomic actions directly aimed at the process of running the project and of atomic actions that support the communication processes used to collaborate virtually. They may be seen as (very) small pieces of workflow that can be stitched together at will while retaining the changeability of the so constructed process. The atomic actions are then modeled in the environment. This enables us to allow students, when analyzing the tasks in their project, to use these atomic actions as building blocks to model their own working and communication processes, and change these if reflection on the processes so requires.

Table 1. Discrepancies between current instructional design in workflow systems and double-loop learning Instructional design in workflow systems

Double-loop learning in workflow systems

Mainly aimed at static processes

Requires support for dynamic processes

Separation of design and execution of workflows

Design, execution, and adaptation of processes, based on reflection on the effectiveness of the process, is united in the learner

Automation of predictable and repetitive processes

Requires flexible process support

Work process steps show dependencies

Work process steps show a lack of prestructure

Designs are portable to other systems

Need to redesign new implementations

Work processes are predefined at design time

Work processes can be redefined in run time

311

Bridging the Gap between Instructional Design and Double-Loop Learning

Although our work in the COOPER project also involves modeling the entire company environment through the use of the Webratio design tool, our current list of atomic actions consists of actions to be used only in Phases 3 and 4 of the project (see above). A provisional list of these atomic actions is presented in Table 2. All communication tools are modeled using the Webratio design tool except for the Voice over IP tool, which connects to a central VoIP server outside the COOPER environment. An example of how a working process is created and can be changed is shown in Figure 5. In the COOPER environment, a work process can be considered as a sequence of phases in which each phase is delimited by workflow synchronization points. These points form possible

constraints in the control of the process flow. The definition of a work process therefore proceeds in phases. For each phase, the selection of one or more atomic actions is required (corresponding to the phase’s activities). Figure 5a shows the page where a tutor at run time defines a process by selecting an atomic action (e.g., upload of a document) for inclusion in a process phase, and describes the purpose of the activity by entering a short textual description. The activity is then assigned to the team member(s) that should accomplish it. Activities can be assigned to single users or to a group of users. In the last case, the activity definition also requires the user to specify the type of parallelism governing the execution of the parallel activity. For example, a user may choose whether all team members are

Table 2. Atomic actions classified by the collaborative activity they relate to Project Activities

Atomic Actions

Define project method

Define a task Assign a task Define a project milestone Define a deliverable

Organize review

Create a review report Assign a resource to a reviewer Submit a review on a resource

Organize assessment

Define an assessment criterion Define a performance indicator Plan an assessment

Manage resources

Upload a document Publish a document Route a document

Project Support Activities

Communication

* Voice over IP (Internet protocol)

312

Open a VoIP* meeting Join a VoIP meeting Make a VoIP call Create a chat room Open a chat session Moderate a chat session Join a chat session Define a voting question Submit a vote Summarize voting results Open a videoconference Moderate a videoconference

Join a videoconference Open a co-browsing activity Moderate a co-browsing activity Join a co-browsing activity Open a forum Open a forum thread Moderate a forum thread Send a forum thread message Define a wiki Modify a wiki page

Bridging the Gap between Instructional Design and Double-Loop Learning

models that can be designed independent from specific project teams and specific actors. The template only defines the temporal sequence of activities and possible activity synchronization constraints, omitting the assignment of activities to users. Once defined, these templates can be used for starting new processes that only require the selection of the actors for each activity in the template. So, using process templates enables us to present students with a predefined set of templates of activities (useful for less experienced project members). If we choose not to use templates, we can offer users a clean slate on which to model

asked to execute the activity, or whether at least one of them should execute it. Finally, the definition of a single activity may also require the association of resources for managing document flows as it often occurs in cooperation processes. After a process has been defined, it is possible to revise its definition (as is shown in Figure 5b) by modifying or deleting its activities or the assignment of activities to users. As long as a process is not running, any activity can be modified. Once the work process is running, modifications are only allowed on activities not yet started. The COOPER environment also supports the creation of process templates, that is, process Figure 5. Definition and alteration of processes

(a)

(b)

313

Bridging the Gap between Instructional Design and Double-Loop Learning

and adapt their own processes (for students well acquainted with project work). Both options retain the flexibility to make on-the-fly changes, thus bridging the gap between instructional design and double-loop learning.

dIscussIon We set out to research the question of whether double-loop-learning educational scenarios can be implemented in workflow-like educational systems. A clear advantage of using standards based workflow systems is the possibility to exchange the educational designs with other institutions. We have shown that double-loop learning processes as occurring in the virtual company educational scenario can be modeled using the concept of atomic actions. Atomic actions are a novel approach to the implementation of flexibility and adaptability in otherwise rigid workflow systems. The use of the concept of atomic actions is not limited to the COOPER environment alone. Research into an extension of other workflow-based e-learning systems, based on, for example, IMS learning design, may also be considered. A current limitation of our solution is that, although the use of atomic actions in process design offers students flexibility, not all dependencies between process steps are resolved. Further analysis of these dependencies and ways to avoid them is required. A broader list of atomic actions and the modeling of other educational scenarios in the COOPER environment are also subject to further research.

Acknowledgment This article is sponsored by the COOPER Project that is funded by the European Commission’s Sixth Framework Programme, Priority 2 IST, Contract No. 027073.

314

reFerences Argyris, C. (1999). On organizational learning nd (2 ed.). Oxford, United Kingdom: Blackwell Publishing. Argyris, C., & Schön, D. A. (1974). Theory in practice. San Francisco: Jossey-Bass. Argyris, C., & Schön, D. A. (1996). Organizational learning II: Theory, method, and practice. Reading, MA: Addison Wesley. Bitter, M., Sloep, P., & Jansen, D. (2003). Learning to change: The virtual business learning approach to professional workplace learning. Educational Technology & Society, 6(1), 18-25. Brambilla, M. (2006, July 12-14). Generation of WebML Web application models from business process specifications. Paper presented at the Sixth International Conference on Web Engineering (ICWE2006), Palo Alto, CA. Brambilla, M., Ceri, S., Fraternali, P., & Manolescu, I. (in press). Process modeling in Web applications. Transactions on Software Engineering and Methodology. Georgakopoulos, D., Hornick, M., & Sheth, A. (1995). An overview of workflow management: From process modeling to workflow automation infrastructure. Distributed and Parallel Databases, 3(2), 119-153. Leo, D. H., Perez, J. I. A., & Dimitriadis, Y. A. (2004, August 30-September 1). IMS learning design support for the formalization of collaborative learning patterns. Paper presented at the IEEE International Conference on Advanced Learning Technologies (ICALT’04), Joensuu, Finland. Mangan, P., & Sadiq, S. (2002, January-February). On building workflow models for flexible proth cesses. Paper presented at the 13 Australasian Database Conference (ADC2002), Melbourne, Australia.

Bridging the Gap between Instructional Design and Double-Loop Learning

Open Universiteit Nederland. (2006). Retrieved from http://www.incompany-milieuadvies.nl Rinderle, S., Reichert, M., & Dadam, P. (2004). Flexible support of team processes by adaptive workflow systems. Distributed and Parallel Databases, 16, 91-116. Smith, M. K. (2007). Learning in organizations. Retrieved May 31, 2007, from http://www.infed. org/biblio/organizational-learning.htm VanBruggen, J. M., Boshuizen, H. P. A., & Kirschner, P. A. (2003). A cognitive framework for cooperative problem solving with argument visualization. In P. A. Kirschner, S. J. Buck-

ingham Shum, & C. S. Carr (Eds.), Visualizing argumentation: Software tools for collaborative and educational sense-making (pp. 25-47). London: Springer. Westera, W., & Sloep, P. (1998). The virtual company: Towards a self-directed, competence-based learning environment. Educational Technology, 38(1), 32-38. Westera, W., Sloep, P. B., & Gerrissen, J. (2000). The design of the virtual company: Synergism of learning and working in a networked environment. Innovations in Education and Training International, 37, 24-33.

This work was previously published in the International Journal of Web-Based Learning and Teaching Technologies, Vol. 3, Issue 1, edited by L. Esnault, pp. 78-89, copyright 2008 by IGI Publishing (an imprint of IGI Global).

315

316

Chapter 20

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD) Evangelos Triantafillou Center of Educational Technology, Greece Elissavet Georgiadou Center of Educational Technology, Greece Anastasios A. Economides University of Macedonia, Greece

AbstrAct The introduction of mobile devices into the learning pedagogy can compliment e-learning and e-testing by creating an additional channel of assessment with mobile devices. The current study describes the design issues that were considered for the development and the implementation of a CAT on mobile devices, the CAT-MD (Computerized Adaptive Testing on Mobile Devices). The system was implemented in two phases, where initially, a standalone prototype application was developed in order to implement the architecture of the CAT-MD. After a formative evaluation, the second phase took place, where a server-based application was developed in order to add new functionalities to the system so that CATMD can be an effective and efficient assessment tool that can add value to the educational process. The mobility of the CAT-MD eliminates the need for a specialized computer lab, as it can be used anywhere, including a traditional classroom.

computerIzed AdAptIVe testIng The recent years Computer Based Testing (CBT) is widely used in educational and training as there DOI: 10.4018/978-1-60566-938-0.ch020

are a number of perceived benefits in using computers for assessing performance such as: (a) large numbers can be marked quickly and accurately, (b) students response can be monitored, (c) assessment can be offered in an open access environment, (d) assessments can be stored and reused, (e) immedi-

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

ate feedback can be given, (f) assessment items can be randomly selected to provide a different paper to each student (Harvey & Mogey, 1999). Moreover, another benefit of CBTs would be to bring the assessment environment closer to the learning environment. Software tools and webbased sources are frequently used to support the learning process, so it seems reasonable to use similar computer-based technologies in the assessment process (Baklavas, Economides & Roumeliotis, 1999; Lilley & Barker, 2002). Most types of CBT are based on fixed-length computerized assessment that presents the same number of items to each examinee in a specified order and the score usually depends on the number of items answered correctly, giving little or no attention to the ability of each individual examinee. However, in Computerized Adaptive Testing (CAT), a special case of computer-based testing, each examinee takes a unique test that is tailored to his/her ability level. As an alternative of giving each examinee the same fixed test, CAT item selection adapts to the ability level of individual examinees and after each response the ability estimate is updated and the next item is selected to have optimal properties at the new estimate (van der Linden & Glas, 2003). The CAT presents first an item of moderate difficulty in order to initially assess each individual’s level. During the test, each answer is scored immediately and if the examinee answers correctly then the test statistically estimates her/his ability as higher and then presents an item that matches this higher ability. The opposite occurs if the item is answered incorrectly. The computer continuously re-evaluates the ability of the examinee until the accuracy of the estimate reaches a statistically acceptable level or when some limit is reached; such as a maximum number of test items. The score is determined from the level of the difficulty, and as a result, while all examinees may answer the same percentage of questions correctly the high ability ones will get a better score as they answer correctly more difficult items.

Regardless of some disadvantages reported in the literature –for example, high cost of development, item calibration, item exposure (Eggen, 2001; Boyd, 2003), the effect of a flawed item (Abdullah, 2003), or the use of CAT for summative assessment (Lilley & Barker, 2002) – CAT has several advantages. Testing on demand can be facilitated so as an examinee can take the test whenever and wherever s/he is ready. Multiple media can be used to create innovative item formats and more realistic testing environments. Other possible advantages are flexibility of test management; immediate availability of scores; increased test security; increased motivation etc. However, the main advantage of CAT over any other computerized based test is efficiency. Since fewer items are needed to achieve a statistically acceptable level of accuracy, significantly less time is needed to administer a CAT compared to a fixed length Computerized Based Testing (Rudner, 1998; Linacre, 2000). Since the mid-80s when the first CAT systems became operational, i.e. the Armed Services Vocational Aptitude Battery for the US Department of Defence account (van der Linden & Glas, 2003) using adaptive techniques to administer multiple-choice items, much research and many technical challenges have made new assessment tools possible. Currently, there are several tools for developing adaptive computerized test such as FastTEST (Assessment Systems Corporation, 2009), QuestionMark (2009), Webassesor (2009), SIETTE (Conejo, Guzmán, Millán, Trella, PérezDe-La-Cruz & Ríos, 2004), Test++ (Barra, Lannaccone, Palmieri & Scarano, 2002) etc. The availability of advanced mobile technologies have started to extend e-learning by creating an additional channel of assessment with mobile devices such as hand phones, Personal Digital Assistants (PDAs) or pocket PCs.

317

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

mobIle leArnIng In the last decade the use of different mobile products such as mobile phones and Personal Digital Assistant (PDA) devices has increased rapidly. Moreover, much attention has been paid to mobile computing within Information Technology industry. Availability of advanced mobile technologies, such as high bandwidth infrastructure, wireless technologies, and handheld devices, has started to extend e-learning towards mobile learning (Sharples, 2000). Mobile learning (m-learning) intersects mobile computing with e-learning; it combines individualized (or personal) learning with anytime and anywhere learning. The advantages of m-learning include: flexibility, low cost, small size, ease of use and timely application (Jones & Jo, 2004). The introduction of mobiles devices into the learning pedagogy can compliment e-learning by creating an additional channel of assessment with mobile devices such as PDAs, mobile phones, portable computers. Due to their convenient size and reasonable computing power, mobile devices have emerged as a potential platform for computer-based testing. Currently, there are several learning systems (Goh, Kinshuk & Lin, 2003; Mayorga, Fernández, 2003) and interactive tests (Go test go Inc, 2009) developed to be used for PDA and Java mobile phones. However, little research has been done on the implementation of CAT using mobile devices and this is the focus of our research. The current study is an attempt to examine the design and development issues, which may be important in the implementation of a CAT using mobile devises such as mobile phones and PDAs. As a case study an educational assessment prototype was developed, called CAT-MD (Computerized Adaptive Testing on Mobile Devices), to support the assessment procedure of the subject “Physics” which is typically offered to second grade students in senior high school in Greece. After formative evaluation, a server-based application has been

318

developed in order to add new functionalities to the system so that CAT-MD can be an effective and efficient assessment tool that can add value to the educational process.

sYstem ArcHItecture The prototype CAT-MD uses the Item Response Theory (IRT) as an underlying psychometric theory, which is the base for many adaptive assessment systems and depicts the relationship between examinees and items through mathematical models (Lord, 1980; Hambleton, Swamination & Rogers, 1991; Wainer, 1990). Psychometric theory is the psychological theory or technique of mental measurement, which is the base for understanding general testing theory and methods. The central element of IRT is mathematical functions that calculate the probability of a specific examinee answering a particular item correctly. IRT is used to estimate the student’s knowledge level, in order to determine the next item to be posed, and to decide when to finish the test. In IRT-based CAT as each student answers a question, his or her response is evaluated as being either correct or incorrect. The process of displaying questions, evaluating responses and selecting the next question to be administered based on the student’s latest estimated ability is repeated until a stopping rule has been reached or a certain number of questions has been administered, whichever happens first. There are four main components needed for developing IRT-based CAT: the item pool, the item selection procedure, the ability estimation and the stopping rule (Dodd, De Ayala & Koch, 1995). The following sections describe these components of the CAT-MD system.

Item pool The most important element of a CAT is the item pool, which is a collection of test items that includes a full range of levels of proficiency, and

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

from which varying sets of items are presented to the examinees. The success of any CAT program is largely dependent on the quality of the item pool that can be conceptualized according to two basic criteria: a) the total number of items in the pool must be sufficient to supply informative items throughout a testing session, and b) the items in the pool must have characteristics that provide adequate information at the proficiency levels that are of greatest interest to the test developer. This criterion mainly suggests that at all important levels of proficiency there are sufficient numbers of items whose difficulty parameters provide valuable information. Therefore, a high-quality item pool will include sufficient numbers of useful items that allow efficient, informative testing at important levels of proficiency (Wise, 1997). The item parameters included in the pool are dependent upon the Item Response Theory (IRT) model selected to model the data and to measure the examinees’ ability levels. In IRT-based CATs, the difficulty of an item describes where the item functions along the ability scale. For example, an easy item functions among the low-ability examinees and a hard item functions among the high-ability examinees; thus, difficulty is a location index. An ideal item pool needs many items, best spread evenly over the possible range of difficulty.

Item selection Two common classes of IRT models are determined by the way items’ responses are scored. Items with only two response options (correct or incorrect) are modelled with the dichotomous IRT models. Items with more than two response options can be modelled with polytomous IRT models (Boyd, 2003). Our prototype CAT-MD includes multiple choice items and true false items. Since, these are examples of items that can be scored dichotomously; CAT-MD is based on a dichotomous IRT model.

The main aspect of IRT is the Item Characteristic Curve (ICC) (Baker, 2001). ICC is an exponential function, which expresses the probability of a learner with certain skill level correctly answering a question of a certain difficulty level. ICC is a cumulative distribution function with a discrete probability. The models most commonly used as ICC functions are the family of logistics models of one (1PL), two (2PL) and three parameters (3PL). The 1-parameter logistic (1PL), or Rasch model is the simplest IRT model. The Danish mathematician Georg Rasch first published the 1-parameter logistic model in 1960s and as its name implies, it assumes that only a single item parameter is required to represent the item response process. This item parameter is termed difficulty and the equation for this model is given by: P( )

1 1

e

1(

b)

(1)

where, e is the constant 2.718, b is the difficulty parameter and θ is an ability level. In CAT-MD, as each student answers a question, his or her response is evaluated as being either correct or incorrect. In the event of a correct response, the probability P(θ) is estimated applying the formula shown in Eq. (1). Otherwise, the function Q(θ)=1-P(θ) is used. The Item Information Function (IIF) is also considered as an important value in the IRT’s item selection process. It gives information about the item to be presented to the leaner in an adaptive assessment. For selecting a question appropriate to the learner, IIF for all the questions in the assessment should be calculated and the question with highest value of IIF is presented to the learner. This provides more information about the learner’s ability and is given by the equation:

319

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

Ii ( )

Pi ( )(1

stopping rule

Pi ( ))

(2)

where Pi(θ) is the probability of a correct response to item i conditioned on ability θ (Baker, 2001; Lord, 1980).

Ability estimation After each item is administered and scored, an interim estimate of examinees’ ability (θ) is calculated and used by the item selection procedure to select the next item. The most commonly used estimation procedure is maximum likelihood estimation (MLE) (Lord, 1980). Similar to the item parameter estimation, this procedure is an iterative process. It begins with some a priori value for the ability of the examinee. In CAT-MD, it begins with θ=1. The estimation calculation approach is the modification of the Newton-Raphson iterative method for solving equations method outlined by Lord. The estimation equation used is shown below: n

n 1

n

i 1 n i 1

Si ( n ) (3)

Ii ( n )

where, Si ( )

[ui

Pi ( )]

Pi ' ( ) Pi ( )[1

Pi ( )]

(4)

One important characteristic of CAT is the test termination criterion or else the stopping rule. The termination criterion is generally based on the accuracy with which the examinees’ ability has been assessed (Boyd, 2003). There are several factors that affect the termination of a CAT test. According to Wainer (1990, p. 114), an adaptive test terminates when one or more of the following stopping rules are met: (a) when a target measurement precision level has been attained, (b) when a pre-selected number of items has been given, or (c) when a predetermined amount of time has elapsed. Moreover, Linacre (2000) adds to the above that an adaptive test also terminates when the item bank is exhausted or the test taker is exhibiting off-test behavior, such as responding too quickly or too slowly. Any of these stopping rules, or a mixture thereof, can be used to halt a CAT. Measurement precision is usually assessed based on standard error of measurement associated with a given ability. The difference between a student’s actual score and his highest or lowest hypothetical score is known as the standard error of measurement. The standard error is a measure of test consistency that is not affected by score variability. The standard error associated with a given ability is calculated by summing the values of the item information functions (IIF) at the candidate’s ability level to obtain the test information. Test information, TI(θ), is given by the equation:

TI ( )

N i 1

where θ is the skill level after n questions, and ui = 1 if the response is correct and ui = 0 for the incorrect response.

320

Ii ( ) (5)

Next, the standard error is calculated by using the equation:

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

SE ( )

1 TI ( )

(6)

In classical measurement, the standard error of measurement is a key concept and is used in describing the level of precision of true score estimates. When the test reliability is 1, the standard error of measurement is 0. When reliability is 0, then the standard error is the same as the standard deviation. With a test reliability of 0.90, the standard error of measurement for the test is about .33 of the standard deviation of examinee test scores. In item response theory-based measurement, and when ability scores are scaled to a mean of zero and a standard deviation of one (which is common), this level of reliability corresponds to a standard error of about .33 and test information of about 10. It is common in practice to design CATs so that the standard errors are about .33 or smaller (Rudner, 1998; Ron Hambleton cited in Rudner, 1998). CAT-MD is designed under the general rule; the standard errors are about .33 or smaller.

sYstem ImplementAtIon CAT-MD was implemented in two phases. Initially, a stand-alone prototype application was designed and developed in order to implement the architecture of the CAT-MD. In this initial prototype, the

item pool was embedded within the application and therefore it was difficult for any educator without programming experience to administrate the test. After formative evaluation of the initial prototype the second phase took place, where a server-based application has been developed in order to add new functionalities to the system based on the results of the evaluation.

First phase The interface of CAT-MD has been developed using Macromedia Flash as it offers competitive advantages. It is a lightweight, cross-platform runtime that can be used not just for enterprise applications, but also for communications, and mobile applications. According to Macromedia Company the 98 percent of all Internet enabled computers and 30 million mobile devices use the Flash technology (www.macromedia.com). To date, many manufacturers license Macromedia Flash on their branded consumer electronics devices, such as mobile phones, portable media players, PDAs, and other devices. These licensees include leading mobile device manufacturers such as Nokia, Samsung, Motorola, and Sony Ericsson. In its initial version CAT-MD included a database that contains 80 items related to the chapter “Electricity” from the “Physics” subject. For every item, the item pool included the item’s text, details

Figure 1. Interface of CAT-MD

The CAT-MD on HP iPAQ (PDA)

The CAT-MD on Motorola MPx220 (mobile)

321

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

on the correct answer and the difficulty level. The difficulty level varied from “very easy” to “very hard” and the values used cover the range between -2 and +2. The interface of CAT-MD and its functionalities was the result of revisions based on the analysis of the data collected during the formative evaluation (Triantafillou, Georgiadou & Economides 2008). Three types of formative evaluation were used: expert review, one-to-one evaluation and small group evaluation. Tessmer (1993) defines these evaluations types as follows: •

•

•

Expert review – experts review the system with or without the evaluator present. The experts can be content experts, designers or instructors. One-to-one evaluation – one learner at a time reviews the system with the evaluator and comments upon it. Small group evaluation – the evaluator tries out the system with a group of learners and records their performance and comments.

Recommendations from experts and suggestions that resulted from one-to-one and small group evaluation are summarized below: •

CAT-MD is an effective and efficient assessment tool. The mobility eliminates the need for a specialized computer lab and it

Figure 2. Screenshot of CAT-MD

322

• •

•

•

can be used anywhere, including a traditional classroom. The system needs to clearly indicate when the next item is ready to be administered. The system should provide immediate feedback on the correctness of examinee’s answer. Students should be able to access information at any time with regards to the total numbers of items administered. Images on PDAs should be carefully selected taking into considerations the small resolution and size of the device.

Figure 1 presents two screenshots of the implementation of CAT-MD on a mobile phone and on a PDA. Moreover, the CAT-MD is portable to any device that has installed the Macromedia Standalone-Flash Player. In addition, if a Macromedia plug-in for the web browser (Internet Explorer, Mozilla, etc.) is installed, the CAT-MD can be also accesses as flash shockwave film. Figure 2 illustrates how a test item is presented to an examinee within the prototype. Each question has four multiple choice answers and the user can select the correct one by clicking the corresponding button. The system responds immediately indicating whether the selected answer is correct or not. The user can not alter his/her selection as this is not permitted from the CAT’s architecture. Every time the test statistically estimates the user’s ability

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

based on the answer given and then presents an item that matches this new ability. At the lower right corner of the screen, there is a button that becomes active whenever the user completes the selection in each item. As a result, the user cannot omit any item as this would conflict with the item selection algorithm. Further, at the lower left corner of the screen, a number appears that corresponds to the total number of the items that the user has already answered. The user does not know when the test will terminate, however, it is considered useful to display the total number of the answered items.

second phase As mentioned above, in the initial prototype the item pool was embedded within the application and therefore it was difficult for any examiner without programming experience to administrate the test. To overcome this problem and also to add new functionalities, a server based system is implemented so that the examiner can administrate the test. The server-based system is consisted from two parts: the back-end and the front-end. The front-end of the system, that is the user interface of CAT-MD, was implemented in the first phase. However, in its current version, immediately when the test begins, the program downloads all the items that the examiner has placed in the da-

tabase. Therefore, it is essential -in the beginning at least- a connection to the Internet. The back-end part is a test editor; a flash application the examiner uses to upload items. An item consists of a single question and its’ possible answers (right or wrong, or multiple choice). The examiner must also specify other required parameters, such as the difficulty level of the item and the correct answer. Figure 3 shows the procedure of adding items, one by one, to the item pool. To set up communication between Flash and the database, LoadVars instances is used to send and receive data from PHP server side scripts that retrieve and save data to the database. Data may be sent via GET or POST, and is returned with var=value formatting. To get the data out of the database into Flash, a server side script is called that runs a SQL SELECT statement and returns the data to Flash. In www.edutech.gr/cat_md a web-based simulation of the CAT-MD application is presented: both the front-end and the back-end. In this site access to the test editor (back-end) is given for all. The item pool is consisted of dummy items in ordered to present the functionality of the system. In this simulation of CAT-MD, after each administration of an item, the examinee’s standard error associated with a given ability (θ) is calculated to determine whether a new item must

Figure 3. Screenshot of the test editor

323

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

be selected or whether the administration of the test can be terminated. The test terminates when the examinee’s standard error of measurement associated with a given ability falls below a certain specified value or when the item pool is exhausted. At the beginning of the test the test taker has to answer two items. If he succeeds or fails to answer in both items then it is likely to perform either very good or very bad, so we define the standard error in 0.5 (in this case it is possible that the test could terminate in less than 20 items). Differently, we define the standard error in 0.3. These limits can be altered depending on the precision of the requested estimation of theta (θ). In general, the smaller the standard error is defined; a bigger number of questions will be used and higher precision in the estimate of theta (θ) (Linacre, 2006).

summArY This chapter describes the design and development of the CAT-MD (Computerized Adaptive Testing on Mobile Devices), a prototype CAT on mobile devices such as PDAs. The prototype uses the Item Response Theory (IRT) as an underlying psychometric theory. Four main components are developed within the prototype: the item pool, the item selection procedure, the ability estimation and the stopping rule. CAT-MD was implemented in two phases, where initially, a stand-alone prototype application was developed in order to implement the architecture of the CAT-MD. Based on the results of the formative evaluation of the initial prototype the second phase took place, where a server-based application has been developed in order to add new functionalities to the system. The server-based system is consisted from two parts: the back-end and the front-end. The front-end of the system is the user interface of CAT-MD implemented in the first phase. The back-end part is a flash application that the examiner uses to upload items; is a test editor.

324

At present, the system is implemented in beta version where the functionality is ‘one-to-one’, meaning that it is available to one particular examiner that develops a test. After evaluating the system, our aim is to develop it further, in order to implement a ‘many-to-many’ functionality. In this extended version examiners can create their own test, add items to their pool, and determine the difficulty level and the group of the test-takers. The test-takers after logging in the system will receive the appropriate test that the examiner has already administered to them. Current research on adaptive systems, the data resulted from the evaluation of the system and our personal experience during this research demonstrates that CAT-MD is an effective and efficient assessment tool that can add value to the educational process. The mobility of the system eliminates the need for a specialized computer lab as it can be used anywhere, including a traditional classroom.

Acknowledgment The work presented in this chapter is partially funded by the General Secretariat for Research and Technology, Hellenic Republic, through the E-Learning, EL-51, FlexLearn project.

reFerences Abdullah, S. C. (2003). Student Modelling By Adaptive Testing - A Knowledge-Based Approach. Unpublished PhD Thesis, University of Kent at Canterbury. Arroyo, I., Conejo, R., Guzman, E., & Wolf, B. P. (2001). An Adaptive Web-based Component for Cognitive Ability Estimation. In Proc. of Artificial Intelligence in Education (pp. 456-466). Amsterdam.

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

Assessment Systems Corporation. (2009). Retrieved from http://www.assess.com Baker, F. (2001). The Basics of Item Response Theory. ERIC Clearinghouse on Assessment and Evaluation, University of Maryland, College Park, MD. Baklavas, G. Economides, A.A., & Roumeliotis, M. (1999). Evaluation and comparison of Webbased testing tools. In Proceedings WebNet-99, World Conference on WWW and Internet (pp. 81-86). Barra, M., Lannaccone, A., Palmieri, G., & Scarano, V. (2002). Test++: an adaptive training system on the Internet. In Proceedings of seventh International Symposium on Computers and Communications (pp. 225-230). Boyd, A. M. (2003). Strategies for Controlling Testlet Exposure Rates in Computerized Adaptive Testing Systems. Unpublished PhD Thesis, The University of Texas at Austin. Conejo, R., Guzmán, E., Millán, E., Trella, M., Pérez-De-La-Cruz, J., & Ríos, A. (2004). SIETTE: A Web–Based Tool for Adaptive Testing. International Journal of Artificial Intelligence in Education, 14, 1–33. Dodd, B. G., De Ayala, R. J., & Koch, W. R. (1995). Computerized adaptive testing with polytomous items. Applied Psychological Measurement, 19(1), 5–22. doi:10.1177/014662169501900103 Eggen, T. J. H. M. (2001). Overexposure and underexposure of items in computerized adaptive testing. Measurement and Research Department Reports 2001-1, Citogroep Arnhem. Go test go Inc. (2009). Retrieved from http:// www.gotestgo.com Goh, T. Kinshuk, & Lin, T. (2003). Developing an adaptive mobile learning system. In Proc. of the International Conference on Computers in Education, Hong Kong 2003 (pp. 1062-1065).

Hambleton, R. K., Swamination, H., & Rogers, H. J. (1991). Fundamentals of Item Response Theory. Thousand Oaks, CA: Sage Publications Inc. Harvey, J., & Mogey, N. (1999). Pragmatic issues when integrating technology into the assessment of students. In S. Brown, P. Race, & J. Bull (Eds.), Computer-assisted assessment in higher education. London: Kogan-Page. Jones, V., & Jo, H. J. (2004).Ubiquitous learning environment: An adaptive teaching system using ubiquitous technology. In Proceedings of the 21st ASCILITE Conference, Perth, Western Australia. Lilley, M., & Barker, T. (2002). The Development and Evaluation of a Computer-Adaptive Testing Application for English Language. 6th Computer Assisted Assessment Conference, Loughborough. Linacre, J. M. (2000). Computer-Adaptive Testing: A Methodology whose Time has Come. MESA Memorandum No. 69. In S. Chae, U. Kang, E. Jeon & J.M. Linacre (Eds.), Development of Computerised Middle School Achievement Test (in Korean). Seoul, South Korea: Komesa Press. Linacre, J. M. (2006). Computer-Adaptive Tests (CAT), Standard Errors and Stopping Rules. Rasch Measurement Transactions, 20(2), 1062. Retrieved from http://www.rasch.org/rmt/rmt202f.htm Lord, F. M. (1980). Applications of Item Response Theory to Practical Testing Problems. New Jersey: Lawrence Erlbaum Associates. Mayorga, M. C., & Fernández, A. (2003). Learning Tools for Java Enabled Phones: An Application to Actuarial Studies. In Proc. of the International Conference MLEARN (pp. 95-98). QuestionMark. Getting results (2009). Retrieved from http://www.questionmark.com

325

The Implementation of a Server-Based Computerized Adaptive Testing on Mobile Devices (CAT-MD)

Rudner, L. M. (1998). An online, interactive, Computer Adaptive Testing Tutorial. 11/98. Retrieved from http://EdRes.org/scripts/cat Sharples, M. (2000). The Design of Personal Mobile Technologies for Lifelong Learning. Computers & Education, 34, 177–193. doi:10.1016/ S0360-1315(99)00044-5

van der Linden, W. J., & Glas, C. A. W. (2003). Preface. In van der Linden, W.J., Glas, C.A.W (Eds), Computerised Adaptive Testing: Theory and Practice. Boston: Kluwer Academic Publishers. Wainer, H. (1990). Computerized Adaptive Testing (A Primer). New Jersey: Lawrence Erlbaum Associates.

Tessmer, M. (1993). Panning and Conducting Formative Evaluations. Kogan Page Limited.

Webassessor (2009). Retrieved from http://www. webassessor.com

Triantafillou, E., Georgiadou, E., & Economides, A. (2008). The Design and Evaluation of a Computerized Adaptive Test on Mobile Devices. Computers & Education, 50(4), 1319–1330. doi:10.1016/j.compedu.2006.12.005

Wise, S. L. (1997). Overview of practical issues in a CAT program. Paper presented at the annual meeting of the National Council on Measurement in Education, Chicago IL.

326

327

Chapter 21

Integrating Awareness Mechanisms in Web-Based Argumentative Collaboration Support Environments Manolis Tzagarakis Research Academic Computer Technology Institute, Greece Nikos Karousos Research Academic Computer Technology Institute, Greece Nikos Karacapilidis University of Patras, Greece

AbstrAct Much research has been performed on how computer-based technologies might facilitate awareness among cooperating actors. However, existing approaches in providing awareness services prove to be inadequate in data-intensive instances of argumentative collaboration. Moreover, they fail to address the needs of dynamic, web-based communities. In this context, this chapter presents a list of awareness mechanisms that have been integrated in an innovative web-based collaboration support tool, namely CoPe_it!, the ultimate aim being to satisfy the requirements associated to the above remarks. The proposed mechanisms are described and elaborated with respect to various awareness types reported in the literature.

1. IntroductIon The concept of awareness has been extensively elaborated in the field of computer-supported collaborative work (CSCW) (Schmidt, 2002; Carroll et al., 2006). In this context, awareness can be defined DOI: 10.4018/978-1-60566-938-0.ch021

as an understanding of the activities of others, which provides a context for one’s own activity (Dourish & Bellotti, 1992). Much research has been carried out on how computer-based technologies might facilitate awareness among cooperating actors (i.e. members of a community). An important body of this work attempts to develop computational environments based on event propagation mechanisms

Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Integrating Awareness Mechanisms

for collecting, disseminating, and integrating information concerning collaborative activities (Schmidt, 2002). Generally speaking, awareness of past and current actions in such shared environments and over shared artefacts influences and guides the actors’ decisions and course of actions. It allows them to have a general perception of the community’s activities, progress, and problems, as well as to have a perception about their progress compared to the others. Some awareness-related services, offered by these environments, could also aid actors to find potential collaborators for exchanging diverse types of artefacts (which can serve as an instrument to generate and sustain awareness themselves) or asking for help. However, powerful awareness mechanisms are rather rare in argumentative collaboration environments. Moreover, existing approaches in providing awareness services prove to be inadequate in data-intensive situations (Carroll et al., 2006). Collaboration settings are often associated with huge, ever-increasing amounts of multiple types of data, obtained from diverse sources that often have a low signal-to-noise ratio for addressing the problem at hand. In turn, these data may vary in terms of subjectivity, ranging from individual opinions and estimations to broadly accepted practices and indisputable measurements and scientific results. Their types can be of diverse level as far as human understanding and machine interpretation are concerned. They can be put forward by people having diverse or even conflicting interests. At the same time, the associated data are in most cases interconnected, in a vague or explicit way. Data and their interconnections often reveal social networks and social interactions of different patterns. The above bring up the need for innovative software tools that offer the appropriate awareness services in order to make it easier for actors and communities to capture, represent and process big and complex volumes of data and knowledge during argumentative collaboration. Such services should shift in focus from the collection

328

and representation of information to its meaningful assessment and utilization, the ultimate aim being to facilitate and augment collaborative sense-making. This can be seen as a special type of social computing where various computations concerning the associated context and group’s behaviour need to be supported. In line with the above, this chapter presents a list of awareness mechanisms that have been integrated in a web-based tool, namely CoPe_it! (http://copeit.cti.gr), which supports data-intensive and cognitively-complex argumentative collaboration. In the following sections, we sketch our overall approach and motivation, make a distinction of various awareness types, describe the proposed mechanisms and services in detail, and conclude with some preliminary evaluation results and future work directions.

2. AwAreness In ArgumentAtIVe collAborAtIon support enVIronments While awareness is critical in CSCW, it is a rather unexplored domain in the context of argumentative collaboration environments. Although many tools exist to support online discussion and deliberation, which range from simple discussion forums found on the Web to sophisticated and formal argumentation and decision support systems (Karacapilidis & Papadias, 2001), these do not consider awareness mechanisms as a focal point. Such mechanisms are usually considered as a complementary (i.e. not a core) functionality of solutions addressing collaboration needs. The majority of these tools provide only simple awareness mechanisms that include user presence indicators and information related to the source of individual resources (i.e. who admitted a resource and when). One reason that these tools do not employ more sophisticated awareness mechanisms is related to the way these tools support the underlying collaboration. In particular, the majority of argu-

Integrating Awareness Mechanisms

mentative collaboration approaches enable only asynchronous collaboration between participants and limit the number of allowed discourse moves during the collaboration. In discussion forums, for instance, users may only post messages that may be sorted chronologically but disallow updating or further processing (such as explicit associating or aggregating) of existing posts. Similar limitations in discourse moves can be seen even in formal argumentation systems. The provision of a limited set of discourse moves, which lack the ability to update or recast existing collaboration items, prohibits these tools to cope with problems that arise during data intensive collaboration. One solution to this problem is to extent argumentative collaboration tools with knowledge management capabilities. Among others, such capabilities permit individual users to associate items of the discussion at their will and pace, tag individual items or even aggregate a number of them into higher level abstractions; hence, they can tame the complexity of the collaboration space. Tools that expand the number of available discourse moves, such as explicitly associating items or aggregating them into larger structures, are usually designed to be used by a single person, as it is the case with almost all IBISbased tools (Conklin et al., 1987; Conklin et al., 1991; Shum et al., 1993). In any case, for tools operating in such a setting, awareness services although helpful are not considered as a critical issue. However, today’s Web environment differs substantially from the controlled environments that traditional argumentation systems require. In particular, Web users are moving away from applications that impose semantics and tightly control the formalization process. Moreover, there are usually no limits with respect to the number and type of users that participate in web-based argumentative collaboration support tools. In such contemporary settings, awareness mechanisms are more than critical. They cannot be considered as an optional service; rather, they have become a required functionality.

Argumentative collaboration environments, augmented with knowledge management services to cope with data–intensive collaboration, require a reconsideration of the status of their awareness services. This is because the more control users have over the items of a discussion in order to cope with data-intensiveness of the collaboration space, the more difficult it is for participating members to identify changes that occur due to the actions of their peers. This ultimately harms the user’s ability to follow the evolution of the collaboration and asses its current state. In such rich environments, awareness mechanisms are not simply a useful extension but a required functionality that can significantly sustain the effectiveness of the collaboration. However, introducing awareness services into data-intensive argumentative collaboration environments is not without concerns. A number of issues need to be taken into consideration that deal, among others, with which awareness types are useful in a community setting that is supported by argumentative collaboration tools, how to unobtrusively deliver awareness information, and how to deal with the asynchronous and semisynchronous nature of the collaboration space (Dourish & Bellotti, 1992). Moreover, issues such as customization and personalization of awareness services, protection of sensitive information, and provision of awareness services to third-party applications are also critical.

3. IntegrAtIng AwAreness mecHAnIsms Awareness mechanisms are essential in cooperative and collaborative environments where people interact through task sharing and assets exchange (Gillet et al., 2006). However, and as previous research has proven, not all notifications are welcomed by users and they might end up having an adverse effect. As a matter of fact, many studies have shown that excessive unnecessary

329

Integrating Awareness Mechanisms

notifications might lead to undesirable effects such as a decrease in productivity (Speier et al., 1997; Spira & Feintuch, 2005). For instance, in a learning community that includes educators, teaching assistants and a considerable number of students, if every time that a student comments on a document or an educator decides to change the rights assigned to a role in a specific activity everyone is notified, information overload is unavoidable. Consequently, community members will tend to ignore all received notifications, some of which might be relatively important and require an immediate action. A way to avoid these adverse effects and successfully sustain collaboration is to provide context-sensitive, user-centred awareness services. In order to appropriately develop such services, three basic issues have to be addressed: (i) what information should be delivered? (ii) when should it be delivered? and (iii) how should it rendered? (Greenberg & Roseman, 1996). In other words, the relevancy and importance of a notification to a specific user, its delivery and display means, and its time of delivery are the three intrinsic notification parameters that should be adapted based on the interests and notification preferences of the target user. At the same time, the notion of community in the context under consideration refers to a highly dynamic entity that evolves and changes during collaboration sessions and its lifetime; new actors can be added to a community or abandon it, some actors may be online and others not, different actor roles can be assigned (which might even change during a collaboration session), while the status of individual actors evolves in terms of their participation and expertise. In addition, actors may be members of different communities or work on multiple workspaces, where they may have different roles, be assigned to different tasks and pursue different goals. Although expressive, such a highly dynamic environment may lead users in blurring or even loosing the mental image they have of their com-

330

munity (e.g. what goals are to be achieved, what task they are supposed to do, how do others contribute to the community) and the knowledge items of a collaborative workspace. This compromises the ability of individual actors to align their actions during a collaboration session with the actions of their peers and hence threatens the group’s cohesiveness. Absence of such cohesiveness results in harming the ability of the community to solve problems efficiently. This situation is exactly the opposite of what the awareness mechanisms should try to achieve. To avoid the aforementioned problems, our attempts focus on controlling the consequences of such a highly dynamic and data-intensive environment. Hence, the proposed mechanisms provide features and functionalities with which the various events during a collaboration session can be timely captured, analyzed and made visible to end users. Another important issue during the shaping of our approach concerned the awareness types to be supported. Related work from the CSCW field reveals that various sets of awareness types have been already proposed (Chen & Gaines, 1997; Gutwin et al., 1995; Gutwin et al., 1996; Nutter & Boldyreff, 2003). These sets attempt to address different concerns in a collaboration setting. However, in the context of our approach, no single set of awareness types was able to address the abovementioned collaboration aspects and requirements. Instead, a synthesis of relevant awareness types found in the literature has been adopted. This set of awareness types permits addressing awareness issues at both the individual and the community levels, something that is critical for collaboration support services. The proposed list of awareness types to be offered in a data-intensive collaboration setting includes: •

Informal awareness: This form of awareness of a work community is the general knowledge of who is around and what he/ she is doing. It has been pointed out as an enabler of spontaneous interaction (Gross et al., 2005).

Integrating Awareness Mechanisms

•

•

•

•

•

•

Presence awareness: It involves information about the status of users. This information indicates each user’s availability, aptitude and willingness to collaborate with others. Task awareness: It involves information about the aim of a task, its requirements and how it fits within a bigger plan. Social awareness: It concerns the information that a community’s member maintains about his/her peers in a social or conversational level. It includes issues like the degree of attention and the level of interest of a member. Group-structural awareness: It involves information about the members’ roles and responsibilities, their positions on an issue and the overall community’s processes. Historical awareness: It concerns knowledge about how artefacts resulting from collaboration have evolved in the course of their development. Workspace awareness: It concerns the upto-the-minute knowledge about others’ interaction within a shared workspace (Greenberg & Roseman, 1996). This includes knowledge about the collaborative workspace in general, information about other actors’ interactions with the shared workspace and the artefacts it contains. Its difference compared to the informal awareness is that the workspace awareness is relevant only within the context of particular, shared collaboration environments. Informal awareness does not make such an assumption and considers a broader, system-wide context. Several elements are relevant to this type of awareness: presence (is anyone in the workspace?), identity (who is participating?), authorship (who is doing what?), action (what are the actors doing?), action history (how did that operation happen?), artefact history (how did this artefact reach this state?), etc. (for a complete list see (Gross et al., 2005)).

It is important to notice that the adopted awareness types are not considered as independent; rather, they overlap in a collaborative environment (Greenberg & Roseman, 1996). Hence, some awareness functionalities may be related to more than one awareness types. For instance, awareness related to the creation of new communities and statistical reports about their life cycle (in a particular workspace or not) falls into this case.

4. cAse studY: AwAreness mecHAnIsms In cope_It! In this section, we present the mechanisms that CoPe_it! offers in order to deliver awareness information to a community’s members. For each mechanism, we discuss issues related to what information is available, how it is conveyed to users, how preferences with respect to desired awareness information can be expressed and what cues are used to facilitate transparent delivery of this information. Before that, we briefly introduce some key concepts of CoPe_it! in order to sketch the overall computer-supported environment in which the proposed awareness services function and better explain their purpose and role.

4.1 A short description of cope_it! CoPe_it! is a web based tool supporting argumentative collaboration within communities (Karacapilidis & Tzagarakis, 2007a; Karacapilidis & Tzagarakis, 2007b). The tool’s workspaces provide the ground where collaborative activities take place. Communities may have one or more workspaces where their members can upload diverse types of knowledge items such as notes, ideas and comments as well as other artefacts such as files, images and videos. In addition to operations related to sharing knowledge items, CoPe_it! provides also knowledge management means. Within workspaces, users can associate all knowledge types in arbitrary ways that suit their

331

Integrating Awareness Mechanisms

understanding of the domain. Moreover, they can freely change the type of the knowledge items (e.g. changing a note to an idea and vice versa) at any point during the collaboration. Furthermore, they can aggregate or specialize existing items hence creating new items. Workspaces can be either public (shared by a group of people) or private. Public workspaces are used to support collaborative work, while private ones can be also seen as personal mind maps or as note keeping environments. To support the evolution of collaboration in communities, CoPe_it! builds upon an incremental formalisation approach, through which the emergent transformation of loosely coupled, informal and unstructured workspaces to highly structured, formal workspaces is achieved. The latter may enable decision making support, through the exploitation of dedicated mechanisms. Alternative formalisations of a particular workspace are possible; these are supported by appropriate visualisations schemas. The collaboration environment of CoPe_it! brings in a new way of interacting with knowledge items in shared workspaces. This is attained by introducing a new set of operations that go beyond traditional operations found in contemporary collaboration environments, such as web-based forums. These operations include amongst others changing the spatial position of knowledge items on the workspace, relating knowledge items using explicit relationships, aggregating knowledge items, changing the visual appearance of knowledge items or relationships, and changing the view of a workspace. Being a natural extension of the collaboration space, they do not make the tool more complicated; rather, they turn it to be a much more dynamic environment compared to traditional solutions. Within CoPe_it!, the notion of a community exists explicitly. Users may belong to one or more communities. Distinct roles within a community can be assigned; these determine and control the access of users to resources. In terms of user

332

privileges, a hierarchical organisation scheme is assumed. Every community has one or more administrators (moderators).

4.2 Awareness mechanisms in cope_it! 4.2.1 User Bar An integral part of the user’s main interface is the information that indicates the presence or not of a community’s members within CoPe_it!. The bottom-left pane of Figure 1 (entitled Quick Contacts) shows which users that belong to the same community are currently online. Special visual cues (called presence indicators) convey this status. A green bullet indicates a user that is currently online, while a gray bullet indicates a user not currently online. By hovering over a user in the presence pane, a window displays information on the user profile. This is the information that the users have decided to share. It gives the currently logged on user the ability to get in touch with his/her peers by either email or chat. Hence, spontaneous and ad-hoc interactions can be possible. A special visual cue indicates users that have a particular role in the communities of which the current user is a member. For example, special icons will indicate users that are administrators/ moderators of a community or a particular workspace. It is noted that the list of users visible on the quick contacts pane are those that belong to the same community as the current user and not those who are working on the same workspace as the current user. This case is described in the context of another awareness mechanism.

4.2.2 Mini Map Every workspace in CoPe_it! is equipped with the ability to get an overview of the workspace in which a user is currently collaborating (Figure 2). Through the mini map space, the user is able

Integrating Awareness Mechanisms

Figure 1. Information on the status of individual users that belong to a community

to see areas of activity of that workspace that indicate which issues are being discussed by other community members. Due to the synchronous nature of the CoPe_it! workspaces, the mini map may also enable users to see where the most collaborative activity is happening. While the main display permits a user to focus on his/her own tasks, the mini map lets the same user glimpse on which issues other members of the workspace are currently engaged. In different situations, this may also provide valuable insights that facilitate the coordination of collaborative actions within a workspace. Furthermore, the option of highlighting a particular point on the workspace (in order to indicate to all online participants an area of interest) is also provided (Figure 2).

4.2.3 Online User Bar and Head-Up Display The online (synchronous) participation of a group of users in a workspace is depicted through a user bar that appears at the top part of this workspace (Figure 3). In such a way, each participant may easily see all the other online members of the workspace and understand the current status of an ongoing collaboration. Furthermore, participants may directly communicate by sending personal or broadcasted messages. As depicted in Figure 3, a participant may be informed about who has the “turn” and who else is requesting to take the “turn” of the workspace (“turn” refers to the ability of a user to take an action in a workspace; according to our approach, in a synchronous collaboration, only one user has the “turn” each time). If more than one requests exist, the priority is shown

Figure 2. Mini Map provides an overview of the entire workspace (bottom-right part); A certain point on the workspace is highlighted, indicating to all users an area of interest (middle-left part)

333

Integrating Awareness Mechanisms

Figure 3. Displaying on-line participants (top bar of the workspace)

Figure 4. An instance of the workspace head up display (WHUD)

Figure 5. Viewing past messages

through an indicative number. Moreover, each participant’s image is accompanied by a border that indicates his/her role (e.g. an orange border indicates the moderator of a workspace). Every workspace also provides a transparent area (at the bottom left part of the display) that includes information about current events (Figure 4). This area is called the workspace head up display

334

(WHUD). This is the main mechanism through which events of the synchronous collaboration in a workspace will be perceivable by the user. In particular the WHUD will allow a user to: (i) get information about the users that are currently working in that workspace; (ii) get notification on the actions other participants carry out in the workspace and on events related to individual

Integrating Awareness Mechanisms

Figure 6. Replay mechanism window

artefacts, e.g. artefact creation, deletion, update etc; (iii) send personal or broadcast messages, and (iv) get informed about previous messages exchanged among participants (Figure 5).

4.2.4 Workspace Replay and Resource History At the workspace level, users are able to query what events happened in the past. Various filtering options are available so that they can see changes within a workspace. Changes may concern users, resources or operations (e.g. what relationships have been added, what deletions and insertions occurred, etc.). The above functionality is supported in CoPe_it! by the workspace replay mechanism (Figure 6). By triggering this mechanism, a CoPe_it! user is able to view all actions that have been taken place during a particular time period (sequentially). There exist a set of functionalities for better managing the replay procedure (“pause”, “move back”, “move next” and “stop playing” options).

In addition to chronological information, other visual cues have implemented to indicate the access intensity of individual resources (collaboration items). Figure 7 illustrates how ‘access intensity’ is rendered visible to users. A usage bar is shown next to each resource that can take colours from white to red. A darker colour indicates more frequent access by a larger number of users (than a lighter one). This aims at making visible which resources of a workspace are accessed the most (and which are not). As a result, this functionality may give useful insights on which resources have not been taken into consideration.

4.2.5 Workspace Teleporting CoPe_it! enables users to peek at other workspaces that may or may not be in their community and see what activities happen there without being noticed by the users that are currently working on that workspace. According to similar mechanisms that are available in CSCW (Gutwin et al., 1996), this is referred to as ‘workspace teleporting’. This

Figure 7. Information about the usage of various collaboration items in a workspace (the red bar on the left indicates the access intensity; from left to right the figure shows items in increasing usage intensity).

335

Integrating Awareness Mechanisms

functionality is achieved by assigning a special role to individual users and in particular the role of a ‘lurker’. Not all users have this role by default and assignment of this role to community members that in general do not have access to a workspace is controlled by the communities and workspaces administrator. Users that are able to lurk at other workspaces are made aware of this role by displaying special icons in their environment (e.g. binoculars next to the list of workspaces).

4.2.6 Statistics For each community, various types of statistical information are also provided. This gives insights on the participation level of the members in the community. These statistics include: •

•

•

Number of logins by community members, where the number of members that logged on into CoPe_it! and how this number evolved over a time period are presented. Number of resources generated by the community during all collaboration sessions. Overview statistics on the activities within a community, that may include the most active workspace, the most active members etc. (Figure 8).

Figure 8. Statistics: overview report

336

Apart from the above, another set of statistical reports is provided to administrators and/or community moderators. This concerns information about the creation of new communities, the total number of communities, users, workspaces and workspace items, the average usage time of the system, the total visits of the system, the used storage capacity, etc.

4.3 supported Awareness types Summarising the above, Table 1 gives an overview of the awareness cues available in CoPe_it! and indicates to which awareness type they contribute. The awareness capabilities of CoPe_it! attempt to address the problems that originate from the highly dynamic environment that communities require and CoPe_it! provides. The available awareness mechanisms address a mixture of asynchronous and synchronous concerns. This is in contrast to the approach that existing collaboration systems have with respect to awareness, as they target either synchronous or asynchronous issues of the collaboration. The awareness mechanisms of CoPe_it! cover a critical range of aspects ensuring effective collaboration, as they enable spontaneous and ad-hoc interaction between community members, easy comprehension of the events taking place within workspaces, as well as a meaningful assessment of a community’s activities and their evolution over time.

Integrating Awareness Mechanisms

4.4 Adapting Awareness According to user needs

4.5 providing Awareness services to third party Applications.

Although CoPe_it! introduces a number of awareness services to address various aspects of the collaboration, their use may impose information overload to users. To avoid this situation, users have the ability to tailor and personalize them. More specifically, CoPe_it! enables users to activate or deactivate the available awareness services according to their preferences. In such a way, they may adjust the variety and volume of the related indicators received. These preferences can be directly set by users in the context of a selected workspace, a specific community that the user belongs to, or the entire CoPe_it! environment and are maintained in the user’s profile.

As a web application, CoPe_it! aims not only to support native CoPe_it! users in collaborative activities, but also to provide the implemented functionality to third party applications (achieving interoperability and using widely accepted web protocols). In this context, CoPe_it! can provide awareness services to other tools. This provision is currently achieved in two different ways: •

The RSS (Really Simple Syndication) feed support. Through this protocol, information concerning the most recent events that took place in CoPe_it! is given. This

Table 1. Overview of awareness cues in CoPe_it! and association of them to particular awareness types Awareness Cues

Display

Awareness Types

Indication of the users‘ status

• When the user is online a green bullet appears in the user bar next to his /her name • The names and the status of online users working in a workspace appear in the workspace user bar.

Workspace, social, informal awareness

Indication of current activity in a workspace

• The mini map shows changes of the workspace due to the interaction of other working users. • Synchronously carried out operations are shown on the head up display.

Workspace, social, informal awareness

Indication of past activity in a workspace

• Icon of clock marks recently changed items since the last user’s login. • Users can replay the collaboration that has taken place in a workspace. • Users can examine the history of the individual artefacts.

Workspace awareness

Indication of an item frequency of use

• Usage bar of the item appears in darker colour.

Historical, workspace awareness

Indication of the rights over an item

• Through appropriate icons, users are informed about the actions they may perform on an item.

Group-structural awareness

Indication of teleporting to other workspaces

• Icon depicting binoculars next to all workspaces permitting teleporting is shown. • Binoculars are shown next to the current user’s name in his/her profile.

Social, informal awareness

Indication of the roles assigned to community members.

• Administrators/moderators that belong to the same community as the current user appear in the user bar with an icon depicting a red gear (similar cues are used in the head up display). • Administrators/moderators of workspaces where the user has access appear in the user bar with an icon depicting a green gear (similar cues are used in the head up display). • In the workspace user bar, community and workspace administrators are appropriately displayed.

Group-structural awareness

Indication of the activity within and outside a community

• Statistics in the form of bars and charts are available to users. • Overall statistics are available to administrators /moderators

Group-structural, historical, social, system awareness

337

Integrating Awareness Mechanisms

•

may include information about the users recently registered, the last created public workspaces, etc. The CoPe_it! event Web Service. Through this, it is possible to retrieve information on events that happen in CoPe_it! by adopting the Web Service standards for communication. Such events may concern both the users and the resources that are involved in the tool’s workspaces. For instance, through this service, one may get information about who has been logged in within a certain time interval, what are the recently added resources to a certain public workspace, what communities and public workspaces have been created, etc.

5. conclusIon The overall approach followed in the development of CoPe_it! is the result of action research studies (Checkland & Holwell, 1998) concerning the improvement of practices, strategies and knowledge in diverse data-intensive collaborative environments. Moreover, the research method adopted follows the design science paradigm (Hevner et al., 2004). CoPe_it! has been already introduced in multiple collaborative settings for a series of pilot applications. Preliminary results referring to the awareness services provided show that they fully cover the user requirements associated to the data-intensive nature of a collaboration setting, as well as to the dynamic nature of web-based communities. In particular, users have admitted that these services stimulate interaction, makes them more accountable for their contributions, while they aid them to conceive, document and analyze the overall collaboration context in a holistic manner. Existing, broadly used webbased argumentative collaboration environments, such as discussion forums, lack such awareness mechanisms, hence imposing serious limitations in data-intensive collaboration. Future work directions include the assessment

338

of the effectiveness of existing awareness mechanisms, extension of the available notification support, and reconsideration of CoPe_it!’s architecture with respect to new awareness services. In particular, related to assessing the awareness mechanisms, extensive evaluation of CoPe_it! in diverse collaboration situations is currently being conducted, which is expected to shape our mind towards the development of additional awareness services required, as well as the experimentation with and integration of the associated visualization cues. Regarding notification support, future work will focus on including e-mail and SMS messages as an additional notification mean, emphasizing on how these can be deployed in a non-disruptive way. Finally, aiming at providing effortlessly new awareness services in the future, the conceptual architecture of CoPe_it! is reconsidered in order to treat awareness services as generic and expandable components.

Acknowledgment Research carried out in the context of this chapter has been partially funded by the EU PALETTE (Pedagogically Sustained Adaptive Learning through the Exploitation of Tacit and Explicit Knowledge) Integrated Project (IST FP6-2004, Contract No. 028038).

reFerences Carroll, J. M., Rosson, M. B., Convertino, G., & Ganoe, C. H. (2006). Awareness and teamwork in computer-supported collaborations. Interacting with Computers, 18(1), 21–46. doi:10.1016/j. intcom.2005.05.005 Checkland, P., & Holwell, S. (1998). Action Research: Its Nature and Validity. Systemic Practice and Action Research, 11(1), 9–21. doi:10.1023/A:1022908820784

Integrating Awareness Mechanisms

Chen, L., & Gaines, B. (1997). A Cyber-Organism Model for Awareness in Collaborative Communities on the Internet. International Journal of Intelligent Systems, 12(1), 31–56. doi:10.1002/ (SICI)1098-111X(199701)12:13.0.CO;2-Z Conklin, J., & Begeman, M. L. (1987). gIBIS: A Hypertext tool for Team Design Deliberation. In Proc. Hypertext’87 Conference, Chapel Hill, North Carolina, November (pp. 247-151). Conklin, J., & Yakemovic, K. C. B. (1991). A process-oriented approach to design rationale. Human-Computer Interaction, 6(3-4), 357–391. doi:10.1207/s15327051hci0603&4_6 Dourish, P., & Bellotti, V. (1992). Awareness and coordination in shared workspaces. In Proc. CSCW ’92, Toronto, Ontario, Canada (pp. 107-114). Gillet, D., Salzmann, C., & Rekik, Y. (2006). Awareness: An Enabling Feature for Mediated Interaction in Communities of Practice. In Proc. TEL-CoPs’06 - 1st Int. Workshop on Building Technology Enhanced Learning solutions for Communities of Practice, Crete, Greece. Greenberg, S., & Roseman, M. (1996). Workspace Awareness for Groupware. In Proc. CHI’96 Conference (Companion), Vancouver, Canada (pp. 208-209). Gross, T., Stary, C., & Totter, A. (2005). UserCentered Awareness in Computer-Supported Cooperative Work-Systems: Structured Embedding of Findings from Social Sciences. International Journal of Human-Computer Interaction, 18(3), 323–360. doi:10.1207/s15327590ijhc1803_5 Gutwin, C., Greenberg, S., & Roseman, M. (1996). Workspace Awareness in Real-Time Distributed Groupware: Framework, Widgets, and Evaluation. In Proc. Conf. on Human-Computer Interaction - HCI’96, London, UK (pp. 281-298).

Gutwin, C., Stark, G., & Greenberg, S. (1995). Support for workspace awareness in educational groupware. In Proc. 1st Int. Conf. on Computer Support for Collaborative Learning, Bloomington, IN (pp. 147-156). Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75–105. Karacapilidis, N., & Papadias, D. (2001). Computer Supported Argumentation and Collaborative Decision Making: The HERMES system. Information Systems, 26(4), 259–277. doi:10.1016/ S0306-4379(01)00020-5 Karacapilidis, N., & Tzagarakis, M. (2007a). Supporting Incremental Formalization in Collaborative Learning Environments. In Proc. 2nd European Conference on Technology Enhanced Learning (EC-TEL 2007), Crete, Greece (LNCS 4753, pp. 127–142). Karacapilidis, N., & Tzagarakis, M. (2007b). Web-based collaboration and decision making support: A multi-disciplinary approach. International Journal of Web-Based Learning and Teaching Technologies, 2(4), 12–23. Nutter, D., & Boldyreff, C. (2003). Historical Awareness Support and its Evaluation in Collaborative Software Engineering. In Proc. 12th Int. Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. Schmidt, K. (2002). The Problem with ‘Awareness’: Introductory Remarks on ‘Awareness in CSCW’. Computer Supported Cooperative Work, 11(3), 285–298. doi:10.1023/A:1021272909573 Shum, S., & MacLean, A. Forder, J. & Hammond, N. (1993). Summarising the Evolution of Design Concepts within a Design Rationale Framework. In Adjunct Proceedings of InterCHI’93: ACM/ IFIP Conference on Human Factors in Computing Systems, April 24-29, Amsterdam (pp. 43-44).

339

Integrating Awareness Mechanisms

Speier, C., Valacich, J. S., & Vessey, I. (1997). The effects of task interruption and information presentation on individual decision-making. In Proc. 18th Int. Conf. on Information Systems, Atlanta, GA (pp. 21-36).

340

Spira, J. B., & Feintuch, J. B. (2005). The Cost of Not Paying Attention: How Interruptions Impact Knowledge Worker Productivity. New York: Basex.

341

Compilation of References

Abdullah, S. C. (2003). Student Modelling By Adaptive Testing - A Knowledge-Based Approach. Unpublished PhD Thesis, University of Kent at Canterbury.

Alavi, M. (1994). Computer-mediated collaborative learning: An empirical evaluation. MIS Quarterly, 18(2), 159–174. doi:10.2307/249763

Ackermann, E. K. (2004). Constructing Knowledge and Transforming the World. In M. Tokoro & L.Steels (Eds.), A learning zone of one’s own: Sharing representations and flow in collaborative learning environments (Vol. 1, pp. 15–37). IOS Press.

Alavi, M., & Leidner, D. E. (2001). Research commentary: Technology-mediated learning- A call for greater depth and breadth of research. Information Systems Research, 12(1), 1–10. doi:10.1287/isre.12.1.1.9720

Adkins, J. (2007). Metacognition: Designing For Transfer. Retrieved Feb. 25, 2008, from http://www.usask.ca/education/coursework/802papers/Adkins/ADKINS.PDF.

Alavi, M., Wheeler, B. C., & Valacich, J. S. (1995). Using IT to reengineer business education: An exploratory investigation of collaborative telelearning. MIS Quarterly, 19(3), 293–312. doi:10.2307/249597

Adrián, T. S. (2002). Estratégias para incrementar la elaboración de inferencias al leer textos de orden expositivo. Investigacion y Posgrado, 17, 225–240.

Alexander, B. (2006). Web 2.0: A New Wave of Innovation for Teaching and Learning? EDUCAUSE Review, 41(2), 32–44.

Advanced Distributed Learning (ADL). (2004). Sharable content object reference model (SCORM) overview. Author.

Allen, I. E., & Seaman, J. (2004). Entering the mainstream: The quality and extent of online education in the United States, 2003 and 2004. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http://www.sloan-c.org

Aggarwal, A. K., & Legon, R. (2006). Case studyWeb-based education diffusion. International Journal of Web-Based Learning and Teaching Technologies, 1(1), 49–72. Aggarwal, A. K., & Legon, R. (2006). Web-Based Education Diffusion. International Journal of Web-Based Learning and Teaching Technologies, 1(1), 49–72. Ahmed, R. (2000). Effectiveness of Web-based virtual learning environments in business education: Focusing on basic skills training for information technology. Unpublished Doctoral Dissertation, Louisiana State University.

Allen, I. E., & Seaman, J. (2005). Growing by degrees: Online education in the United States, 2005. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http://www.sloan-c.org/publications/survey/ pdf/ growing_by_ degrees.pdf Allen, I. E., & Seaman, J. (2006). Making the grade: Online education in the United States, 2006. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http://www.sloan-c.org/publications/survey/ pdf/ Making_the_ Grade.pdf

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Compilation of References

Allen, I. E., & Seaman, J. (2007). Online nation: Five years of growth in online learning. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http://www.sloan-c.org/publications/survey/index.asp Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States, 2008. Sloan Center for Online Education, MA. Retrieved December 30, 2008, from http://www.sloan-c.org/publications/survey/ pdf/ staying_the_course.pdf Allen, K. (2005). Online learning: Constructivism and conversation as an approach to learning. Innovations in Education and Teaching International, 42(3), 247–256. doi:10.1080/01587910500167985

Arbaugh, J. B. (2005). Is there an optimal design for online MBA courses? Academy of Management Learning & Education, 4(2), 135–149. nd

Argyris, C. (1999). On organizational learning (2 ed.). Oxford, United Kingdom: Blackwell Publishing. Argyris, C., & Schön, D. A. (1974). Theory in practice. San Francisco: Jossey-Bass. Argyris, C., & Schön, D. A. (1996). Organizational learning II: Theory, method, and practice. Reading, MA: Addison Wesley. Aronson, E., Blaney, N., Stephan, C., Sikes, J., & Snapp, M. (1978). The jigsaw classroom. Sage Publications.

Allen, S., Evans, S., & Ure, D. (2005). Virtual Communities of Practices: vehicles for organisational learning and improved job performance. International Journal of Learning Technology, 1(3), 252–272. doi:10.1504/ IJLT.2005.006517

Aroyo, L., & Dicheva, D. (2001). AIMS: Learning and teaching support for www-based education. International Journal of Continuing Engineering Education and Lifelong Learning, 11(1-2), 152–164. doi:10.1504/ IJCEELL.2001.000390

Amabile, T. (1993, Fall). Motivating creativity in organizations. California Management Review, 40(1), 39–58.

Aroyo, L., & Dicheva, D. (2004). The new challenges for e-learning: The educational semantic web. Educational Technology & Society, 7(4), 59–69.

Anderson, E. E. (1985). Managerial considerations in participative design of MIS/DSS. Information & Management, 9(4), 201–208. doi:10.1016/0378-7206(85)900576 Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. Anderson, T., & Elloumi, F. (2004). Theory and practice of online learning. Canada: Athanasca University. Retrieved from http://cde.athabascau.ca/ Anderson, T., Rourke, L., Garrison, D. R., & Archer, W. (2001). Assessing social presence in asynchronous text-based computer conferencing. Journal of Distance Education, 14(2). Andrews, N., & Tyson, L. D. (2004). The upwardly Global MBA. Fall 2004. Retrieved from http://www. strategy-business.com

342

Arroyo, I., Conejo, R., Guzman, E., & Wolf, B. P. (2001). An Adaptive Web-based Component for Cognitive Ability Estimation. In Proc. of Artificial Intelligence in Education (pp. 456-466). Amsterdam. Assessment Systems Corporation. (2009). Retrieved from http://www.assess.com Attwell, G. (2006). Personal Learning Environment. Retrieved on May 17, 2007 from http://www.knownet.com/ writing/weblogs/Graham_Attwell/entries/6521819364 Attwell, G. (2007a). The Personal Learning Environments - the future of eLearning? eLearning Papers, 2(1). Attwell, G. (2007b). Web 2.0 and the changing ways we are using computers for learning: what are the implications for pedagogy and curriculum? Retrieved March 3, 2008, from http://www.elearningeuropa.info/directory/ index.php?page=doc&doc_id=9756&doclng=6. Augar, N., Raitman, R., & Zhou, W. (2004). Teaching and learning online with wikis. In R. Atkinson, C.

Compilation of References

McBeath, D. Jonas-Dwyer & R. Phillips (Eds.), Beyond the Comfort Zone, Proceedings of the 21st ASCILITE Conference, Perth, Western Australia, December 5-8 2004. ASCILITE. Retrieved on 17 May, 2007 from http://www.ascilite.org.au/conferences/perth04/procs/ contents.html Ayres, P. (2006). The impact of reducing intrinsic cognitive load on learning in a mathematical domain. Applied Cognitive Psychology, 20(3), 287-298. Babcock, M. (2007). Learning logs in introductory literature courses. Teaching in Higher Education, 12(4), 513–523. doi:10.1080/13562510701415615 Baets, W., & Van der Linden, G. (2003). Virtual Corporate Universities: a Matrix of Knowledge and Learning for the New Digital Dawn. Kluwer Academic Publisher. Baker, F. (2001). The Basics of Item Response Theory. ERIC Clearinghouse on Assessment and Evaluation, University of Maryland, College Park, MD. Baker, J. D. (2004). An investigation of relationships among instructor immediacy and affective and cognitive learning in the online classroom. Internet and Higher Education 7, 113. Bormuth, R. J. (196-8). Cloze test readability: Criterion reference scores. Journal of Educational Measurement, 5, 189–196. Baklavas, G. Economides, A.A., & Roumeliotis, M. (1999). Evaluation and comparison of Web-based testing tools. In Proceedings WebNet-99, World Conference on WWW and Internet (pp. 81-86). Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling, personal computer adoption and use as an illustration. Technology Studies, 2(2), 285–309. Barker, B. O., & Hall, R. F. (1998). Planning for technology implementation in rural schools. Rural Educator, 19(3), 1–6. Barker, P. (1994). Designing interactive learning. In T. de Jong & L. Sarti (Eds.), Design and production of multimedia and simulation-based learning material. Dordrecht, the Netherlands: Kluwer Academic Publishers.

Barki, H., Titah, R., & Boffo, C. (2007). Information system use-related activity: An expanded behavioral conceptualization of individual-level information system use. Information Systems Research, 18(2), 173–192. doi:10.1287/isre.1070.0122 Barkley, A. P. (2000). Unanticipated benefits from the use of computer technology in a large introductory course. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 147-149). Bolton, MA: Anker Publishing Company Inc. Baron, R. M., & Kenny, D. A. (1986). The moderatormediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. doi:10.1037/0022-3514.51.6.1173 Barra, M., Lannaccone, A., Palmieri, G., & Scarano, V. (2002). Test++: an adaptive training system on the Internet. In Proceedings of seventh International Symposium on Computers and Communications (pp. 225-230). Barron, A. E., & Orwig, G. W. (1997). New technologies for education: A beginner’s guide. Englewood Cliffs, CO: Libraries Unlimited. Barrows, H. S., & Tamblyn, R. M. (1980). Problem-Based Learning: An Approach to Medical Education. New York: Springer Publishing Co. Bates, A. W. (1995). Technology, open learning and distance education. London/New York: Routledge. Baumgartner, P. (2004). The Zen Art of aching. Communication and Interactions in eEducation. Retrieved on 17 May, 2007 from http://bt-mac2.fernuni-hagen.de/ peter/gems/zenartofteaching.pdf Baumgartner, P. (2005). Eine neue Lernkultur entwickeln: Komptenzbasierte Ausbildung mit Blogs und E-Portfolios. In V. Hornung-Prähauser (Ed.), ePortfolio Forum Austria, Proceedings of the ePortfolio Austria 2005 – Salzburg, 4 April 2004 (pp. 33-38). Baumgartner, P., Häfele, H., & Maier-Häfele, K. (2004). Content Management Systeme in e-Education. Auswahl, Potenziale und Einsatzmöglichkeiten. Innsbruck: Studienverlag.

343

Compilation of References

Beckett, D. (2006). Semantics Through the Tag. XTech 2006 “Building Web 2.0”. Bendel, O. (2006). Wikipedia als Methode und Gegenstand der Lehre. In K. Hildebrand (Ed.), HMD - Praxis der Wirtschaftsinformatik: Vol. H. 252. Social software (pp. 82-88). Heidelberg: dpunkt-Verlag. Bennett, S., Maton, K., & Kervin, L. (2008). The ‘digital natives’ debate: A critical review of the evidence. British Journal of Educational Technology. Retrieved June 1, 2008, from http://www.blackwell-synergy.com/ toc/bjet/0/0. Berge, Z. (1998). Differences in teamwork between postsecondary classrooms and the workplace. Education and Training, 40(5), 194-201. Berge, Z. L. (1995). Facilitating computer conferencing: Recommendations from the field. Educational Technology, 35(1), 22-30. Berge, Z. L. (2002). Obstacles to distance training and education in corporate organizations. Journal of Workplace Learning, 4(5), 182–189. Berge, Z. L., & Lenora, G. (2008). Implementing and sustaining e-learning in the workplace. International Journal of Web-Based Learning and Teaching Technologies, 3(3), 44–53. Berkovsky, S., Kuflik, T., & Ricci, F. (2008). Crossrepresentation mediation of user models. User Modeling and User-Adapted Interaction, On-line First. Berners-Lee, T. (1998). Why RDF model is different from the XML model. Retrieved January 9, 2009, from http:// www.w3c.org/DesignIssues/RDF-XML Bhagyavati, S. K., & Whitehead, C. C. (2005). Using asynchronous discussions to enhance student participation in CS courses. Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education SIGCSE ’05, 37(1). Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–826.

344

Bilodeau, H. (1995). Enseignement à distance à l aide de tableaux électroniques. Report prepared for the Université du Québec en Abitibi-Témiscamingue. Biolghini, D. (2001). Comunità in rete e Net learning. Milan: RCS libri. Bitter, M., Sloep, P., & Jansen, D. (2003). Learning to change: The virtual business learning approach to professional workplace learning. Educational Technology & Society, 6(1), 18-25. Blackmore, C., Tantam, D., & van Deurzen, E. (2008). Evaluation of e-learning outcomes: experience from an online psychotherapy education programme. Open Learning, 23(3), 185–201. doi:10.1080/02680510802420027 Blyth, C. S. (2000). Enhancing the study of foreign language with technology: A CD-ROM and companion Web site for first-year French. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 258-260). MA: Anker Publishing Company Inc. Bonk, C. J., Kirkley, J., Hara, N., & Dennen, V. P. (2001). Finding the instructor in post-secondary online learning: Pedagogical, social, managerial and technological locations. In J. Stephenson (Ed.), Teaching and Learning Online: Pedagogies for New Technologies (pp. 76-97), London: Kogan Page. Bönnighausen, M., & Wilkesmann, U. (2005). E-Learning meets Wissensmanagement: Wie Qualifikations- und Kompetenzentwicklung in Betrieben zugleich erfolgen. Retrieved from http://www.diezeitschrift.de. Borghi, L., De Ambrosis, A., & Mascheretti, P. (2002). Formazione in rete degli insegnanti di fisica: un esempio. Tecnologie Didattiche, 26, 58–61. Borstorff, P. C., & Lowe, S. K. (2007). Student perceptions and opinions toward e-learning in the college environment. Academy of Educational Leadership Journal, 11(2), 13–30. Böttger, M., & Röll, M. (2004). Weblog publishing as support for exploratory learning on the world wide web. In P. Isaias, K. Demetrios & G. Sampson (Eds.), Cognition and Exploratory Learning in Digital Age (CELDA 2004),

Compilation of References

Proceedings of the IADIS International Conference – Lisbon, 15-17 December 2004, IADIS Press Bouras, C., & Tsiatsos, T. (2002). Extending the Limits of CVEs to Support Collaborative e-Learning Scenarios. 2nd IEEE International Conference on Advanced Learning Technologies, Kazan, Russia, 9 - 12 September 2002 (pp. 420-424). Bouras, C., & Tsiatsos, T. (2006). Educational Virtual Environments: Design Rationale and Architecture. Multimedia Tools and Applications, 29(2), 153-173.Bouras, C., Tegos, C., Triglianos, V. & Tsiatsos, T. (2007). X3D multi-user virtual environment platform for collaborative spatial design. Paper presented at the 9th International Workshop on Multimedia Network Systems and Applications (MNSA-2007), Toronto, Canada, 25 - 29 June 2007. Bouras, C., Giannaka, E., & Tsiatsos, T. (2005). Designing Virtual Spaces to Support Learning Communities and e – Collaboration. 5th IEEE International Conference on Advanced Learning Technologies, Koahsiung, Taiwan, 5 - 8 July 2005 (pp. 328-332). Bower, M. (2007). Groupwork activities in synchronous th online classroom spaces. In Proceedings of the 38 SIGCSE Technical Symposium on Computer Science Education, Covington, KY (pp. 91-95). Boyd, A. M. (2003). Strategies for Controlling Testlet Exposure Rates in Computerized Adaptive Testing Systems. Unpublished PhD Thesis, The University of Texas at Austin. Brahm, T. (2007). Social Software und Personal Broadcasting - Stand der Forschung. In S. Seufert & D. Euler (Eds.) Ne(x)t Generation Learning: Wikis, Blogs, Mediacasts & Co. - Social Software und Personal Broadcasting auf der Spur, SCIL Arbeitsbericht 12, Universität St. Gallen. (pp. 20-38). Brambilla, M. (2006, July 12-14). Generation of WebML Web application models from business process specifications. Paper presented at the Sixth International Conference on Web Engineering (ICWE2006), Palo Alto, CA.

Brambilla, M., Ceri, S., Fraternali, P., & Manolescu, I. (in press). Process modeling in Web applications. Transactions on Software Engineering and Methodology. Brandenburg, D. C., & Binder, C. V. (1999). Emerging trends in human performance interventions. In H.D. Stolovitch & E.J. Keeps (Eds.), Handbook of Human Performance Technology: Improving Individual and Organizational Performance Worldwide (pp. 843-866). San Francisco: Jossey-Bass. Bransford, J., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How People Learn. Washington, DC: National Academy Press. Bregman, P., & Jacobson, H. (2000). Searching for answers: Yes, you can measure the business results of training. Training, 38(8), 68–72. Breuleux, A., Henchey, N., Laferrière, T., Moll, M., Saint-Onge, H., Van Der Vlist, A., & Wall, E. T. (1996). Apprendre au 21ème siècle. Retrieved December 30, 2008, from http://www.fse.ulaval.ca/fac/tact/fr/html/ vision2.html Bricco, E., & Rossi, M. (2004). La simulation globale à l’épreuve de la formation à distance: Un fil d’Ariane nécessaire. Information, Savoirs, Décisions & Médiations: Information Sciences for Decision Making, 18. Retrieved January 14, 2008, from http://isdm.univ-tln. fr/PDF/isdm18/53-rossi-bricco.pdf Brindle, M., & Levesque, L. (2000). Bridging the gap: Challenges and prescriptions for interactive distance education. Journal of Management Education, 24(4), 445–457. doi:10.1177/105256290002400404 Brown, A. R., & Voltz, B. D. (2005). Elements of effective e-learning design. The International Review of Research in Open and Distance Learning, 5(1). Retrieved June 24, 2008, from http://www.irrodl.org/index.php/irrodl/ article/viewArticle/217/300 Brown, E. (2006). Discussion board or discussion bored? Paper presented at the Seventh UCLT Conference, Anglia Ruskin University. Brown, J. S., & Duguid, P. (2000). The Social Life of Information. Harvard Business School Press.

345

Compilation of References

Brown, J. S., & Duguid, P. (2001). Structure and spontaneity: Knowledge and organization. In I. Nonaka & D. J. Teece (Eds.), Managing industrial knowledge: Creation, transfer and utilization (pp. 44-67). London: Sage. Brown, R. E. (2001). The process of community-building in distance learning classes. Journal of Asynchronous Learning Networks, 5(2).

Calongne, C. (2007). A View from Second Life’s Trenches: Are You a Pioneer or a Settler? In Proceedings of the NMC Summer Conference, 2007 (pp. 111-119). Campbell, D. M., Melenyzer, B. J., Nettles, D. H., & Wyman, R. M., Jr. (2000). Portfolio and performance assessment in teacher education. Allyn & Bacon.

Bruner, J. (1966, 1970). The Process of Education. Harvard University Press.

Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally.

Bruner, J. (1985). Models of the learner. Educational Researcher, 14(6), 5–8.

Campos, D. S. (1982). Psicologia da aprendizagem. Editora Vozes Ltda, Petrópolis, RJ, Brazil

Brusilovsky, P. (2001). Adaptive Hypermedia. User Modeling and User-Adapted Interaction, 11, 87–110. doi:10.1023/A:1011143116306

Campos, F. C. A., Rocha, A. R. C., & Campos, G. H. B. (1998). Design instrucional e construtivismo: Em busca de modelos para o desenvolvimento de software. Retrieved June 24, 2008, from http://www.niee.ufrgs.br/ ribie98/TRABALHOS/250M.pdf

Brusilovsky, P., & Vassileva, J. (2003). Course sequencing techniques for large-scale web-based education. International Journal of Continuing Engineering Education and Lifelong Learning, 13(1-2), 75–94. Brusilovsky, P., Eklund, J., & Schwarz, E. (1998, April 14-18). Web-based education for all: A tool for developing adaptive courseware. In Proceedings of Seventh International World Wide Web Conference, Computer Networks and ISDN Systems, 30(1-7), 291-300. Brusilovsky, P., Schwartz, E., & Weber, G. (1996, June 12-14). ELM-ART: An intelligent tutoring system on the world wide web. In Proceedings of Third International Conference on Intelligent Tutoring Systems (ITS), Montreal, Canada. Burkett, W. H., Compton, D. M., & Burkett, G. G. (2001). An examination of computer attitudes, anxieties, and aversions among diverse college populations: issues central to understanding information sciences in the new millennium. Informing Science, 4(3), 77–85. Busato, V. V., Prins, F. J., Elshout, J. J., & Hamaker, C. (1998). The relation between learning styles, the big five personality traits and achievement motivation in higher education. Personality and Individual Differences, 26, 129–140. doi:10.1016/S0191-8869(98)00112-3

346

Canales, A., Peña, A., Peredo, R., Sossa, H., & Gutiérrez, A. (2007). Adaptive and intelligent web based education system: Towards an integral architecture and framework. Expert Systems with Applications, 33, 1080–1083. doi:10.1016/j.eswa.2006.08.034 Capin, T., Pandzic, I., Magnenat-Thalmann, N., & Thalmann, D. (1999). Avatars in Networked Virtual Environments. John Wiley & Sons Ltd. Carduso, J., & Sheth, A. P. (2006). Semantic Web Services, Processes and Applications. New York: Springer Science+Business Media, LLC. Carey, T., Mitchell, S., Peerenboom, D., & Lytwyn, M. (1998). Effectiveness of learning technologies: The costs and effectiveness of technology-based approaches to teaching and learning. University of Guelph. Carnevale, D. (2002, February 25). Online students don’t fare as well as classroom counterparts. The Chronicle of Higher Education, Distance Education Daily News. Retrieved December 30, 2008, from http://chronicle.com/ free/2002/02/2002022501u.htm Carr, J. (1999). The future is already here. A National Strategy for Australian Education and Training to Maximize Opportunities Offered by High Capacity Commu-

Compilation of References

nication Services. Retrieved December 30, 2008, from http://www.educationau. edu.au/archives/broadbnd/ REPORT.HTM Carroll, J. B. (1968). On learning from being told. Educational Psychologist, 5, 4–10. Carroll, J. M., Rosson, M. B., Convertino, G., & Ganoe, C. H. (2006). Awareness and teamwork in computersupported collaborations. Interacting with Computers, 18(1), 21–46. doi:10.1016/j.intcom.2005.05.005 Carson, J. G., & Longhini, A. (2002). Focusing on learning styles and strategies: A diary study in an immersion setting. Language Learning, 52, 401–438. doi:10.1111/0023-8333.00188 Carswell, A. D., & Venkatesh, V. (2002). Learner outcomes in an asynchronous distance education environment. International Journal of Human-Computer Studies, 56, 475–494. doi:10.1006/ijhc.2002.1004 Carte, T. A., & Russell, C. J. (2003). In pursuit of moderation: Nine common errors and their solutions. MIS Quarterly, 27(3), 479–501. Cartelli, A. (2005). Towards an Information System Making Transparent Teaching Processes and Applying Informing Science to Education. Journal of Issues in Informing Science and Information Technology, 2, 369–381. Cartelli, A. (2006). Un esempio di progettazione di attività formativa mirata. Quaderno n. 2 del Centro di Facoltà per le T.I.C. e la didattica on line. Cassino (Italy): Idea Stampa. Cartelli, A. (2008). Web technologies and the integration of different e-learning strategies in education. International Journal of Web-Based Learning and Teaching Technologies, 3(2), 23–34. Cartelli, A. (2008a). The Implementation of Practices with ICT as a New Teaching-Learning Paradigm. In A. Cartelli & M. Palma (Eds.), Encyclopedia of Information Communication Technology (pp. 413-418). Hershey, PA: Information Science Reference.

Cartelli, A. (2008b). Towards a new model for knowledge construction and evolution. In A. Cartelli & M. Palma (Eds.), Encyclopedia of Information Communication Technology (pp. 767-774). Hershey, PA: Information Science Reference. Cassell, J., Bickmore, T., Campbell, L., Vilhjálmsson, H., & Yan, H. (2000). Human conversation as a system framework: Designing embodied conversational agents. In J. Cassell, J. W. Sullivan, S. Prevost, & E. F. Churchill (Eds.), Embodied conversational agents (pp. 29-63). Cambridge, MA: MIT Press. Cassidy, S., & Eachus, P. (2002). Developing the computer selfefficacy (CUSE) scale: investigating the relationship between computer self efficacy, gender and experience with computers. Journal of Educational Computing Research, 26, 169–189. doi:10.2190/JGJR-0KVL-HRF7GCNV Cattell, R. B., Eber, H. W., & Tatsuoka, M. M. (1970). Handbook for the sixteen personality factor questionnaire (16 PF). Institute for Personality and Ability Testing. Centra, J. A. (1979). Determining faculty effectiveness. San Francisco, CA: Jossey-Bass. Chamot, A. U. (2005). Language learning strategy instruction: Current issues and research. Annual Review of Applied Linguistics, 25, 112–130. doi:10.1017/ S0267190505000061 Chan, Y.-Y., Leung C.-H., & Li, J. K. (2003). Evaluation on security and privacy of Web-based learning systems. rd In Proceedings of the 3 IEEE International Conference on Advanced Learning Technologies: ICALT’03, Athens, Greece (pp. 308-309). Chang, L., Wang, G., & Wu, Y. (1999). An approach for attribute reduction and rule generation based on Rough set theory. Journal of Software, 10(11), 1207–1211. Chao, J. (2007). Student Project Collaboration Using Wikis. In Proceedings of the 20th Conference on Software Engineering Education & Training (pp. 255-261). Washington, DC.

347

Compilation of References

Checkland, P., & Holwell, S. (1998). Action Research: Its Nature and Validity. Systemic Practice and Action Research, 11(1), 9–21. doi:10.1023/A:1022908820784

Cognition and Technology Group at Vanderbilt. (1991). Some thoughts about constructivism and instructional design. Educational Technology, 31(10), 16-18.

Chen, L., & Gaines, B. (1997). A Cyber-Organism Model for Awareness in Collaborative Communities on the Internet. International Journal of Intelligent Systems, 12(1), 31– 56. doi:10.1002/(SICI)1098-111X(199701)12:13.0.CO;2-Z

Cohen, A., & Dörnyei, Z. (2002). Focus on the language learner: motivation, styles, and strategies. In N. Schmitt (Ed.), An introduction to applied linguistics (pp. 170-190). London: Arnold.

Chi, E. H., & Mytkowicz, T. (2008). Understanding the efficiency of social tagging systems using information theory. In HT ‘08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia (pp. 81-88). New York: ACM. Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in higher education. AAHE Bulletin, 39(7), 3–7. Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G.A. Marcoulides (Ed.), Modern methods for business research (pp. 295336). Mahwah, NJ: Laurence Erlbaum Associates. Chin, W. W., Johnson, N., & Schwarz, A. (2008). A fast form approach to measuring technology acceptance and other constructs. MIS Quarterly, 32(4), 687–704. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/ adoption study. Information Systems Research, 14(2), 189–217. doi:10.1287/isre.14.2.189.16018 Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459. Clark, S. & Maher, M.L. (2006). Collaborative Learning in a 3D Virtual Place: Investigating the Role of Place in a Virtual Learning Environment. doi: 10.2316/Journal.208.2006.4.208-0896. Coates, T. (2005). An addendum to a definition of Social Software. Retrieved on March 4, 2007 from http://www. plasticbag.org/archives/2005/01/an_addendum_to_a_ definition_of_social_software

348

Cohen, D., & Prusak, L. (2001). In Good Company: How Social Capital Makes Organizations Work. Boston, MA: Harvard Business School Press. Cohen. A. (1998). Strategies in learning and using a second language. London: Longman. Cole, M., & Engestrom, Y. (1993). A cultural-historical approach to distributed cognition. In G. Salomon (Ed.), Distributed Cognitions: Psychological and Educational Considerations. New York: Cambridge University Press. Cole, P. (1992). Constructivism revisited: A search for common ground. Educational Technology, 32(2), 27–34. Commission of the European Communities. The (2005). Recommendation of the European Parliament and of the Commission on key competences for lifelong learning. Retrieved January 9, 2009 from http://ec.europa.eu/ education/policies/2010/doc/keyrec_en.pdf Conejo, R., Guzmán, E., Millán, E., Trella, M., PérezDe-La-Cruz, J., & Ríos, A. (2004). SIETTE: A Web– Based Tool for Adaptive Testing. International Journal of Artificial Intelligence in Education, 14, 1–33. Conklin, J., & Begeman, M. L. (1987). gIBIS: A Hypertext tool for Team Design Deliberation. In Proc. Hypertext’87 Conference, Chapel Hill, North Carolina, November (pp. 247-151). Conklin, J., & Yakemovic, K. C. B. (1991). A process-oriented approach to design rationale. HumanComputer Interaction, 6(3-4), 357–391. doi:10.1207/ s15327051hci0603&4_6 Conole, G., Dyke, M., Oliver, M., & Seale, J. (2004). Mapping pedagogy and tools for effective learning design. Computers & Education, 43(1–2), 17–33.

Compilation of References

Corby, O., Dieng-Kuntz, R., & Faron-Zucker, C. (2004, August 22-27). Querying the semantic web with the Corese search engine. In Proceedings of ECAI (pp. 705709). Valencia, Spain: IOS Press. Corby, O., Dieng-Kuntz, R., Faron-Zucker, C., & Gandon, F. (2006). Searching the semantic web: Approximate query processing based on ontologies. IEEE Intelligent Systems Journal, 21(1), 20–27. doi:10.1109/ MIS.2006.16 Costa, I. T., Fagundes, L. C., & Nevado, R. A. (1998). Projeto TEC-LEC Modelo de uma Nova Metodologia em EAD Incorporando os Recursos da Telemática. Proceedings of the Informática na Educação: Teoria e Prática, 1(1), 83–100. Porto Alegre. Counterman, C., Golden, G., Gollub, R., Norton, M., Severance, C., & Speelmon, L. (2004). Sakai Architecture. Courte, J. E. (2007). Comparing student acceptance and performance of online activities to classroom activities. In Proceedings of the 8th ACM SIGITE Conference on Information Technology Education (pp. 185-190). Destin, Florida. Cowan, M. M., & Piepgrass, K. W. (1997). The first day of class on a two-year campus: What students really want us to know about their science classroom experiences. Journal of College Science Teaching, 27(2), 104–106. Craig, E. (2007). Changing paradigms: managed learning environments and Web 2.0. Campus-Wide Information Systems, 24(3), 152–161. doi:10.1108/10650740710762185 Criswell, E. (2000). The humanistic tradition: A vision for the future. Journal of Humanistic Psychology, 40(3), 74–82. Cross, J. (2004). An informal history of eLearning. On the Horizon, 12(3), 103–110. Cullen, J., Hadjivassiliou, E., Hamilton, E., Kelleher, J., Sommerlad, E., & Stern, E. (2002). Review of current pedagogic research and practice in the fields of postcompulsory education and lifelong learning. Tavistock Institute. Retrieved July 2006 from http://www.tlrp.org/ pub/acadpub.html

Damiani, E., Corallo, A., Elia, G., & Ceravolo, P. (2002). Standard per i learning objects: Interoperabilità ed integrazione nella didattica a distanza. Convegno internazionale: eLearning: una sfida per l’Universita’ Strategie Metodi Prospettive. Daniel, B., Schwier, R. A., & McCalla, G. (2003). Social capital in virtual learning communities and distributed communities of practice. Canadian Journal of Learning and Technology, 29(3), 113–139. Dartmouth College PKI Lab. (2001). PKI applications in academic computing. Retrieved from http:// www. cs.dartmouth.edu/~pkilab/acapps.shtml Dashti, A., & Safar, M. (2007). Streaming of Continuous Media for Distance Education Systems. International Journal of Distance Education Technologies, 5(3), 42–66. Davenport, T. H., & Prusak, L. (1998, 2000). Working Knowledge. Harvard Business School Press. Davie, L. E. (1989). Facilitation techniques for the online tutor. In R. Mason & A. R. Kaye (Eds.), Mindweave: Communication, computers and distance education. Oxford, United Kingdom: Pergamon Press. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi:10.2307/249008 Davis, F. D., & Venkatesh, V. (2004). Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Transactions on Engineering Management, 51, 31–46. doi:10.1109/TEM.2003.822468 Dawnes, S. (2005). E-learning 2.0. eLearn Magazine. Association for Computing Machinery, Inc. Retrieved November 9, 2005, from http://elearnmag.org/subpage. cfm?section=articles&article=29-1 De Bra, P., Aerts, A., Berden, B., de Lange, B., Rousseau, B., Santic, T., et al. (2003, August 26-30). AHA! The adaptive hypermedia architecture. In Proceedings of fourteenth ACM Conference on Hypertext and Hypermedia (pp. 81-84). Nottingham, United Kingdom: ACM Press.

349

Compilation of References

De Bra, P., Brusilovsky, P., & Houben, G.-J. (1999). Adaptive hypermedia: From systems to framework. ACM Computing Surveys, 31(4). doi:10.1145/345966.345996 Deakins, D., & Freel, M. (1998). Entrepreneurial learning and the growth process in SMEs. The Learning Organization, 144–155. doi:10.1108/09696479810223428 Dede, C. (1998). The scaling-up process for technologybased educational innovations. In C. Dede (Ed.), Learning with Technology: 1998 ASCD Yearbook (pp. 199-215). Alexandria, VA: Association for Supervision and Curriculum Development. Dehors, S., Faron-Zucker, C., & Dieng-Kuntz, R. (2006, July 5-7). Reusing learning resources based on semantic web technologies. In Proceedings of 6th IEEE International Conference on Advanced Learning Technologies, ICALT, Kerkrade, Netherlands.

and the emergence of the virtual university (pp. 1-14). Pergamon Press. Dodd, B. G., De Ayala, R. J., & Koch, W. R. (1995). Computerized adaptive testing with polytomous items. Applied Psychological Measurement, 19(1), 5–22. doi:10.1177/014662169501900103 Doll, W. J., & Torkzadeh, G. (1989). A discrepancy model of end-user computing involvement. Management Science, 35(10), 1151–1171. doi:10.1287/mnsc.35.10.1151 Doll, W. J., & Torkzadeh, G. (1991). The measurement of end-user computing satisfaction: Theoretical and methodological issues. MIS Quarterly, 15(1), 5–10. doi:10.2307/249429

Delfino, M., & Persico, D. (in press). Online or face-toface? Experimenting with different techniques in teacher training. Journal of Computer Assisted Learning.

Dolog, P., & Nejdl, W. (2003) Challenges and benefits of the semantic web for user modelling. In Proceedings. of AH. Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems at World Wide Web, UserModelling and Hypertext Conference, (Budapest, Pittsburgh, Nottinngham).

Devaraj, S., Easley, R. F., & Crant, J. M. (2008). How does personality matter? Relating the five-factor model to technology acceptance and use. Information Systems Research, 19(1), 93–107. doi:10.1287/isre.1070.0153

Dolog, P., Henze, N., Nejdl, W., & Sintek, M. (2004, May 17-22). Personalization in distributed e-learning environments. In Proceedings of Thirteenth International World Wide Web Conference (pp. 170-179). New York, USA.

Dick, W., & Carey, L. (1996). The systematic design of instruction. New York: HarperCollins Publisher.

Dooley, K. E., & Wickersham, L. E. (2007). Distraction, Domination and Disconnection in whole-class online Discussions. Quarterly Review of Distance Education, 8(1), 1–8.

Dickey, M. D. (2005). Three-dimensional virtual worlds and distance learning: Two case studies of active worlds as a medium for distance education. British Journal of Educational Technology, 36(3), 439-451. Dillenbourg, P. (1999). What do you mean by collaborative learning? In P. Dillenbourg (Ed.), Collaborative-learning: Cognitive and Computational Approaches (pp. 1-19). Oxford: Elsevier. Dillenbourg, P. (Ed.). (1999). Collaborative learning: Cognitive and computational approaches. Pergamon. Dillinger, M. (2001). Learning environments: The virtual university and beyond. In F. Tschang & T. D. Senta (Eds.), Access to knowledge: New information technologies

350

Dourish, P. (2003). Where the Action Is: The Foundations of embodied interaction. MIT Press. Dourish, P., & Bellotti, V. (1992). Awareness and coordination in shared workspaces. In Proc. CSCW ’92, Toronto, Ontario, Canada (pp. 107-114). Dourish, P., & Harrison, S. (1996). Re-Placing Space: The Roles of Place and Space in Collaborative Systems. In Proceedings of the ACM CSCW’96 Conference on Computer Supported Cooperative Work (pp. 68-85). Downes, S. (2005). e-Learning 2.0. eLearn Magazine. Retrieved from http://elearnmag.org/subpage.cfm?secti on=articles\&article=29-1

Compilation of References

Driscoll, M. P. (2000). Psychology of Learning for Instruction. Boston, MA: Allyn and Bacon. Druskat, V. U., & Kayes, C. (2000). Learning versus performance in short-term project teams. Small Group Research, 31(3), 328-353. Du, S. H. & Wagner, C. (2005). Learning with Weblogs: An Empirical Investigation. HICSS ’05: Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS’05) - Track 1 (pp. 7b-7b). Washington, DC, USA: IEEE Computer Society. Duffy, P. D., & Bruns, A. (2006). The Use of Blogs, Wikis and RSS in Education: A Conversation of Possibilities. [Brisbane.]. Proceedings Online Learning and Teaching Conference, 2006, 31–38. Duffy, T., & Jonassen, D. H. (1992). Constructivism: New Implications for Instructional Technology. In T. Duffy & D. H. Jonassen (Eds.), Constructivism and the Technology of Instruction: A Conversation. Hillsdale, NJ: Erlbaum. Dumas, C., Saugis, G., Degrande, S., Plénacoste, P., Chaillou, C., & Viaud, M. (1999). Spin: A 3D interface for cooperative work. Virtual Reality (Waltham Cross), 4(1), 15–25. doi:. doi:10.1007/BF01434991 Duvel, C., & Pate, S. (2003). Computer knowleadge: report from a student self evaluation. Journal of Information Technology, 20(1), 2–16. Eachus, P., & Cassidy, S. (2006). Development of the Web Users Self-efficacy Scale (WUSE). Issues in Informing Science and Information Technology, 3(1), 199–209. Eap, T., Hatala, M., & Richards, G. (2004). Digital repository interoperability: Design, implementation and deployment of the ECL protocol and connecting th middleware. In Proceedings of the 13 International World Wide Web Conference on Alternate Track Papers & Posters. New York: ACM Press. Efimova, L., & Fiedler, S. (2004). Learning webs: Learning in weblog networks. In P. Kommers, P. Isaias & M. B. Nunes (Eds.) Web Based Communities. Proceedings of the IADIS International Conference 2004, Lisbon 24-

26 March 2004, IADIS Press. (pp. 490-494). Retrieved on May 17, 2007 from https://doc.telin.nl/dscgi/ds.py/ Get/File-35344 Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman and Hall. Eggen, T. J. H. M. (2001). Overexposure and underexposure of items in computerized adaptive testing. Measurement and Research Department Reports 2001-1, Citogroep Arnhem. Ehrman, M. E., Leaver, B. L., & Oxford, R. L. (2003). A brief overview of individual differences in second language learning. System, 31, 313–330. doi:10.1016/ S0346-251X(03)00045-9 Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. Entwistle, N., & Waterston, S. (1998). Approaches to studying and levels of processing in university students. The British Journal of Educational Psychology, 58. Erl, T. (2007). SOA Principles of Service Design (1st ed.). Boston, MA: Prentice Hall. Ertmer, P. A., & Newby, T. J. (1993). Behaviorism, cognitivism, constructivism: Comparing critical features from a design perspective. Performance Improvement Quarterly, 6(4), 50–72. Eurostat. (2006). News release 20/6/2006: The e-society in 2005. Retrieved from http://ec.europa.eu/eurostat/ Eurydice, The information network on education (2002a). Initial training and transition to working life, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education, vol. 1. Retrieved January 9, 2009 from http://eacea.ec.europa. eu/ressources/eurydice/pdf/0_integral/037EN.pdf Eurydice, The information network on education (2002b). Supply and demand, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education, vol. 2. Retrieved January 9, 2009 from http://eacea.ec.europa.eu/ressources/eurydice/ pdf/0_integral/034EN.pdf

351

Compilation of References

Eurydice, The information network on education (2003). Working conditions and pay, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education, vol. 3. A Retrieved January 9, 2009 from http://eacea.ec.europa.eu/ressources/eurydice/ pdf/0_integral/040EN.pdf Eurydice, The information network on education (2004). Keeping teaching attractive for the 21st century / Volume 4, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education, vol. 4. Retrieved January 9, 2009 from http://eacea.ec.europa. eu/ressources/eurydice/pdf/0_integral/043EN.pdf Eurydice, The information network on education (2005). Reforms of the teaching profession: a historical survey (1975-2002) / Supplementary report, The teaching profession in Europe: Profile, trends and concerns. General lower secondary education.Retrieved January 9, 2009 from http://eacea.ec.europa.eu/ressources/eurydice/ pdf/0_integral/067EN.pdf Eurydice. (2004). Key data on information and communication technology in schools in Europe 2004 edition. Retrieved from http://www.eurydice.org Evans, C. & Sadler-Smith, E. (2006). Learning styles in education and training: problems, politicisation and potential. Education + Training, 48(2/3), 77-83 Eynon, R. (2008). The use of the World Wide Web in learning and teaching in higher education: Reality and rhetoric. Innovations in Education and Teaching International, 45(1), 15–23. doi:10.1080/14703290701757401 Eysenck, H. J., & Eysenck, S. B. G. (1975). Manual of the eysenck personality questionnaire. London: Hodder & Stoughton. Eysenck, S. B. G., Eysenck, H. J., & Barrett, P. (1985). A revised version of the psychoticism scale. Personality and Individual Differences, 6, 21–29. doi:10.1016/01918869(85)90026-1 Fallah, M. H., & Ubell, R. (2000). Blind scores in a graduate test: Conventional compared with Web-based outcomes. ALN Magazine, 4(2), 1–4.

352

Famous models Cattell 16PF profile. (n.d.). Retrieved Feb. 26, 2008, from http://www.chimaeraconsulting. com/16pf.htm. Fehringer, H. M. (2004). Contributions and limitations of Cattell’s sixteen personality factor model. Retrieved Feb. 26, 2008, from http://www.personalityresearch.org/ papers/fehringer.html. Fenn, J., & Raskino, M. (2008). Mastering the hype cycle: How to choose the right innovation at the right time. Boston, MA: Harvard Business Press. Fiedler, S. (2004). Personal Webpublishing as a reflective conversational tool for self-organized learning. In T. Burg (Ed.), BlogTalks (pp. 190-216). Retrieved on May 7, 2007 from http://seblogging.cognitivearchitects.com/ stories/storyReader$963 Fielding, R. T. (2000). Architectural Styles and the Design of Network-based Software Architectures. University of California, Irvine. Fillion, G. (2004). Publishing in the organizational sciences: An extended literature review on the dimensions and elements of an hypothetico-deductive scientific research, and some guidelines on “how” and “when” they should be integrated. Academy of Information and Management Sciences Journal, 7(1), 81–114. Flanagan, D. P., Ortiz, S. O., Alfonso, V. C., & Mascolo, J. T. (2002). The achievement test desk reference. Boston: Allyn & Bacon. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American. Ford, N., & Chen, S. Y. (2000). Individual differences, hypermedia navigation and learning: an empirical study. Journal of Educational Multimedia and Hypermedia, (9): 281–311. Fox, S. (2005). An actor-network critique of community in higher education: implications for networked learning. Studies in Higher Education, 30(1), 95–110. doi:10.1080/0307507052000307821

Compilation of References

Galeev, I., Tararina, L., & Kolosov, O. (2004, August 30-September 1). Adaptation on the basis of the skills overlay model. In Proceedings of IEEE International Conference on Advanced Learning Technologies (pp. 648-650). Joensuu, Finland: IEEE Computer Society. Galliani, L. (2004). La scuola in rete. Bari: Laterza. Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105. Georgakopoulos, D., Hornick, M., & Sheth, A. (1995). An overview of workflow management: From process modeling to workflow automation infrastructure. Distributed and Parallel Databases, 3(2), 119-153. Giardina, M., Oubenaissa, L., & Bhattacharya, M. (2002). Designing a framework for the implementation of situated online, collaborative, problem-based activity: Operating within a local and multi-cultural learning context. International Journal on E-Learning, 1(3), 41–46. Norfolk, VA: AACE. Gibb, A. (1997). Small firms training and competitiveness: building up the small business as a learning organisation. International Small Business Journal, 3, 13–29. doi:10.1177/0266242697153001 Giddens, A. (1999). The third way: The renewal of social democracy. Melden, MA: Polity Press. Gilbert, J., Morton, S., & Rowley, J. (2007). e-learning: The student experience. British Journal of Educational Technology, 38(4), 560–573. doi:10.1111/j.14678535.2007.00723.x Gillet, D., Salzmann, C., & Rekik, Y. (2006). Awareness: An Enabling Feature for Mediated Interaction in Communities of Practice. In Proc. TEL-CoPs’06 - 1st Int. Workshop on Building Technology Enhanced Learning solutions for Communities of Practice, Crete, Greece. Go test go Inc. (2009). Retrieved from http://www. gotestgo.com Gogoulou, A., Gouli, E., Grigoriadou, E., & Samarakou, M. (2005). ACT: A Web-based adaptive communication

tool. In Proceedings of Computer Supported Collaborative Learning 2005: The Next 10 Years! Mahwah, NJ: Lawrence Erlbaum Assoc. Goh, T. Kinshuk, & Lin, T. (2003). Developing an adaptive mobile learning system. In Proc. of the International Conference on Computers in Education, Hong Kong 2003 (pp. 1062-1065). Golder, S. A., & Huberman, B. A. (2005). The Structure of Collaborative Tagging Systems. Retrieved fromn http:// arxiv.org/abs/cs.DL/0508082 Gomez, E. A., Wu, D., Passerini, K., & Bieber, M. (2007). Utilizing Web tools for computer-mediated communication to enhance team-based learning. International Journal of Web-Based Learning and Teaching Technologies, 2(2), 21–37. González-Tablas, A. I., Wouters, K., & Ramos, B. (2004). Teaching X.509/PKIX based digital signatures while enhancing non-repudiation of a Web based assessment system. In Proceedings of the IADIS International Conference WWW/Internet, Madrid, Spain (Vol. 1, pp. 43-51). González-Tablas, A. I., Wouters, K., Ramos, B., & Ribagorda, A. (2007). EVAWEB: A Web-based assessment system to learn X.509/PKIX digital signatures. IEEE Transactions on Education, 50(2), 112-117. Goodyear, P. (2002). Psychological foundations of networked learning. In C. Steeples & C.Jones (Eds.), Networked learning: Perspectives and Issues (chap. 4, pp. 49-76). London: Springer. Gordon-Murnane, L. (2006). Social Bookmarking, Folksonomies, and Web 2.0 Tools, 14(2), 26-38. Gorman, G., Hanlon, D., & King, W. (1997). Some research perspectives on entrepreneurship education, enterprise education and education for small business management: a ten year literature review. International Small Business Journal, 3, 56–77. doi:10.1177/0266242697153004 Gough, H. G. (1975). Manual for the CPI, California Psychological Inventory. Consulting Psychologists Press.

353

Compilation of References

Govindaraju, M., Slominski, A., Choppella, V., Bramley, R., & Gannon, D. (2000). Requirements for and Evaluation of RMI Protocols for Scientific Computing. Paper presented at the IEEE/ACM SC2000, Dallas, Texas, USA. Graham, G. (2005). Behaviorism. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Fall 2002 ed.). Retrieved June 24, 2008, from http://plato.stanford.edu/ entries/behaviorism/#1 Gray, N. A. B. (2004). Comparison of Web Services, Java-RMI, and CORBA service implementations. Paper presented at the Fifth Australasian Workshop on Software and System Architectures (AWSA 2004), Melbourne, Australia. Green, S. G., & Taber, T. D. (1980). The effects of three social decision schemes on decision group process. Organizational Behavior and Human Performance, 25, 97–106. doi:10.1016/0030-5073(80)90027-6 Greenberg, S., & Roseman, M. (1996). Workspace Awareness for Groupware. In Proc. CHI’96 Conference (Companion), Vancouver, Canada (pp. 208-209). Gross, T., Stary, C., & Totter, A. (2005). User-Centered Awareness in Computer-Supported Cooperative WorkSystems: Structured Embedding of Findings from Social Sciences. International Journal of Human-Computer Interaction, 18(3), 323–360. doi:10.1207/s15327590ijhc1803_5 Grosso, W. (1998). Java RMI. O’Reilly. Guo, Y., Bai, Sh., Yang, Zh., & Zhang, K. (2005). Analyzing scale of web logs and mining users’ interests. Chinese Journal of Computers, 28(9), 1483–1496. Gutwin, C., Greenberg, S., & Roseman, M. (1996). Workspace Awareness in Real-Time Distributed Groupware: Framework, Widgets, and Evaluation. In Proc. Conf. on Human-Computer Interaction - HCI’96, London, UK (pp. 281-298). Gutwin, C., Stark, G., & Greenberg, S. (1995). Support for workspace awareness in educational groupware. In Proc. 1st Int. Conf. on Computer Support for Collaborative Learning, Bloomington, IN (pp. 147-156).

354

Hackman, J., & Oldham, G. (1975). Development of the job diagnostic survey. The Journal of Applied Psychology, 60(2), 159–170. doi:10.1037/h0076546 Haggod, M. C. (2003). New media and online literacies: no age left behind. Reading Research Quarterly, 3, 387–391. Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Prentice Hall. Hallet, K., & Cummings, J. (1997). The virtual classroom as authentic experience. In Proceedings of the Annual Conference on Distance Teaching and Learning: Competition, Connection, Collaboration (pp. 103-107). Madison, WI: University of Wisconsin-Madison. Hambleton, R. K., Swamination, H., & Rogers, H. J. (1991). Fundamentals of Item Response Theory. Thousand Oaks, CA: Sage Publications Inc. Hamburg, I., Busse, T., & Marin, M. (2005). Using elearning scenarios for making decisions in organizations. th In Proceedings of the 6 European Conference on ELearning, E-Business, E-Government, E-Work, E-Health, E-Democracy, E-Mediary, Virtual Institutes, On-Line, BB Services, ERA and their Influences on the Economic and Social Environment, Bucharest, Romania. Hammond, T., Hannay, T., & Lund, B. (2004, December). The Role of RSS in Science Publishing. Syndication and Annotation on the Web. D-Lib Magazine. Retrieved on April 20, 2007 from http://www.dlib.org/dlib/december04/hammond/12hammond.html Hammond, T., Hannay, T., Lund, B., & Scott, J. (2005). Social Bookmarking Tools – A Case Study. D-Lib Magazine, 11(4). doi:10.1045/april2005-hammond Hand, D. J. (1999). Statistics and data mining: intersecting disciplines. ACM SIGKDD Explorations, 1, 16–19. doi:10.1145/846170.846171 Harasim, L. M. (1987a). Computer-mediated cooperation in education: Group learning networks. Proceedings of the Second Guelph Symposium on Computer Conferencing, June 1-4.

Compilation of References

Harasim, L. M. (1987b). Teaching and learning online: Issues in designing computer-mediated graduate courses. Canadian Journal of Educational Communication, 16(2), 117–135. Harasim, L. M., Hiltz, S. R., Teles, L., & Turoff, M. (1995). Learning networks: A field guide to teaching and learning online. Cambridge, MA: MIT Press. Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440–465. doi:10.1287/mnsc.40.4.440 Harvey, J., & Mogey, N. (1999). Pragmatic issues when integrating technology into the assessment of students. In S. Brown, P. Race, & J. Bull (Eds.), Computer-assisted assessment in higher education. London: Kogan-Page. Hatcher, L. (1994). Astep-by-step approach to using the ® ® SAS system for factor analysis. Cary, NC: The SAS Institute. Hathaway, S. R. (1951). Minnesota multiphasic personality inventory: manual. Psychological Corp. Hathaway, S.R., & McKinley J.C. (1967). Minnesota multiphasic personality inventory. fas.umontreal.ca. Hauser, L. (2005). Behaviorism. In The Internet encyclopedia of philosophy. Retrieved June 24, 2008, from http://www.utm.edu.research/iep/b/behavior.htm Hay, K. E., Elliot, D., & Kim, B. (2002). Collaborative network-based virtual reality: The past, the present, and the future of the virtual solar system. Paper presented at the CSCL conference, Boulder, CO. Retrieved July 6, 2006, from http://newmedia.colorado.edu/cscl/151.pdf Hazari, S. (2004). Strategy for assessment of online course discussions. Journal of Information Systems Education, 15(4), 349-356. Helic, D., Hrastmik, J., & Maurer, H. (2005). An analysis application of business process management technology in e-learning systems. In Proceedings of the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (e-Learn 2005), Vancouver, Canada.

Hembree, R. (1988). Correlates, causes, effects, and treatment of test anxiety. Review of Educational Research, 58, 47–77. Hennessy, S., Ruthven, K., & Brindley, S. (2005). Teacher perspectives on integrating ICT into subject teaching: commitment, constraints, caution, and change. Journal of Curriculum Studies, 37(2), 155–192. doi:10.1080/0022027032000276961 Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75–105. Heymann, P., Ramage, D., & Garcia-Molina, H. (2008). Social tag prediction. In SIGIR ‘08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 531-538). New York: ACM. Hiltz, S. R. (1988). Learning in a virtual classroom. A virtual classroom on EIES: Final evaluation report (Vol. 1). Newark, NJ: New Jersey Institute of Technology. Hiltz, S. R. (1990). Evaluating the virtual classroom. In L.M. Harasim (Ed.), Online education: Perspectives on a new environment (pp. 133-183). New York: Praeger Publishers. Hiltz, S. R. (1994). The Virtual Classroom: Learning Without Limits Via Computer Networks. Human-Computer Interaction Series, Norwood, NJ: Ablex Publishing Corporation. Hiltz, S. R., & Turoff, M. (1997). Teaching in a virtual classroom. Collection of 81 PowerPoint slides used to structure an online course. Retrieved December 30, 2008, from http://eies.njit.edu/~hiltz/CRProject/VC/ VCtraining/ index.htm Hiltz, S. R., & Wellman, B. (1997). Asynchronous learning networks as a virtual classroom. Communications of the ACM, 40(9), 44–49. doi:10.1145/260750.260764 Hobbs, M., Brown, E., & Gordon, M. (2006). Using a virtual world for transferable skills in gaming education. ITALICS, 5(3).

355

Compilation of References

Hobbs, R. (2006). Multiple visions of multimedia literacy: emerging areas of synthesis. In M. C. McKenna, L. D. Labbo, R. D. Kieffer, & D. Reinking. International handbook of literacy and technology (vol. II, pp. 15-28). Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Hobbs, V. M., & Osburn, D. D. (1989). Distance learning evaluation study report II: A study of North Dakota and Missouri schools implementing German I by satellite. ERIC ED, 317, 195. Hodgins, H. W. (2000). Into the future: A vision paper. Commission on Technology and Adult Learning. Retrieved June 24, 2008, from http://www.learnativity. com/download/MP7.pdf Hof, R. (2006, March 28). A virtual world’s real dollars. Business Week. Holmberg, B. (1995). Theory and Practice of Distance Education (2nd ed.). London: Routledge. Holmes, B., Tangney, B., FitzGibbon, A., Savage, T., & Mehan, S. (2001). Communal Constructivism: Students constructing learning for as well as with others. In C. Crawford, D. A. Willis, R. Carlsen, I. Gibson, K. McFerrin & J. Price, et al. (Eds.), Proceedings of Society for Information Technology and Teacher Education Inte rnational Conference 2001 (pp. 3114-3119). Norfolk, VA: AACE. Honey, P., & Mumford, A. (1992). The manual of learning styles. Maidenhead. Hsi, S., & Hoadley, C. M. (1997). Productive discussion in science: gender equity through electronic discourse. Journal of Science Education and Technology, 6(1). doi:10.1023/A:1022564817713 Hsu, C., & Backhouse, J. (2002). Information systems security education: Redressing the balance of theory and practice. Journal of Information Systems Education, 13(3), 211-218. Hsu, J. (2008). Innovative Technologies for Education and Learning: Education and Knowledge-Oriented Applications of Blogs, Wikis, Podcasts, and More. International Journal of Web-Based Learning and Teaching Technologies, 3(3), 62–81.

356

Hug, T. (2005, May 6-8). Micro learning and narration. In Proceedings of fourth Media in Transition Conference. Cambridge, MA: MIT Hughes, C., & Moshell, E. (1997). Shared virtual worlds for education: The ExploreNet experiment. ACM Multimedia, 5(2), 145-154. Huh, J., & Hirumi, A. (2004). Reading Assessment Strategies for On-Line Learners. Chicago: Association for Educational Communications and Technology. Interactive Advertising Bureau Brasil (2007). Boletim de Mídia Maio de 2007. Retrieved from http://www. iabbrasil.org.br/ Huitt, W. (2003). Constructivism. Educational Psychology Interactive. Retrieved June 24, 2008, from http:// chiron.valdosta.edu/whuitt/col/cogsys/construct.html Huitt, W., & Hummel, J. (1998). An overview to the behavioral perspective. Retrieved June 24, 2008, from http://chiron.valdosta.edu/whuitt/col/behsys.html Hurst, B. (2005). My journey with learning logs. Journal of Adolescent & Adult Literacy, 49(1), 42–46. doi:10.1598/ JAAL.49.1.5 Hutchins, M. A., Stevenson, D. R., Gunn, C., Krumpholz, A., Adriaansen, T., Pyman, B., & O’Leary, S. (2005). Communication in a networked haptic virtual environment for temporal bone surgery training. Virtual Reality (Waltham Cross). doi:.doi:10.1007/s10055-005-0015-1 IEEE Learning Technology Standards Committee. (2002). Learning object metadata (LOM) (IEEE 1484.12.1). IEEE. (2002). Final 1484.12.1-2002 LOM Draft Standard. Retrieved from http://ltsc.ieee.org/wg12/files/ LOM_1484_12_1_v1_Final_Draft.pdf IMS Global Consortium. (2003). IMS digital repositories interoperability: Core functions information model IMS DRI. Author. IMS Global Consortium. (2006). IMS question and test interoperability, Version 2.1. Author. IMS Global Consortium.(2001).IMS learning design information model, Version 1.0. Author.

Compilation of References

IMS Global Learning Consortium. (2004). IMS Content Packaging Best Practice and Implementation Guide. Version 1.1.4 Final Specification Implementation guidelines. Retrieved from http://www.imsglobal.org/content/ packaging/cpv1p1p4/imscp_bestv1p1p4.html IMS Global Learning Consortium. (2006). IMS General Web Services Specification - Version 1 Final Specification. 1.0 Final. Retrieved January 7, 2009, from http:// www.imsglobal.org/gws/index.html IMS Global Learning Consortium. (2008). IMS Common Cartridge Specification. Version 1.0 final. Retrieved January 7, 2009, from http://www.imsglobal.org/cc/ index.html International Journal of Knowledge and Learning, 1(4), 332-334. ISO/IEC 13888-3. (1997). Information technology: Security techniques. Non-repudiation: Part 3. Mechanisms using asymmetric techniques.

ciation for Information Systems (AIS) 1997 Americas Conference, Indianapolis, Indiana, August 15-17. Jarczyk, A., Löffler, P., & Shipman, F. (1992). Design Rationale for Software Engineering: A Survey. 25th Hawaii International Conference on System Sciences (Vol. 2, pp. 577-586). Jegede, O. J., & Kirkwood, J. (1992). Student’s anxiety in learning through distance education. This report is part of a larger project funded by a University of Southern Queensland Internal Research Grant, Toowoomba, Australia. ERIC ED, 360, 476. Johnson, A., Roussos, M., Leigh, J., Barnes, C., Vasilakis, C., & Moher, T. (1998). The NICE Project: Learning Together in a Virtual World. In Proceedings of IEEE Virtual Reality Annual International Symposium (pp. 176-183). Johnson, D. W., & Johnson, R. (1992). Positive interdependence: Key to effective cooperation. In

ISO/IEC 7498-2. (1988). Information processing systems: Open systems interconnection. Basic reference model: Part 2. Security architecture.

Johnson, D. W., Johnson, R. T., & Smith, K. A. (1998). Active learning: Cooperation in the college classroom. Edina, MN: Interaction Book Company.

ISTE. International Society for Technology in Education (2000). National Educational Technology Standards for Students. Eugene: ISTE.

Joia, L. A. (2001). Evaluation of hybrid socio-constructivist model for teacher training. Journal of Technology and Teacher Education, 9(4), 519–549.

Ives, B., & Olson, M. (1984). User involvement and MIS success: A review of research. Management Science, 30(5), 586–603. doi:10.1287/mnsc.30.5.586

Joia, L. A., & Casado, N. (2007, September 23–26). Fatores criticos de sucesso em treinamentos corporativos a distância via Web: Evidências empírico-exploratóriasa partir de um estudo de caso. In Proceedings of the 31st EnANPAD (Brazilian Academy of Management Meeting), Rio de Janeiro.

Jackson, B., & Anagnostopoulou, K. (2001). Making the right connections: Improving quality in online learning. In J. Stephenson (Ed.), Teaching and Learning Online: Pedagogies for New Technologies (pp. 53-64). London: Kogan Page. Jackson, R. L., & Winn, W. (1999). Collaboration and learning in immersive virtual environments. In C. Hoadley & J. Roschelle (Eds.), Proceedings of the Computer Support for Collaborative Learning (CSCL) 1999 Conference. Mahwah, NJ: Lawrence Erlbaum. Jankowski, D. (1997). Student attitudes toward Internetresident course materials. Paper presented at the Asso-

Joly, M. C. R. A. (2002). Avaliando o uso educacional de recursos tecnológicos em leitura e escrita. In M.C.R.A. Joly (Ed.), A tecnologia no ensino: implicações para aprendizagem. (p.117-142). São Paulo: Casa do Psicólogo. Joly, M. C. R. A. (2004). Evidências de validade de uma escala de desempenho docente em informática educacional. Psico-USF, 9, 173–180.

357

Compilation of References

Joly, M. C. R. A. (2005). Teste de Cloze por opção Ensino Médio. (Tech. Report). Universidade São Francisco, Itatiba, Brazil.

Joly, M. C. R. A., Santos, A. A. A., & Sisto, F. F. (2005). Questões do cotidiano universitário (1st ed.). São Paulo: Casa do Psicólogo.

Joly, M. C. R. A. (2006). Cloze Oriented System (COS). (Tech. Report). Universidade São Francisco, Itatiba, Brazil.

Joly, M.C.R.A. & Martins, R.X. (2005a). Estudo de validade da Escala de Desempenho em Informática Educacional para professores. Avaliação psicológica, 4(2), 105-113.

Joly, M. C. R. A. (2007). The validity of Cloze Oriented System (COS): a correlation study with an electronic comprehension test and a reading attitude survey. Psicologia Escolar e Educacional, 11 (especial), 49-59. Joly, M. C. R. A., & Guerra, P. B. C. (2004). Compreensão em leitura e barreiras à criatividade: um estudo com universitários ingressantes. PSICO, 35(2), 151–160. Joly, M. C. R. A., & Martins, R. X. (2005b). Escala de desempenho em tecnologias para educação – EDTEC (Tech. Report). Universidade São Francisco, Itatiba, Brazil. Joly, M. C. R. A., & Martins, R. X. (2008). Habilidades em tecnologias: avaliação de professores da educação básica brasileira. Psicologia e desenvolvimento tecnológico. Psicol. Am. Lat. [online]. July 2008, no.13 [cited 12 January 2009]. Retrieved from http://pepsic. bvs-psi.org.br Joly, M. C. R. A., & Silveira, M. A. (2003). Avaliação preliminar do Questionário de Informática Educacional (QIE). Psicologia em Estudo, 8(1), 85–92. doi:10.1590/ S1413-73722003000100011 Joly, M. C. R. A., Capovilla, A. S. G., Bighetti, C. A., Neri, M. L., & Nicolau, A. F. (2005). The reading comprehension of freshmen students: comparing printed and digital texts. In Mendéz-Villas, B.G.Pereira, J.M. González, & J.A. M. González. (Eds.), Recent Research Developments in Learning Technologies (pp. 823-828). Badajoz: Indugrafic. Joly, M. C. R. A., Nunes, C. H. S. S., & Istome, A. C. (2007). Desempenho em tecnologia e traços de personalidade: estudo de validade com universitários. Psi (São Paulo), 8, 85–94.

Jonassen, D. H. (1981). What are cognitive tools? University of Colorado, U.S.A. Retrieved June 24, 2008, from http://www.cs.umu.se/kurser/TDBC12/HT99/ Jonassen.html Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology: Research and Development, 45(1), 65-95. Jonassen, D. H. (2000). Toward a meta-theory of problemsolving. Educational Technology: Research & Development, 48(4), 63-85. Jonassen, D. H., Mayes, T., & McAleese, R. (1993). A manifesto for a constructivist approach to uses of technology in higher education. In T. M. Duffy, J. Lowyck, D. H. Jonassen & T. Welsh (Eds.), Xpert.press: Vol. Vol. 105. Designing environments for constructive learning. Proceedings of the Nato Advanced Research Workshop on the Design of Constructivist Learning Environments Implications for Instructional Design and the Use of Technology, held at the Catholic University Leuven, Belgium, May 14 - 18, 1993 (pp. 231-247). Berlin/ Heidelberg: Springer. Jonassen, D., Davidson, M., Collins, M., Campbell, J., & Hag, B. (1995). Constructivism and Computermediated Communication in Distance Education. American Journal of Distance Education, 9(2), 7–26. doi:10.1080/08923649509526885 Jones, S. (2006). The Internet Goes to College How students are living in the future with today’s technology. Pew Internet & American Life Project. Retrieved from http://www.pewinternet.org/ Jones, V., & Jo, H. J. (2004).Ubiquitous learning environment: An adaptive teaching system using ubiquitous

358

Compilation of References

technology. In Proceedings of the 21st ASCILITE Conference, Perth, Western Australia. Juric, M. B., Kezmah, B., Hericko, M., Rozman, I., & Vezocnik, I. (2004). Java RMI, RMI tunneling and Web services comparison and performance analysis. Software, Practice & Experience, 36(14), 1543–1562. Kafai, Y., & Resnick, M. (Eds.). (1996). Constructionism in practice: Designing, thinking, and learning in a digital world. Lawrence Erlbaum Associates. Kalawsky, R. S. (1998). Exploiting Virtual Reality Techniques in Education and Training: Technological Issues, A report prepared for AGOCG. Retrieved January 14, 2009 from http://www.agocg.ac.uk/reports/virtual/ vrtech/title.htm Kalyuga, S. (2006). Assessment of learners’ organized knowledge structures in adaptive learning environments. Applied Cognitive Psychology, 20(3), 333-342. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23-31. Kantel, J. (2003). Vom Weblog lernen... Community, Peer-to-Peer und Eigenständigkeit als ein Modell für zukünftige Wissenssammlungen. Retrieved on March 12, 2007 from http://static.userland.com/sh4/gems/ schockwellenreiter/blogtalktext.pdf Kanuka, H., & Anderson, T. (1999). Using constructivism in technology-mediated learning: Constructing order out of the chaos in the literature. Radical Pedagogy, 1(2). Karacapilidis, N., & Papadias, D. (2001). Computer Supported Argumentation and Collaborative Decision Making: The HERMES system. Information Systems, 26(4), 259–277. doi:10.1016/S0306-4379(01)00020-5 Karacapilidis, N., & Tzagarakis, M. (2007a). Supporting Incremental Formalization in Collaborative Learning Environments. In Proc. 2nd European Conference on Technology Enhanced Learning (EC-TEL 2007), Crete, Greece (LNCS 4753, pp. 127–142).

Karacapilidis, N., & Tzagarakis, M. (2007b). Web-based collaboration and decision making support: A multi-disciplinary approach. International Journal of Web-Based Learning and Teaching Technologies, 2(4), 12–23. Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS Quarterly, 30(4), 781–804. Karp, D. A., & Yoels, W. C. (1976). The college classroom: Some observations on the meanings of student participation. Sociology and Social Research, 60, 421–439. Kay, H., Dodd, B., & Sime, M. (1970). Iniciação à instrução programada e às máquinas de ensinar. São Paulo: IBRASA. Keegan, D. (1995). Distance Education Technology for the New Millenium: Compressed Video Teaching. FernUniversität, Hagen, Germany. Keegan, D. (1996). Foundations of Distance Education (3rd ed.). London: Routledge. Keller, J., & Suzuki, K. (2004). Learner motivation and e-learning design: A multinationally validated process. Learning, Media and Technology, 29(3), 229–239. Keller, T., & Dansereau, F. (2001). The effect of adding items to scales: An illustrative case of LMX. Organizational Research Methods, 4(2), 131–143. doi:10.1177/109442810142003 Kennedy, G. E., Judd, T. S., Churchward, A., Gray, K., & Krause, K.-L. (2008). First year students’ experiences with technology: Are they really digital natives? Australasian Journal of Educational Technology, 24(1), 108-122. Retrieved June 1, 2008, from http://www.ascilite.org.au/ ajet/ajet24/kennedy.html Kerres, M. (2006). Web 2.0 and its implications to ELearning. In T. Hug, M. Lindner & P.A. Bruck (Eds.), Micromedia & e-Learning 2.0: Gaining the Big Picture, Proceedings of Microlearning Conference 2006 – Innsbruck, June 9 2004. University press Kerres, M. (2007). Microlearning as a challenge for instructional design. In T. Hug & M. Lindner (Eds.) Didactics of Microlearning. Muenster: Waxmann. Retrieved

359

Compilation of References

on March 10, 2007 from mediendidaktik.uni-duisburgessen.de/files/Microlearning-kerres.pdf Kim, H., & Chan, K. (2008). Learning implicit user interest hierarchy for context in personalization. Applied Intelligence, 28, 153–166. doi:10.1007/s10489-007-0056-0 Kinuthia, W., & Dagada, R. (2008). E-learning incorporation: An exploratory study of three South African higher education institutions. International Journal on E-Learning, 7(4), 623–639. Kirner, T. G., Kirner, C., Kawamoto, A. L. S., Cantão, J., Pinto, A., & Wazlawick, R. S. (2001). Development of a Collaborative Virtual Environment for Educational Applications. In Proceedings of the sixth international conference on 3D Web Technology (pp. 61-68). Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instructions does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based learning. Educational Psychologist, 41(2), 75-86. Kirschner, P., Strijbos, J. W., Karel Kreijns, K., & Beers, P. J. (2004). Designing electronic collaborative learning environments. Educational Technology Research and Development, 52(3), 47–66. Klamma, R., Chatti, M. A., Duval, E., Fiedler, S., Hummel, H., Hvannberg, E. T., et al. (2006). Social software for professional learning: Examples and research issues. In B. Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. Sampson & W. Didderen (Eds.), Proceedings of the Sixth International Conference on Advanced Learning Technologies (pp. 912-914). IEEE. Klamma, R., Chatti, M. A., Duval, E., Hummel, H., Hvannberg, E. T., Kravcik, M., et al. (2007). Social Software for Life-long Learning. Educational Technology & Society, 10(3), 72-83. Retrieved from http://dspace.ou.nl/ bitstream/1820/910/4/ET%26S\_socialsoftware.pdf Knight, P. (2007). Promoting retention and successful completion on masters courses in education: A study comparing e-tuition using asynchronous conferencing software with face-to-face tuition. Open Learning, 22(1), 87–96. doi:10.1080/02680510601100184

360

Kobsa, A., Koenemann, J., & Pohl, W. (2001). Personalised hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review (pp. 111-155). Cambridge University Press. Kohlhase, A. (2006). The User as Prisoner: How the Dilemma Might Dissolve. See (Memmel et al., 2006), pp. 26 – 31. Kohlhase, A. (2008). Semantic Interaction Design: Composing Knowledge with CPoint. Dissertation thesis. Retrieved from http://kwarc.info/ako/pubs/AKo_Promo. pdf. Kolb, A. (1984), Experiential learning: experience as the source of learning and development. Boston: McBer and Company. Koslin, B. L., Zeno, S., & Koslin, S. (1987). The DRP: An effectiveness measure in reading. New York: TASA. Koubek, A., & Müller; K. (2002). Collaborative Virtual Environments for Learning. ACM SIG Proceedings. Kowch, E. G., & Schwier, R. A. (1997). Characteristics of Technology-Based Virtual Learning Communities. Retrieved September 27, 2003 from http://www.usak. ca/education/coursework/802papers/communities/ community. Kreijns, K., Kirschner, P. A., & Jochems, W. (2002). The sociability of computer-supported collaborative learning environments. Educational Technology & Society, 5(1). Kreijns, K., Kirschner, P., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computersupported collaborative learning environments: a review of the research. Computers in Human Behavior, 19, 335–353. doi:10.1016/S0747-5632(02)00057-2 Kruh, J. J., & Murphy, K. L. (1990), Interaction in teleconferencing: The key to quality instruction. Paper presented at the 12th Annual Rural and Small Schools Conference, Kansas State University, Manhattan. ERIC ED 329 498. Krysiński, J., Skzypczak, A., Demski, G., & Predki, B. (2001). Application of the rough set theory in

Compilation of References

structure activity relationships of antielectrostatic imidazolium compounds. Quantitative StructureActivity Relationships, 20, 395–401. doi:10.1002/15213838(200112)20:5/63.0.CO;2-# Kuljis, J., & Lees, D. Y. (2002). Lessons from Industry in the Design of Virtual Collaborative Learning Environments. In Proceedings of International Conference Information Technology Interfaces - ITI 2002Cavtat, Croatia (pp. 31-36). Kutscher, N., Klein, A., & Iske, S. (2005). Differences in Internet Usage - Social Inequality and Informal Education. Vol. 3. Social Work & Society (pp. 215-233). Laister, J., & Kober, S. (2002). Social Aspects of Collaborative Learning in Virtual Learning Environments. In Proceedings of the Networked Learning 2002 Conference. Retrieved October 7, 2007 from http://www. networkedlearningconference.org.uk/past/nlc2002/ proceedings/papers/19.htm Lambiotte, R., & Ausloos, M. (2005).Collaborative Tagging as a Tripartite Network. arXiv:cs.DS/0512090 v2. Landry, J. R. (2000). Playing at Learning: Why Knowledge Creation Needs Fun. In HICSS. Langer, E. (1998). The Power of Mindful Learning. Perseus Books Group Lathem, J., Gomadam, K., & Sheth, A. P. (2007). SAREST and (S)mashups: Adding Semantics to RESTful Services. Paper presented at the International Conference on Semantic Computing (ICSC 2007), Irvine, CA. Laurillard, D.M., Stratfold, M., Luckin, R., Plowman, L., & Taylor, J. (2000). Affordances for learning in a non-linear narrative medium. Journal of Interactive Media in Education, 2.

classroom. IEEE Transactions on Consumer Electronics, 43(3), 264-270. Lee, K. J. (2006). What Goes Around Comes Around: An Analysis of del.icio.us as Social Space. Computer Supported Cooperative Work, 2006, CSCW ‘06. 20th Anniversary Conference (pp. 181-190). Lego (2006). Retrieved from Online at http://shop.lego. com/product.asp?p=B8527&cn=55&d=11&t=5. Leidner, D. E., & Jarvenpaa, S. L. (1993). The information age confronts education: Case studies on electronic classrooms. Information Systems Research, 4(1), 24–54. doi:10.1287/isre.4.1.24 Leidner, D. E., & Jarvenpaa, S. L. (1995). The use of information technology to enhance management school education: A theoretical view. MIS Quarterly, 19(3), 265–291. doi:10.2307/249596 Lenhart, A., & Madden, M. (2005). Teen Content Creators and Consumers. Pew internet project data memo. Retrieved on June 21, 2007 from http://www.pewinternet. org/pdfs/PIP_Teens_Content_Creation.pdf Leo, D. H., Perez, J. I. A., & Dimitriadis, Y. A. (2004, August 30-September 1). IMS learning design support for the formalization of collaborative learning patterns. Paper presented at the IEEE International Conference on Advanced Learning Technologies (ICALT’04), Joensuu, Finland. Leont’ev, A. N. (1981). The problem of activity in psychology. In J. V. Wertsch (Ed.), The Concept of Activity in Soviet Psychology (pp. 37-71). Armonk, NY: Sharpe. Lesser, E., & Prusack, L. (1999). Communities of practice, social capital and organizational knowledge. Information Systems Research, 1(1), 3–10.

Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation (Learning in Doing: Social, Cognitive and Computational Perspectives). Cambridge University Press.

Lesser, E., & Prusak, L. (2000). Communities of Practices Social capital and Organisational knowledge. In E. Lesser, M. Fontaine, & J. Slusher (Eds.), Knowledge and Communities. Butterworth- Heinemann.

Lee, K. C., et al. (1997). Design and implementation of important applications in a Java-based multimedia digital

Leu, D. J. (1996). Exploring Literacy within Multimedia Environments. The Reading Teacher, 50, 162–165.

361

Compilation of References

Leu, D. J., Kinzer, C. R., Cairo, J. L., & Cammack, D. W. (2004). Toward a theory of new literacies emerging from de Internet amd other information and communication technologies. In R. B. Ruddell & N J. Unrau (Eds.), Theorical models and processes of reading (pp. 1570-1613). Newark: International Reading Association. Leu, D. J., Mallette, M. H., Karchmer, R. A., & KaraSoteriou, J. (2005). Innovative Approaches to Literacy Education:using the internet to support new literacies. Newark: International Reading Association. Lewinson, J. (2005). Asynchronous discussion forums in the changing landscape of the online learning environment. Campus-Wide Information Systems, 22(3), 162–167. doi:10.1108/10650740510606162 Liebregt, M. (2005). Collaborative Virtual Environments in education. Paper presented at 2nd Twente Student Conference on IT, Enschede 21 January. Lilley, M., & Barker, T. (2002). The Development and Evaluation of a Computer-Adaptive Testing Application for English Language. 6th Computer Assisted Assessment Conference, Loughborough. Limayem, M., & DeSanctis, G. (2000). Providing decisional guidance for multicriteria decision making in groups. Information Systems Research, 11(4), 386–401. doi:10.1287/isre.11.4.386.11874 Limayem, M., Banerjee, P., & Ma, L. (2002). Impact of GDSS: Opening the black box. Proceedings of the 6th Pacific Asia Conference on Information Systems (PACIS 2002), Tokyo, Japan, Sept. 2-4. Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–738. Linacre, J. M. (2000). Computer-Adaptive Testing: A Methodology whose Time has Come. MESA Memorandum No. 69. In S. Chae, U. Kang, E. Jeon & J.M. Linacre (Eds.), Development of Computerised Middle School Achievement Test (in Korean). Seoul, South Korea: Komesa Press.

362

Linacre, J. M. (2006). Computer-Adaptive Tests (CAT), Standard Errors and Stopping Rules. Rasch Measurement Transactions, 20(2), 1062. Retrieved from http://www. rasch.org/rmt/rmt202f.htm Lindeman, E. (1989).The Meaning of Adult Education. Norman, University of Oklahoma, USA. Linden Research. (2006). What is second life? Retrieved from http://secondlife.com/whatis/ Linn, M. C. (1996). Cognition and distance learning. Journal of the American Society for Information Science American Society for Information Science, 47(11), 826–842. doi:10.1002/(SICI)1097-4571(199611)47:113.0.CO;2-4 Lionarakis, A., & Papademetriou, D. (2003). The quality of the learning experience: A comparative study between open distance and conventional education. Turkish Online Journal of Distance Education, 4. Retrieved December 30, 2008, from http://tojde.anadolu.edu.tr/ tojde10/index.htm. Lister, R., & Jerram, P. (2001). Design for Web-based on-demand multiple choice exams using XML. In Proceedings of the IEEE International Conference on Advanced Learning Technology: Issues, Achievements and Challenges, Madison, WI (pp. 383-386). Liu, D., & Huang, X. (2002). The introduction of learning research. Education Research, 2, 81. Liu, J. N. K., & Cheng, X. (2008). An evaluation of the learning of undergraduates using e-learning in a tertiary institution in China. International Journal on E-Learning, 7(3), 427–447. Liu, J., Li, R., & Zheng, Q. (2004). Research of personality mining in e-learning. Xi’an Jiaotong University Journal, 38, 575–578. Liu, R., & Dai, M. (2003). Research of Chinese college foreign language instruction reform situation and development. Beijing: Foreign Language Education and Research Press, China. Lockwood, F. (1995). Open and Distance Learning Today. London: Routledge.

Compilation of References

Lord, F. M. (1980). Applications of Item Response Theory to Practical Testing Problems. New Jersey: Lawrence Erlbaum Associates. Lou, Y., Bernard, R., & Abrami, P. (2006). Media and pedagogy in undergraduate distance education: A theorybased meta-analysis of empirical literature. Educational Technology Research & Development, 54(2), 141-176. Luke, C. (2003). Pedagogy, connectivity, multimodality, andf interdisciplinary. Reading Research Quarterly, 3, 397–403. Lund, K. (2004). Human support in CSCL: What, for whom, and by whom? In J.-W. Strijbos, P. Kirschner, & R. Martens (Eds.), What we know about CSCL and implementing it in higher education (pp. 167-198). Dordrecht, The Netherlands: Kluwer Academic Publishers. Lupi, V., Pozzi, F., & Torsani, S. (2008). La dimension sociale dans un master à distance, post-universitaire et de traduction juridique. Apprentissage des Langues et Systèmes d’Information et de Communication (ALSIC), 11. Luria, A. R. (1976). Cognitive Development: Its Cultural and Social Foundations. Cambridge, MA: Harvard University Press. Lymna, F. (1981). The responsive classroom discussion. In A. S. Anderson (Ed.), Mainstreaming Digest. College Park, MD: University of Maryland College of Education. Macedonia, M. R., Zyda, M. J., Pratt, D., Brutzman, R., Donald, P., & Barham, P. T. (1995). Exploiting reality with multicast groups: A network architecture for largescale virtual environments. In Proceedings IEEE Virtual Reality Annual International Symposium (VRAIS’95), North Carolina. Magambo, J. (2007). Use of Information and Communication Technologies (ICTs) in Teacher Education in Sub-Saharan Africa: Case Studies of Selected African Universities. Doctoral Dissertation, der Universität zu Köln, Uganda. Retrieved December 30, 2008, from http:// deposit.ddb.de/cgi-bin/dokserv?idn=98581182x&dok_ var=d1&dok_ext=pdf&filename=98581182x.pdf

Mager, R. F. (1972). Objetivos para o ensino efetivo. Rio de Janeiro: SENAI. Maier, R. (2004). Knowledge Management Systems: Information and communication technologies for knowledge management (2nd ed.). Berlin: Springer. Makola, D., Sim, Y. W., Wang, C., & Gilbert, L. S, G., & Wills, G. (2006). A Service-Oriented Architecture for a Collaborative Orthopedic Research Environment. Paper presented at the WWW2006, Edinburgh, Scotland. Malone, J. C., Jr. (1999, January). Operants were never “emitted.” Feeling is doing, and learning takes only one trial: A review of B. F. Skinner’s recent issues in the analysis of behavior. Journal of the Experimental Analysis of Behavior, 1, 115–120. University of Tennessee. Mangan, P., & Sadiq, S. (2002, January-February). On building workflow models for flexible processes. Paper th presented at the 13 Australasian Database Conference (ADC2002), Melbourne, Australia. Manouselis, N., Kastrantas, K., Sanchez-Alonso, S., Cáceres, J., Ebner, H., & Palmér, M. (2009). Architecture of the Organic.Edunet Web Portal. [IJWB]. International Journal of Web Portals, 1(1), 7–19. Marini, J. A. S., & Joly, M. C. R. A. (2006). Escala de estratégia de leitura para o ensino médio - evidências de validade. Projeto de Qualificação. Programa de PósGraduação, Universidade São Francisco, Itatiba. Marlow, C., Naaman, M., Boyd, D., & Davis, M. (2006). HT06, tagging paper, taxonomy, Flickr, academic article, to read. In Proceedings of the seventeenth conference on Hypertext and Hypermedia (pp. 31–40). Marotzky, W. (1990). Entwicklung einer strukturellen Bildungstheorie: Biografie-theoretische Auslegung von Bildungsprozessen in hochkomplexen Gesellschaften. Weinheim: Deutscher Studienverlag. Marshall, J. M. (2002). Learning with technology: Evidence that technology can, and does, support learning. Report prepared for Cable in the Classroom Foundation, San Diego State University, San Diego. Retrieved December 30, 2008, from http://www.medialit.org/ reading_room/article545.html.

363

Compilation of References

Martin, K. (1998). WBI or not WBI? Issues of Teaching and Learning, 4(7). Retrieved June 24, 2008, from http://www.catl.uwa.edu.au/NEWSLETTER/issue0798/ dimensions.html

Mayorga, M. C., & Fernández, A. (2003). Learning Tools for Java Enabled Phones: An Application to Actuarial Studies. In Proc. of the International Conference MLEARN (pp. 95-98).

Martins, R. X. (2006). Tecnologias de Informação e Comunicação: Avaliação de Desempenho dos Estudantes. In M. C. R. A. Joly, & C.Vectori. (Eds.). Questões de Pesquisa e Práticas em Psicologia Escolar. (1st ed., pp. 191-214) São Paulo: Casa do Psicólogo.

McDermott, R. (2000b). Planned spontaneity. Knowledge Management Review, 3(4), 5.

Martins, R. X. (2008). Educational methods and its relations with cognitive abilities and technological performance. Doctoral Dissertation. Programa de PósGraduação Stricto Sensu em Psicologia, Universidade São Francisco, Itatiba, SP. Martz, W. B. Jr, & Shepherd, M. (2007). Managing distance education for success. International Journal of Web-Based Learning and Teaching Technologies, 2(2), 50–58. Mason, R. (1991). Moderating educational computer conferencing. DEOSNEWS, 1(19). Retrieved January 14, 2008, from http://pchfstud1.hsh.no/hfag/litteratur/ jenssen/deosnews/mason.htm Mason, R. (1998). Models of online courses. ALN Magazine, 2(2). Matthews, H. R. (2000). Virtual lectures free students to think critically. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 111113). Bolton, MA: Anker Publishing Company Inc. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43-52. Mayes, T. (2001). Learning technology and learning relationships. In J. Stephenson (Ed.), Teaching and Learning Online: Pedagogies for New Technologies (pp. 16-26). London: Kogan Page. Mayes, T., & de Freitas, S.(2004). Review of e-learning theories, frameworks and models: Stage 2 of the e-learning models desk study. JISC. Retrieved July 2007 from http://www.jisc.ac.uk/uploaded_docu ments/Stage%20 2%20Learning%20Models%20(V ersion%201).pdf

364

McGill, L., Nicol, D., Littlejohn, A., Grierson, H., Juster, N., & Ion, W. J. (2005). Creating an information-rich learning environment to enhance design student learning: challenges and approaches. British Journal of Educational Technology, 36(4), 629–642. doi:10.1111/j.14678535.2005.00540.x McKeen, J. D., Guimaraes, T., & Wetherbe, J. C. (1994). The relationship between user participation and user satisfaction: An investigation of four contingency factors. MIS Quarterly, 18(4), 427–451. doi:10.2307/249523 McNerney, T. S. (2004). From Turtles to Tangible Programming Bricks: Explorations in Physical Language Design. Personal and Ubiquitous Computing, 8(5). doi:10.1007/s00779-004-0295-6 Media Awareness Network. (2004). Young Canadians in a Wired World. Phase II. Focus Groups. Retrieved June 1, 2008, from http://www.media-awareness.ca/ english/research/YCWW/phaseII/upload/YCWWII_Focus_Groups.pdf. Mellis, D. A., et al. (2007): Arduino: An Open Electronics Prototyping Platform. alt.chi section of the CHI 2007conference in San Jose (CA). Memmel, M., Ras, E., & Weibelzahl, S. (2006). 2nd Workshop on Learner Oriented Knowledge Management & KM Oriented e-Learning (LOKMOL). Retrieved from http://cnm.open.ac.uk/projects/ectel06/pdfs/ECTEL06WS68d.pdf Mergel, B. (1998). Instructional design and learning theory. University of Saskatchewan. Retrieved June 24, 2008, from http://www.usask.ca/education/ coursework/802papers/mergel/brenda.htm Merriam, S. B. (1993). Adult learning: Where have we come from? Where are we headed? In An update

Compilation of References

on adult learning theory (pp. 5–14). San Francisco: Jossey-Bass. Michael, J. (2006). Where’s the evidence that active learning works? Advances in Physiology Education, 30(4), 159-167. Miezkowski, K. (2005). Steal this bookmark! Retrieved September 15, 2006 from http://dir.salon.com/story/tech/ feature/2005/02/08/tagging/index_np.html Miklos, Z., Neumann, G., Zdun, U., & Sintek, M. (2003, October 20-23). Querying semantic web resources using TRIPLE views. In Proceedings of the 2nd International Semantic Web Conference (ISWC), Sanibel Island, Florida, USA. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97. Millis, B. J., & Cottell, P. G. (1998). Cooperative learning for higher education faculty. American Council on Education, Series on Higher Education. The Oryx Press, Phoenix, AZ. Ministry of Education (2003), Piani di Studio Personalizzati e Unità di Apprendimento, Annali dell’istruzione (National report: R.I.So.R.S.E.. project), 49(3-4), 164169. Morra, L., & Friedlander, A. C. (1999, May). Case study evaluations (Working Paper Series No. 2). OED (Operations Evaluation Department), World Bank. Moshman, D. (1982). Exogenous, Endogenous and Dialectical Constructivism. Developmental Review, 2, 371–384. doi:10.1016/0273-2297(82)90019-3 Murray, T. (2001). Metalinks: authoring and affordances for conceptual and narrative flow in adaptive hyperbooks. The International Journal of Artificial Intelligence in Education. Special Issue on Adaptive and Intelligent Web-Based Systems, 13(II), 199–233. Myers, I. B., McCaulley, M. H., & Most, R. (1985). Manual: a guide to the development and use of the MyersBriggs type indicator. Consulting Psychologists Press.

Nagarajan, M. (2006). Semantic Annotations in Web Services. In J. Cardoso & A. P. Sheth (Eds.), Semantic Web and Beyond: Computing for Human Experience (1st ed., pp. 35-61). New York: Springer Science+Business Media, LLC. Nejdl, W., Wolf, B., Qu, C., Decker, S., Sintek, M., Naeve, A., et al. (2002, May 7-11). EDUTELLA: a P2P networking infrastructure based on RDF. In Proceedings of 11th World Wide Web Conference, Hawaii, USA. Nevado, R. A., Basso, M. V. A., & Menezes, C. S. (2004). Webfólio: Uma proposta para avaliação na aprendizagem: Conceitos, Estudos de Casos e Suporte Computacional. In Anais do Simpósio Brasileiro de Informática na Educação, Manaus-AM, Brazil. New Media Consultants. (2006). NMC Campus Observer. Retrieved from http://www.nmc.org/sl/ Newlin, M. H., Lavooy, M. J., & Wang, A. Y. (2005). An experimental comparison of conventional and webbased instructional formats. North American Journal of Psychology, 7(2), 327–336. Nguyen, H. S. (1998). Discretization problem for Rough Sets methods. In L. Polkowski & A. Skowron (Ed.) (LNAI, pp. 545-552). Berlin: Springer-Verlag. Nicol, D. J., & MacLeod, I. A. (2005). Using a Shared Workspace and Wireless Laptops to Improve Collaborative Project Learning in an Engineering Design Class, v44 n4 p459-475 May 2005, 459-475. Nilsson, M., Palmér, M., & Brase, J. (2003). The LOM RDF Binding - Principles and Implementation. In Proceedings of the Third Annual ARIADNE conference. Nilsson, M., Palmér, M., & Naeve, A. (2002). Semantic Web Meta-data for e-Learning - Some Architectural Guidelines. Paper presented at the 11th World Wide Web Conference (WWW2002), Hawaii, USA. NMC. (2007). The Spring 2007 Survey: Educators in Second Life. Retrieved from http://www.nmc.org/ pdf/2007-sl-survey-summary.pdf Nonaka, I., & Takeuchi, H. (1995). The knowledgecreating company: how Japanese companies create the

365

Compilation of References

dynamics of innovation. New York: Oxford University Press. Norton, M. J. (2004). A Comparison between the JISC and Sakai Frameworks. 3rd PDF. Retrieved from http:// sakaiproject.org/tech/S040405N.pdf Nutter, D., & Boldyreff, C. (2003). Historical Awareness Support and its Evaluation in Collaborative Software Engineering. In Proc. 12th Int. Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises. O’Reilly, T. (2005). Web 2.0: Compact Definition? Retrieved on May 17, 2007 from http://radar.oreilly.com/ archives/2005/10/web_20_compact_definition.html O’Reilly, T. (2005, September 30). What is Web 2.0: Design Patterns and Business Models for the next generation of software. O’Reilly. Object Management Group (OMG). (2006). BPMN v1.0: OMG final adopted specification. Author. Oblinger, D., & Oblinger, J. (2005). Is It Age or IT: First Steps Towards Understanding the Net Generation. In D. Oblinger & J. Oblinger (Eds.) Educating the Net Generation - Educating the Net Generation., Educause 2005, Retrieved on June 12, 2007 from http://www.educause. edu/educatingthenetgen/ OECD. (2007). Participative Web and User-created Content. Web 2.0, Wikis and Social Networking. Retrieved from http://213.253.134.43/oecd/pdfs/browseit/9307031E. PDF Okada, M., Tarumi, H., Yoshimura, T., & Moriya, K. (2001). Collaborative environmental education using distributed virtual environment accessible from real and virtual worlds. ACM SIGAPP Applied Computing Review, 9(1), 15–21. doi:10.1145/570142.570147 Okada, M., Yamada, A., Tarumi, H., Yoshida, M., & Moriya, K. (2003). DigitalEE II: RV-Augmented Interface Design for Networked Collaborative Environmental Learning. In Proceedings of the International Conference on Computer Support for Collaborative Learning (pp. 265-274).

366

Olivier, B., Roberts, T., & Blinco, K. (2005). The eFramework for Education and Research: An Overview. R1 PDF. Retrieved from http://www.e-framework.org/Portals/9/ Resources/eframeworkrV1.pdf Open Universiteit Nederland. (2006). Retrieved from http://www.incompany-milieuadvies.nl Osborne, A. (1963). Applied imagination (3rd ed.). New York: Scribner’s. Otto, H.-U., Kutscher, N., Klein, A., & Iske, S. (2005). Social Inequality in the Virtual Space: How Do Young People Use the Internet? Results from Empirical Research about Online Use Differences and Acquiring Patterns of Young People. Retrieved January 16, 2008, from http:// www.kib-bielefeld.de/externelinks2005/Social_Inequality%20KIB.pdf Oxford, R. L. (2001). Language learning styles and strategies. In M. Celce-Murcia (Ed.), Teaching English as a second or foreign language (3rd ed., pp. 359-366). Boston: Heinle & Heinle/Thompson International. Paas F., & Kester, L. (2006). Learner and information characteristics in the design of powerful learning environments. Applied Cognitive Psychology, 20(3), 281-285. Paas, F. G. W. C. (1992). Training strategies for at taining transfer of problem-solving skill in statistics: A cognitiveload approach. Journal of Educational Psychology, 84(4), 429-434. Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advanced cognitive load theory. Educational Psychologist, 38(1), 63-71. Pain, D., & Le Heron, J. (2003). WebCT and online assessment: The best thing since SOAP? Educational Technology and Society, 6(2), 62-71. Pallof, R. M., & Pratt, K. (2003). The virtual student: a profile and guide to working with online learners. John Wiley & Sons. Palloff, R., & Pratt, K. (2001). Lessons from the Cyberspace Classroom: The Realities of Online Teaching. San Francisco, CA: Jossey-Bass.

Compilation of References

Palmér, M., Naeve, A., & Paulsson, F. (2004). The SCAM Framework: Helping Semantic Web Applications to Store and Access Metadata. Paper presented at the European Semantic Web Symposium 2004, Heraclion Greece.

ented Architecture Framework for Modularized Virtual Learning Environments - Applying the Learning Object Concept to the VLE. International Journal on E-Learning, 5(1), 45–57.

Papanikolaou, K. A., Grigoriadou, M., Kornilakis, H., & Magoulas, G. D. (2003). Personalizing the interaction in a Web-based educational hypermedia system: the case of INSPIRE. User Modeling and User-Adapted Interaction, 13, 213–267. doi:10.1023/A:1024746731130

Pawlak, Z. (1998). Rough set theory and its applications to data analysis. Cybernetics and Systems, 29, 661–688. doi:10.1080/019697298125470

Papert, S. (2000). What’s the big idea? Towards a pedagogy of idea power. IBM Systems Journal, 39(34), 720–729. Parker, K. R., & Chao, J. T. (2007). Wiki as a Teaching Tool. Interdisciplinary Journal of Knowledge and Learning Objects, 3, 57–72. Paulsen, M. P. (1995). Moderating educational computer conferences. In Z. L. Berge & M. P. Collins (Eds.), Computer-mediated communication and the on-line classroom in distance education. Cresskill, NJ: Hampton Press. Paulsson, F. (2003). Standardized Content Archive Management – SCAM. IEEE Learning Technology newsletter, 5(1), 40-42. Paulsson, F. (2008). Modularization of the Learning Architecture: Supporting Learning Theories by Learning Technologies. Doctoral Thesis, Royal Institute for Technology (KTH), Stockholm. Paulsson, F., & Engman, J. (2005). Marking the National Curriculum - a new model for semantic mark-up. In P. Cunningham & M. Cunningham (Eds.), Innovation and the Knowledge Economy: Issues, Applications and Case Studies (Vol. 2, pp. 1731-1738). Amsterdam: IOS Press Amsterdam. Paulsson, F., & Naeve, A. (2006a). Establishing technical quality criteria for Learning Objects. In P. Cunningham & M. Cunningham (Eds.), Exploiting the Knowledge Economy: Issues, Applications, Case Studies (Vol. 3, pp. 1431-1439). Amsterdam: IOS Press. Paulsson, F., & Naeve, A. (2006b). Virtual Workspace Environment (VWE): A Taxonomy and Service Ori-

Pawlak, Z., Grzymala-Busse, J. W., Slowiński, R., & Ziarko, W. (1995). Rough sets. Communications of the ACM, 38, 88–95. doi:10.1145/219717.219791 Penuel, B., & Roschelle, J. (1999). Designing learning: Cognitive science principles for the innovative organization. Center for Technology in Learning, SRI International. Penuel, W. R., Korbak, C., & Cole, K. A. (2002). Designing assessments for student multimedia projects. Learning and Leading with Technology, 29, 46–53. Perry, N. E., Phillips, L., & Hutchinson, L. R. (2004). Preparing student teachers to support for self-regulated learning. The Elementary School Journal, 106(3), 237–254. doi:10.1086/501485 Phipps, R., & Merisotis, J. (1999). What’s the difference? A review of contemporary research on the effectiveness of distance learning in higher education. The Institute for Higher Education Policy, Washington, DC. Retrieved December 30, 2008, from http://www.ihep.org/Pubs/ PDF/Difference.pdf. Piaget, J. (1964). The construction of the reality in the child. Retrieved June 24, 2008, from http://www2.cddc. vt.edu/marxists/reference/subject/philosophy/works/fr/ piaget2.htm Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401–426. doi:10.2307/3250989 Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications (2nd ed.). Upper Saddle River, NJ: Merrill, Prentice Hall.

367

Compilation of References

Pitner, T., & Drášil, P. (2006). An E-learning 2.0 Environment - Principles, Technology and Prototype. Journal of Universal Computer Science, 2006, 543–550.

Immigrants - Part1.pdf and http://www.marcprensky. com/writing/Prensky -Digital Natives, Digital Immigrants - Part2.pdf

Poosala, V., & Ioannidis, Y. (1997). Selectivity estimation without the attribute value independence assumption. International Conference on Very Large Data Bases (pp. 486-495). Athens, Greece.

QuestionMark. Getting results (2009). Retrieved from http://www.questionmark.com

Portugal, R. C., Guerrero, L. A., & Fuller, D. A. (2000). DeskTOP, a system based on virtual spaces to support and to promote collaborative learning. In Proceedings of the third international conference on Collaborative virtual environments (pp. 199-200). Pountney, R., Parr, S., & Whittaker, V. (2002). Communal Constructivism and Networked Learning: Reflections on a Case Study. In Proceedings of Networked Learning 2002, Berlin. Sliedrecht, the Netherlands: NAISO Academic Press. Köllinger, P. (2002). Report E-Learning in deutschen Unternehmen: Fallstudien, Konzepte, Implementierung (1. Aufl.). Düsseldorf: Symposion. Powell, G. C. (2000). Are you ready for WBT? Wayne State University. Retrieved June 24, 2008, from http:// it.coe.uga.edu/itforum/paper39/paper39.html Pozzi, F., & Persico, D. (2006, June 14-17). Evaluation in CSCL: Tracking and analysing the learning community. In A. Szücs & I. Bø (Eds.), E-Competences for Life, Employment and Innovation: Proceedings of the EDEN 2006 Annual Conference, Vienna (pp. 502-507). Pozzi, F., Manca, S., Persico, D., & Sarti, L. (2007). A general framework for tracking and analysing learning processes in CSCL environments. Innovations in Education & Teaching International (IETI) Journal, 44(2), 169-179. Prasolova-Førland, E., & Divitini, M. (2003). Collaborative virtual environments for supporting learning communities an experience of use. In Proceedings of the 2003 international ACM SIGGROUP conference on supporting group work (pp. 58-67). Prensky, M. (2001). Digital Natives, Digital Immigrants. Retrieved on April 13, 2007 from http://www.marcprensky.com/writing/Prensky -Digital Natives, Digital

368

R. Hertzlazarowitz & N. Miller (Eds.), Interaction in cooperative groups: The theoretical anatomy of group learning (pp. 174-199). Cambridge University Press. Rada, R., Mili, H., Bicknell, E., & Blettner, M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on Systems, Man, and Cybernetics, 19(1), 17–30. doi:10.1109/21.24528 Redecker, C. (2008). Review of Learning 2.0, Practices, Deliverable 2 of the study: Learning 2.0 The Impact of Web 2.0 Innovations on Education and Training in Europe. Draft Working Paper, IPTS – IS Unit. Retrieved from http://is.jrc.ec.europa.eu/pages/documents/Learning20Review.pdf Redfern, S., & Galway, N. (2002). Collaborative Virtual Environments to Support Communication and Community in Internet-Based Distance Education. [JITE]. Journal of Information Technology Education, 1(3), 201–211. Reeves, T. C. (1997). A model of the effective dimensions of interactive learning on the World Wide Web. University of Georgia. Retrieved June 24, 2008, from http://it.coe. uga.edu/~treeves/WebPaper.pdf Reeves, T., & Reeves, M. (1997). Effective dimensions of interactive learning on the World Wide Web. In B. Khan (Ed.), Web-based instruction (pp. 59–66). Englewood Cliffs, NJ: Educational Technology Publications. Reichel, M., & Schelhowe, H. (2008). Between “Instructions” and “Diy”; Tagging in Learning Communities. Herczeg, Michael; Kindsmüller, Martin Christof (Hrsg): Mensch&Computer 2008. München: Oldenbourg 2008, p.307-315. Reigeluth, C. M. (1997a). Educational standards: to standardize or to customize learning? Phi Delta Kappan, 79, 202–206.

Compilation of References

Reigeluth, C. M. (1997b). Instructional theory, practitioner needs, and new directions: some reflections. Educational Technology, 37, 42–47. Reigeluth, C. M. (1999). What is instructional design theory and how is it changing? In C. M. Reigeluth (Ed.), Instructional design theories and models: A new paradigm of instructional theory (Vol. 2, pp. 5-29). Hillsdale, NJ: Lawrence Erlbaum Associates. Reinemeyer, E. (1999). Edward Lee Thorndike. Psychology History. Retrieved June 24, 2008, from http://carbon. cudenver.edu/~mryder/itc_data/idmodels.html Reinking, D. (1997). Me and my hipertext:) a multiple digression analysis of technology and literacy (sic). The Reading Teacher, 50, 626–643. Reinmann, G. (2008). Lehren als Wissensarbeit? Persönliches Wissensmanagement mit Weblogs. Information Wissenschaft & Praxis, 59, 9–57. Reinmann, G. (Ed.). (2007). E-Learning und Wissensmanagement. Zeitschrift Reinmann, G., & Bianco, T. (2008). Knowledge Blogs zwischen Kompetenz, Autonomie und sozialer Eingebundenheit, from Institut für Medien und Bildungstechnologie: http://www.imb-uni-augsburg.de/medienp-dagogik/ biblio/knowledge-blogs-zwischen-kompetenz-autonomie-und-sozialer-eingebundenheit-0 Renner, P. (1997). The art of teaching adults: How to become an exceptional instructor and facilitator. Vancouver, Canada: The Training Associates. Resnick, M. (2002). Rethinking learning in the digital age. In G. S. Kirkman, P. K. Cornelius, J. D. Sachs & K. Schwab (Eds.), Global information technology report 2001 - 2002. Readiness for the networked world (pp. 32-37). New York: Oxford Univ. Press. Resnik, P. (1995). Semantic similarity in a taxonomy: An information-based measure and its applications to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 11, 95–130. Richardson, L., & Ruby, S. (2007). A Service Implementation. In RESTful Web Services. Sebastopol, CA: O’Reilly.

Richter, A., & Koch, M. (2007). Social Software – Status quo und Zukunft, Technischer Bericht Nr. 2007-01, Fakultät für Informatik, Universität der Bundeswehr München, Retrieved on 01 June, 2007 from http://www. unibw.de/wow5_3/forschung/social_software/ Ricketts, J., Wolfe, F. H., Norvelle, E., & Carpenter, E. H. (2000). Multimedia: Asynchronous distributed educationA review and case study. Social Science Computer Review, 18(2), 132–146. doi:10.1177/089443930001800203 Rieber, L. P. (1992). Computer-based microwords: A bridge between constructivism and direct instruction. Educational Technology, Research, and Development. Retrieved June 24, 2008, from http://www.sll.stanford. edu/projects/Wallenberg/ateam/abstracts/Rieber.html Riel, M. (1993). The SCANS report and the AT&T learning network: Preparing students for their future. Telecommunications in Education News, 5(1), 10–13. Rinderle, S., Reichert, M., & Dadam, P. (2004). Flexible support of team processes by adaptive workflow systems. Distributed and Parallel Databases, 16, 91-116. Ritter, S. (1997, August 18-22). PAT Online: A modeltracing tutor on the world-wide web. At the Workshop on Intelligent Educational Systems on the WWW, Kobe Japan. Rivoltella, P. C. (2003). Scuole in Rete e Reti di Scuole. Milan: RCS libri. Robinson, M., Pekkola, S., Korhonen, J., Hujala, S., Toivonen, T., & Saarinen, M.-J. (2001). Extending the Limits of Collaborative Virtual Environments. In E. Churchill, D. Snowdon, & A. Munro (Eds.), Collaborative Virtual Environments: Digital Places and Spaces for Interaction. Berlin: Springer-Verlag. Rogoff, B., & Wertsch, J. (1984). Children’s Learning in the Zone of Proximal Development. San Francisco: Jossey-Bass. Romano, A., Elia, V., & Passiante, G. (2001). Creating Business Innovation Leadership. An Ongoing Experiment: The e-Business Management School at ISUFI. Edizioni Scientifiche Italiane, Naples.

369

Compilation of References

Rosemberg, M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age. New York: McGraw-Hill. Rossi, M., & Sarti, L. (2004). Playing touch and tender: Tutoring strategies in a university master. Information, Savoirs, Décisions & Médiations: Information Sciences for Decision Making, 18. Retrieved January 14, 2008, from http://isdm.univ-tln.fr/PDF/isdm18/56-sarti-rossi.pdf Roussos, M., Johnson, A. E., Leigh, J., Vasilakis, C. A., Barnes, C. R., & Moher, T. G. (1997). NICE: combining constructionism, narrative and collaboration in a virtual learning environment. ACM SIGGRAPH Computer Graphics, 31(3), 62–63. doi:10.1145/262171.262264 Rudner, L. M. (1998). An online, interactive, Computer Adaptive Testing Tutorial. 11/98. Retrieved from http:// EdRes.org/scripts/cat Russell, T. L. (1999). The no significance difference phenomenon. Office of Instructional Telecommunications, Chapel Hill, NC: North Carolina State University Press. Russell, T. L. (2001). The No Significant Difference Phenomenon: A Comparative Research Annotated Biography on Technology for Distance Education. Book available in print from The International Distance Education Certification Center (IDECC), Retrieved December 30, 2008, from https://www.arello.org/store/default.cfm? prodlistid=16-17. Salmon, G. (2003). eTivities: The key to active online learning. Kogan Page Limited. Salmon, G. (2004). E-moderating: The key to online teaching and learning. Routledge. Salomon, G., & Perkins, D. N. (1998). Individual and social aspects of learning. In P.D.Pearson & A. Iran-Nejad (Eds.), American Educational Research Association, 2(3), 1–24. Salovaara, A., & Oulasvirta, A. (2004). Six modes of proactiveresourcemanagement:Auser-centrictypology for proactive behaviours. In Proceedings of the NordicConferenceonHuman-ComputerInteraction

370

(ACM InternationalConferenceProceedingsSeries, Vol. 82, pp. 57-60). Sambamurthy, V., & Zmud, R. (1999). Arrangements for information technology governance: A theory of multiple contingencies. MIS Quarterly, 23(2), 261–290. doi:10.2307/249754 Sancho, J. M. (1998). Para uma tecnologia educacional (B. A. Neves, Trans.). Porto Alegre: ArtMed. Santhanam, R., Sasidharan, S., & Webster, J. (2008). Using self-regulatory learning to enhance e-learning-based information technology training. Information Systems Research, 19(1), 26–49. doi:10.1287/isre.1070.0141 Savery, J. R., & Duffy, T. M. (1995). Problem based learning: An instructional model and its constructivist framework. Educational Technology, 35(5), 31-38. Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. The Journal of the Learning Sciences, 3(3), 265-283. Schaaff, A., & Gray, N. (2008). REST in the VO. Retrieved January 9, 2009, from http://www.ivoa.net/ internal/IVOA/InterOpOct2008GridAndWebServices/ GWS-REST-28October08.pdf Scheloske, M. (2008). Die populärsten wissenschaftlichen Blogs. Retrieved January 5, 2009, from http://www.wissenswerkstatt.net/2008/03/19/die-populaersten-wissenschaftlichen-blogs-wissenschaftsblog-charts-032008/ Schlais, D., & Davis, R. (2001). Distance learning through educational networks: The global new experience. In J. Stephenson (Ed.), Teaching and Learning Online: Pedagogies for New Technologies (pp. 112-126), London: Kogan Page. Schmidt, K. (2002). The Problem with ‘Awareness’: Introductory Remarks on ‘Awareness in CSCW’. Computer Supported Cooperative Work, 11(3), 285–298. doi:10.1023/A:1021272909573 Schneider, D. (1996). Virtual Environments for Education, Research and Life. WWW5 workshop on Virtual Environments and the WWW. Retrieved October 7, 2007 from http://tecfa.unige.ch/moo/paris96/papers/ daniel.html

Compilation of References

Schneider, U. (2004). Wie funktionieren Communities of Practice. In R. Reinhardt (Ed.), Wissenskommunikation in Organisationen. Methoden, Instrumente, Theorienmit 15 Tabellen (pp. 157-156). Berlin: Springer. Schulmeister, R. (2004). Diversität von Studierenden und die Konsequenzen für eLearning. In D. Carstensen & B. Barrios (Eds.), Kommen die digitalen Medien in die Jahre? Medien in der Wissenschaft 29 (p. 133-144). Münster: Waxmann. Schulmeister, R. (2005). Lernplattformen für das virtuelle Lernen: Evaluation und Didaktik (2. Aufl.). München: Oldenbourg. Schulmeister, R. (2008). (Manuscript submitted for publication). Gibt es eine »Net Generation«? [from Universität Hamburg, Zentrum für Hochschul- und Weiterbildung: http://www.zhw.uni-hamburg.de/pdfs/ Schulmeister_Netzgeneration.pdf]. Work (Reading, Mass.). Retrieved June 1, 2008.

Seybert, H. (2007). Gender differences in the use of computers and the Internet. Retrieved January 15, 2008, from http://epp.eurostat.ec.europa.eu/portal/page?_ pageid=1073,46587259&_dad=portal&_ schema=PORTAL&p_product_code=KS-SF-07-119 Shafarenko, A., & Barsky, D. (2000). A secure examination system with multi-mode input on the World-Wide Web. In Proceedings of the IEEE International Workshop on Advanced Learning Technology: Design and Development Issues (IWALT 2000), Palmerston North, New Zealand (pp. 97-100). Shaffer, C., & Anundsen, K. (1993). Creating community anywhere. New York: Jeremy P. Tarcher, Perigee Books. Sharma, P. & Maleyeff, J. (2003). Internet Education: Potential Problems and Solutions, v17 n1 2003, 19–25

Scribner, S. (1985). Vygotsky’s uses of history. In J.V. Wertsch (Ed.), Culture, Communication, and Cognition: Vygotskian Perspectives (pp. 119-145). Cambridge, UK: Cambridge University Press.

Sharma, P., & Fiedler, S. (2004). Introducing technologies and practices for supporting self-organized learning in a hybrid environment. In K. Tochterman & H. Maurer (Eds.), Proceedings of I-Know ‘04, Proceedings of IKnow ‘04 – Graz June 30 - July 2 2004. Know-Center Austria (pp. 543-550).

Secundo, G., & Passiante, G. (2007). An innovative approach to creating business leaders: evidence from a case study. Int. Journal of Management Education, 1(3), 214–230.

Sharpe, R., Benfield, G., Robers, G., & Francis, R. (2006). The undergraduate experience of blended e-learning: A review of UK literature and practice. The Higher Education Academy.

Seely-Brown, J., Collins, A., & Duguid, P. (1995). Situated cognition and the culture of learning.

Sharples, M. (2000). The Design of Personal Mobile Technologies for Lifelong Learning. Computers & Education, 34, 177–193. doi:10.1016/S0360-1315(99)00044-5

Semantic orchestrated interactions in learning technolth ogy. (2006). In Proceedings of the 9 International Conference on Interactive Computer Aided Learning, Villach, Austria. Seufert, S. (2007). Ne(x)t Generation Learning: Was gibt es Neues über das Lernen? In S. Seufert & D. Euler (Eds.) Ne(x)t Generation Learning: Wikis, Blogs, Mediacasts & Co. - Social Software und Personal Broadcasting auf der Spur, SCIL Arbeitsbericht 12, Universität St. Gallen. (pp. 2-19).

Sheard, J. (2004). Electronic learning communities: Strategies for establishment and management. ACM SIGCSE Bulletin, 36(3), 37-41. Shen, Q., Chung, J. K., Challis, D., & Cheung, R. C. T. (2007)... Computer Applications in Engineering Education, 15(1), 30–40. doi:10.1002/cae.20092 Shepherd, E. (2003, October 20). Delivering computerized assessments safely and securely. The e-Learning Developers’ Journal, pp. 1-9.

371

Compilation of References

Sherry, L. (1996). Issues in distance learning. International Journal of Distance Education, 1(4), 337–365. Shirky, C. (2003). Social software and the politics of groups. Retrieved on May 3, 2007 from http://shirky. com/writings/group_politics.html Shum, S., & MacLean, A. Forder, J. & Hammond, N. (1993). Summarising the Evolution of Design Concepts within a Design Rationale Framework. In Adjunct Proceedings of InterCHI’93: ACM/IFIP Conference on Human Factors in Computing Systems, April 24-29, Amsterdam (pp. 43-44). Sicilia, M. A., & Lytras, M. (2005). Scenario-oriented reusable learning object characterizations. Sicilia, M.A., Pagés, C., García, E., Sánchez-Alonso, S., & Rius, A. (2004). Specifying semantic conformance profiles in reusable learning metadata. In Proceedings th of the 5 International Conference on Information Technology Based Higher Education and Training, Istanbul, Turkey. Siemens, G. (2005). Connectivism: Learning as NetworkCreation. Retrieved May 16, 2008, from http://www. elearnspace.org/Articles/networks.htm Siemens, G. (2006). Knowing Knowledge. Retrieved May 16, 2008, from http://www.elearnspace.org/KnowingKnowledge_LowRes.pdf Sim, Y. W., Wang, C., Carr, L. A., Davis, H. C., Gilbert, L., Grange, S., et al. (2005). A Web/Grid Services Approach for a Virtual Research Environment Implementation. Paper presented at the Fourth e-Science All Hands Meeting (AHM 2005), Nottingham, UK. Sims, R. (1997). Interactivity: A forgotten art? Retrieved July 2007 from http://www.gsu.edu/~wwwitr/ docs/ interact/ Sincich, T. (1995). Business statistics by example. Prentice Hall. Singhal, S., & Zyda, M. (1999). Networked Virtual Environments: Design and Implementation. ACM Press.

372

Skowron, A., & Rauszer, C. (1992). The discernibility matrices and functions in information systems. In R. Slowinski (Ed.), Intelligent Decision Support –Handbook of Applications and Advances of the Rough Sets Theory (pp. 331-362). Kluwer Academic Publishers. Slavin, R. E. (1992). When and why does cooperative learning increase achievement? Theoretical and empirical perspectives. In R. Hertzlazarowitz & N. Miller(Eds.), Interaction in cooperative groups: The theoretical anatomy of group learning (pp.145-173). Cambridge University Press. Smith, M. K. (2007). Learning in organizations. Retrieved May 31, 2007, from http://www.infed. org/biblio/ organizational-learning.htm Snow, R. E., & Yalow, E. (1988). Education and Intelligence. In R. J. Sternberg (Ed.), Handbook of human Intelligence (pp. 493-585). New York: Cambridge University Press. Solé, I. (1996). Estratégias de comprensión de la lectura. Lectura Y Vida, 17(4), 5–22. Solvie, P. A. (2008). Encouraging Preservice Teachers’ Construction of Knowledge in Reading Methods Courses. The Journal of Literacy and Technology, 9(2), 57–99. Sorohan, E. G. (1993). We do; therefore we learn. Training & Development, 47(10), 47–55. Speier, C., Valacich, J. S., & Vessey, I. (1997). The effects of task interruption and information presentation on individual decision-making. In Proc. 18th Int. Conf. on Information Systems, Atlanta, GA (pp. 21-36). Spellmann, P., Mosier, J., Deus, L., & Carlson, J. (1997). Collaborative Virtual Workspace. In Proc. of GROUP’97, Phoenix Arizona, ACM (pp. 197-203). Spielberger, C. D., Gorsuch, R. L., & Lushene, R. E. (1970). Manual for the state-trait anxiety inventory (STAI). Palo Alto, CA: Consulting Psychologists. Spira, J. B., & Feintuch, J. B. (2005). The Cost of Not Paying Attention: How Interruptions Impact Knowledge Worker Productivity. New York: Basex.

Compilation of References

Stacey, E. (2002). Social presence online: Networking learners at a distance. Education and Information Technologies, 7(4), 287-294.

Tan, Q., Li, X., & Shi, Zh. (2002). A method of providing individual service in search engine. Computer Science, 29, 23–25.

Steele, G. A. (2008). Using online technology to encourage more student participation in a communication theory course. In Proceedings of the 2nd International MultiConference on Society, Cybernetics and Informatics (Vol. III, pp. 214-219), Orlando, Florida, June 29 - July 2.

Taplin, M. (2000). Problem-based learning in distance education: Practitioners’ beliefs about an action learning project. Distance Education, 21(2), 284-307.

Steinemann, M.-A., Zimmerli, S., Jampen, T., & Braun, T. (2002, May 20-22). Global architecture and partial prototype implementation for enhanced remote courses. In Proceedings of Computers and Advanced Technology in Education (CATE 2002), Cancun, Mexico (pp. 441-446). Stephen, A. (2004). Introduction to BPM. IBM Corporation. Sura, P. K., & Mukkamala, R. (2003, June 23-26). A PKI architecture for academic institutions: Design and prototype. In Proceedings of the International Conference on Security and Management (SAM’03), Las Vegas, NV (Vol. 1, pp. 205-212). Swan, J., Scarbrough, H., & Robertson, M. (2002). The construction of “communities of practice” in the management of innovation. Management Learning, 33(4), 477–496. doi:10.1177/1350507602334005 Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(29), 257–285. Sweller, J., Van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296. Szyperski, C. (2002). Object and component “wiring” standards. In Component Software - Beyond ObjectOriented Programming (2 ed., Vol. 1, pp. 229). New York: ACM Press. Tait, P., & Vessey, I. (1988). The effect of user involvement on system success: A contingency approach. MIS Quarterly, 12(1), 90–107. doi:10.2307/248809

Téllez, J. A. (2005). La comprensión de los textos escritos y la psicología cognitiva.: más allá del procesamiento de la información. Madrid: Dykinson. Tennenhouse,D.(2000).Proactivecomputing. Communications of the ACM, 43(5), 43-50. Tessmer, M. (1993). Panning and Conducting Formative Evaluations. Kogan Page Limited. Thach, E. C., & Murphy, K. L. (1995). Competencies for distance education professionals. Educational Technology Research and Development, 43(1), 57–79. doi:10.1007/ BF02300482 Thijssen, T., & Vernooij, F. (2002). Breaking the Boundaries Between Academic Degrees and Lifelong Learning. In T. A. Johannessen (Ed.), Educational innovation in economics and business: Vol. 6. Teaching today - the knowledge of tomorrow (pp. 137-156). Dordrecht: Kluwer Acad. Thompson, M. (2005). Structural and epistemic parameters in communities of practice. Organization Science, 16(2), 151–164. doi:10.1287/orsc.1050.0120 Thorley, L., & Gregory, R. (1994). Using group-based learning in higher education. London: Kogan Page. Thorndike, E. L. (1911). Animal intelligence. Macmillan. Thorne, S. & Kahn, J. (2003). O.K.I.™ Architectural Concepts. Thorpe, M. (2005). The Impact of ICT on Lifelong Learning. In C. McIntosh & Z. Varoglou (Eds.), Perspectives on Distance Education: Lifelong Learning and Distance Higher Education (pp. 23-32). Paris: Unesco. Thropp, S. E., & Dodds, P. (Eds.). (2006). Sharable Content Object Reference Model (SCORM)® 2004 3rd Edi-

373

Compilation of References

tion Sharable Content Object Reference Model, Content aggregation Model (3rd ed.). Alexandria, VA: ADL. Torniai, C., Jovanovic, J., Gasevic, D., Batemen, S., & Hatala, M. (2008, August 4-7). Leveraging Folksonomies for Ontology Evolution in E-learning Environments. In Proceedings of Second IEEE International Conference on Semantic Computing. Santa Clara, CA, USA. Tornincasa, S. (2001). Web3D Technology applications for distance training and learning: the Leonardo project WEBD. Paper presented at the XII International Conference on Design Tools and, methods in industrial engineering, Rimini. Trajkova, J., & Gauch, S. (2004). Improving ontologybased user profiles. In Proceedings of the RIAO, Vaucluse, France (pp. 380-389). Trentin, G. (2003). Dalla formazione a distanza all’apprendimento in rete. Milan: Franco Angeli. Trentin, G. (2004). Apprendimento in rete e condivisione delle conoscenze. Milan: Franco Angeli. Triantafillou, E., Georgiadou, E., & Economides, A. (2008). The Design and Evaluation of a Computerized Adaptive Test on Mobile Devices. Computers & Education, 50(4), 1319–1330. doi:10.1016/j.compedu.2006.12.005 Tu, C.-H. (2002). The measurement of social presence in an online learning environment. International Journal on E-Learning, 1(2), 34-45. Tucker, S. (2001). Distance education: Better, worse, or as good as traditional education? Online Journal of Distance Learning Administration, 4. Retrieved December 30, 2008, from http://www.westga.edu/~distance/ojdla/ winter44/ tucker44.html. Tuckman, B. W. (1965). Developmental sequence in small groups. Psychological Bulletin, 63, 384-399. Tully, C. J., & Zerle, C. (2005). Handys und jugendliche Alltagswelt. Medien + Erziehung: Merz. Zeitschrift für Medienpädagogik, 49(3), 11–16.

374

Ullrich, C. (2004, November 7-11). Description of an instructional ontology and its application in web services for education. SWEL Workshop, ISWC (pp. 17-23). Hiroshima, Japan. UNESCO, United Nations Educational, Scientific and Cultural Organization, División de Educación Superior. (2004). Las tecnologías de la información y la comunicación en la formación docente Guía de planificación. Montevideo:Trilcef UNESCO. (2005). Information and Communication Technologies in Schools: A Handbook for Teachers or How ICT Can Create New, Open Learning Environments. France: ED/HED/TED/2. Retrieved December 30, 2008, from http://unesdoc.unesco.org/images/0013/001390/139028e. pdf Urban-Lurain, M., & Weinshank, D. (2000). Computing concepts and competencies. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 73-74). Bolton, MA: Anker Publishing Company Inc. Ury, G. (2004). A comparison of undergraduate student performance in online and traditional courses. Journal of Computing Sciences in Colleges, 19(4), 99–107. van der Linden, W. J., & Glas, C. A. W. (2003). Preface. In van der Linden, W.J., Glas, C.A.W (Eds), Computerised Adaptive Testing: Theory and Practice. Boston: Kluwer Academic Publishers. Van Gerven, P. W. M., Paas, F., Van Merrienboer, J. J. G., & Schmidt, H. G. (2006). Modality and variability as factors in training the elderly. Applied Cognitive Psychology, 20(3), 311-320. van Schaik, P., Barker, P., & Beckstrand, S. (2003). A comparison of on-campus and online course delivery methods in Southern Nevada. Innovations in Education and Teaching International, 40(1), 5–15. doi:10.1080/1355800032000038859 van Schaik, P., Barker, P., & Moukadem, I. (2005). Using a digital campus to support electronic learning in Lebanon. Innovations in Education and Teaching International, 42(2), 157–166. doi:10.1080/14703290500062532

Compilation of References

VanBruggen, J. M., Boshuizen, H. P. A., & Kirschner, P. A. (2003). A cognitive framework for cooperative problem solving with argument visualization. In P. A. Kirschner, S. J. Buckingham Shum, & C. S. Carr (Eds.), Visualizing argumentation: Software tools for collaborative and educational sense-making (pp. 25-47). London: Springer. VanMerrienboer, J. J. G., & Ayres, P. (2005). Research on cognitive load theory and its design implications fore-learning. Educational Technology Research and Development, 53(3), 5-13. Veerman, A. L., Andriessen, J. E. B., & Kanselaar, G. (1999). Collaborative learning through computermediated argumentation. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. doi:10.1287/mnsc.46.2.186.11926 Venkatesh, V., & Johnson, P. (2002). Telecommuting technology implementations: A within- and betweensubjects longitudinal field study. Personnel Psychology, 55(3), 661–687. doi:10.1111/j.1744-6570.2002.tb00125.x Venkatesh, V., & Speier, C. (1999). Computer technology training in the workplace: A longitudinal investigation of the effect of mood. Organizational Behavior and Human Decision Processes, 79(1), 1–28. doi:10.1006/ obhd.1999.2837 Venkatesh, V., & Speier, C. (2000). Creating an effective training environment for enhancing telework. International Journal of Human-Computer Studies, 52, 991–1005. doi:10.1006/ijhc.1999.0367 Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 483–502. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Vermetten, J., Lodewijks, G., & Vermunt, D. (2001). The role of personality traits and goal orientations in strategy use. Contemporary Educational Psychology, 26, 150–152. doi:10.1006/ceps.1999.1042 Vigilante, R. (2000). Systems analysis: A graduate course. In D.G. Brown (Ed.), Interactive learning: Vignettes from America’s most wired campus (pp. 75-77). Bolton, MA: Anker Publishing Company Inc. Vogten, H., Martens, H., Nadolski, R., Tattersall, C., Van Rosmalen, P., & Koper, R. (2006). Integrating IMS learning design and IMS question and test interoperability using Copper Coreservice integration. In Proceedings of International Workshop in Learning Networks in Lifelong Competence Development, TEN Competence Conference, Sophia, Bulgaria. Vygotsky, L. (1935). Thougth and languages. Retrieved June 24, 2008, from http://marxists.org/archive/vygotsky/ works/words/vygotsky.htm Vygotsky, L. S. (Ed.). (1987). The Collected Works of Vygotsky. New York: Plenum Press. Wagner, C., & Bolloju, N. (2005). Supporting Knowledge Management in Organizations with Conversational Technologies: Discussion Forums, Weblogs, and Wikis. Journal of Database Management, 16(2), 1–8. Wainer, H. (1990). Computerized Adaptive Testing (A Primer). New Jersey: Lawrence Erlbaum Associates. Waits, T., & Lewis, L. (2003). Distance education at degree-granting postsecondary institutions: 20002001 (NCES 2003-017), U.S. Department of Education, National Center for Education Statistics. Retrieved December 30, 2008, from http://nces.ed.gov/pubsearch/ pubsinfo.asp?pubid=2003017. Wang, X. (2007). What factors promote sustained online discussions and collaborative learning in a Web-based course? International Journal of Web-Based Learning and Teaching Technologies, 2(1), 17–38. Wang, Y., Peng, H., Huang, R., Hou, Y., & Wang, J. (2008). Characteristics of distance learners: Research on relationships of learning motivation, learning strategy, self-

375

Compilation of References

efficacy, attribution and learning results. Open Learning, 23(1), 17–28. doi:10.1080/02680510701815277

Wenden, A. (1991). Learner strategies for learner automomy. New York: Prentice Hall.

Warren, M., & Hutchinson, W. (2003). Information security: An e-learning problem. In Proceedings of the Second International Conference on Advances in WebBased Learning (ICWL 2003), Melbourne, Australia (LNCS 2783, pp. 21-26).

Wenger, E. C., & Snyder, W. M. (2000, Jan-Feb). Communities of practice: The organizational frontier. Harvard Business Review, 139–145.

Waschull, S. B. (2001). The online delivery of psychology courses: Attrition, performance, and evaluation. Teaching of Psychology, 28(2), 143–147. doi:10.1207/ S15328023TOP2802_15 Watson, J. B. (1929). Behaviorism: The modern note in psychology. Retrieved June 24, 2008, from http://psychclassics.yorku.ca/Watson/Battle/watson.htm Webassessor (2009). Retrieved from http://www.webassessor.com Weber, G., Kuhl, H.-C., & Weibelzahl, S. (2001). Developing adaptive internet based courses with the authoring system Netcoach. In Proceedings of third workshop on adaptive hypertext and hypermedia, UM, TU/e Computing Science Report 01/11. Webster, J., & Hackley, P. (1997). Teaching effectiveness in technology-mediated distance learning. Academy of Management Journal, 40(6), 1282–1309. doi:10.2307/257034 Weil, S., Hussain, T., Brunye, T., Sidman, J., & Spahr, L. (2005). The use of massive multi-player gaming technology for military training: A preliminary evaluation. Paper presented at the 49th Annual Meeting of the Human Factors and Ergonomics Society (Sept 26-30, Orlando, FL). Weintraub, R. (1995). Transforming mental models through formal and informal learning: a guide for workplace educators. In S. Chawla, & J. Renesch (Eds.), Learning Organizations: Developing Cultures for Tomorrow’s Workplace (pp. 417-429). Portland, OR: Productivity Press. Wen, Q., & Wang, L. (2004). Effects of various factors on L2 learning strategies: a review. Journal of Foreign Language and Teaching.

376

Wenger, E., McDermott, R., & Snyder, W. M. (2002). Cultivating communities of practice. Boston: Harvard Business School Press. Westera, W., & Sloep, P. (1998). The virtual company: Towards a self-directed, competence-based learning environment. Educational Technology, 38(1), 32-38. Westera, W., Sloep, P. B., & Gerrissen, J. (2000). The design of the virtual company: Synergism of learning and working in a networked environment. Innovations in Education and Training International, 37, 24-33. Weston, T. J. (2005). Why faculty did—and did not— integrate instructional software in their undergraduate classrooms. Innovative Higher Education, 30(2), 99115. Wheeler, S., Yeomans, P., & Wheeler, D. (2008). The good, the bad and the wiki: Evaluating student-generated content for collaborative learning. British Journal of Educational Technology, 39(6), 987–995. doi:10.1111/j.14678535.2007.00799.x White, I. K., & Pagano, R. (2007). Making It Stick: The use of Online Discussion Fora to Support Continuing Professional Development in Higher Education Communities of Practice. In Proceedings of the 2nd International Conference on e-Learning, New York, 28-29 June. Wiley, D. A., & Wiley, D. A. (Eds.). (2002). Connecting Learning Objects to Instructional Design Theory: A Definition, a Metaphor and a Taxonomy. Bloominton: Agency for Instructional Technology and Association for Educational Communications & Technology. Wilhelm, P., & Wilde, R. (2005). Developing a university course for online delivery based on learning objects: From ideals to compromise. Open Learning, 20(1), 65–81. doi:10.1080/0268051042000322104

Compilation of References

Wilhelmsen, S., Stein, I. Å., & Øyvind, M. (1998). Psychological theories: A brief survey of the changing views of learning. Retrieved June 24, 2008, from http://www. uib.no/People/sinia/CSCL/Web_struktur-4.htm Williams, J. (2001). The Business Case for Components. In G. T. Heineman & W. T. Councill (Eds.), ComponentBased Software Engineering: Putting the Pieces Together (1st ed., Vol. 1, pp. 79-97). Addison-Wesley. Williams, J. B. (2004). Exploring the use of blogs as learning spaces in the higher education sector. Australasian Journal of Educational Technology, 20(2), 232–247. Wilson, B. (1990). Student’s assessment of distance learning. Paper presented at the Annual Meeting of the Midsouth Educational Researcher Association, New Orleans, Louisiana, November. ERIC ED 326 558. Wilson, B., Jonassen, D., & Cole, P. (1993). Cognitive approaches to instructional design. University of Colorado at Denver. Retrieved June 24, 2008, from http://carbon. cudenver.edu/~bwilson/training.html Winn, W., & Jackson, R. (1999). Fourteen Propositions About Educational Uses of Virtual Reality. Educational Technology, 39(4), 5–14. Wise, S. L. (1997). Overview of practical issues in a CAT program. Paper presented at the annual meeting of the National Council on Measurement in Education, Chicago IL. World Wide Web Consortium (W3C). (2006). Re-Zhou, J. (2001). Non-repudiation in electronic comquirements for WCAG 2.0 (W3C Working Group merce. Norwood, MA: Artech House Publishers. Note). Wu, X. (2005). Study on dependency relation between learners’ characteristics and learning strategies in network learning. Unpublished master’s thesis, Xi’an Jiaotong University, Xi’an, Shaanxi, P.R.China. Wu, Z., & Palmer, M. (1994, June 27-30). Verb semantics and lexical selection. In thirty second Annual Meeting of the Associations for Computational Linguistics (pp. 133-138). New Mexico State University, Las Cruces, New Mexico, USA.

PGP. (n.d.). The International PGP Home Page. Retrieved from http://www.pgpi.org SimTeach. (2006). Information and community for educators using multi-user virtual environments. Retrieved from http://www.simteach.com/wiki/index. php?title=Top_20_Educational_Locations_in_Second_Life Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education, 39, 361-377. Yang, H., & Wang, L. (2003). An algorithm for extraction individual character of learner in the personalized learning system based on network environment. Computer Engineering and Application, 25, 179. Yessad, A., & Laskri, M. T. (2006, December 7-9). Ontology-driven dynamic course generation for webbased education. In Proceedings of MCSEAI, Agadir, Morocco. Yessad, A., Faron-Zucker, C., Dieng-Kuntz, R., & Laskri, M. T. (2008). Adaptive Learning Organizer for Web-based Education. International Journal of Web-Based Learning and Teaching Technologies, 3(4), 57–73. Yin, R. (1994). Case study research: Design and methods (2nd ed.). Sage Publications Inc. Yin, R. K. (1994). Case Study Research. Design and Methods. Thousand Oaks, CA: Sage. Yoo, Y., & Alavi, M. (2001). Media and group cohesion: Relative influences on social presence, task participation, and group consensus. MIS Quarterly, 25(3), 371–390. doi:10.2307/3250922 Young, A. (1997). Mentoring, modeling, monitoring, motivating: Response to students’ ungraded writing as academic conversation. In M. D. Sorcinelli & P. Elbow (Eds.), Writing to learn: Strategies for assigning and responding to writing across the disciplines. New Directions for Teaching and Learning, 69.

377

Compilation of References

Young, S. S.-C., & Ku, H. H. (2008). Pioneering the Web enhanced learning between Taiwan and China (WELTaC) initiative in higher education: Potential and issues. Innovations in Education and Teaching International, 45(2), 193–203. doi:10.1080/14703290801950468 Zampunieris, D. (2006). Implementation of a proactive learning management system. In T. Reeves & S. Yamashita (Eds.), Proceedings of e-learn: World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education 2006 (pp. 3145-3151). Chesapeake, VA: AACE.

378

Zhang, W., Wu, W., Liang, J., & Li, D. (2001). Theory and approach of Rough set. Beijing: Science Press, China. Zhong, J., Zhu, H., Li, J., & Yu, Y. (2002, July 15-19). Conceptual graph matching for semantic search. In Proceedings of 10th International Conference on Conceptual Structures, ICCS (LNCS 2393, pp. 92-106). Ziarko, W. (2000). Rough sets: trends, challenges, and prospects. In W. Ziarko & Y. Yao (Eds.), RSCTC 2000 (pp.1-7). Berlin: Springer.

379

About the Contributors

Nikos Karacapilidis holds a Professor position at the University of Patras (field: Management Information Systems). His research interests lie in the areas of Technology-Enhanced Learning, Intelligent Web-Based Information Systems, e-Collaboration, Knowledge Management Systems, Group Decision Support Systems, Computer-Supported Argumentation, Enterprise Information Systems and Semantic Web. He has been appointed as Editor-in-Chief of the Advances in Web-based Learning (AWBL) Book Series, as well as co-Editor-in-Chief of the International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), both published by IGI Global. More detailed information about his publications list, research projects involved and professional activities can be found at http://www.mech.upatras. gr/~nikos/. *** Benjamín Ramos Álvarez received the mathematics MSc degree and the computer science PhD degree from Universidad de Valencia (Spain, 1984) and from Universidad Carlos III de Madrid (Spain, 1999) respectively. Since 1990, he has been working in the computer sciences department at Universidad Carlos III de Madrid, currently as associate professor. Mikael Berglund is a Java programmer, a senior system developer and project manager at Ladok at Umeå University in Sweden. He works with the development of administrative systems for higher education. Reinhard Bernsteiner is a senior researcher at the Institute for Applied Systems Research and Development. His research fields comprise eLearning and innovative IT-based instruments for learning and teaching. He received degrees in Business and Informatics Administration from Johannes Kepler University, Austria. He attended postgraduate studies at the Technical University of Vienna. Christos Bouras obtained his Diploma and PhD from the Department Of Computer Engineering and Informatics of Patras University (Greece). He is currently a Professor in the above department. Also he is a scientific advisor of Research Unit 6 in Research Academic Computer Technology Institute (CTI), Patras, Greece. His research interests include Analysis of Performance of Networking and Computer Systems, Computer Networks and Protocols, Telematics and New Services, QoS and Pricing for Networks and Services, e-Learning Networked Virtual Environments and WWW Issues. He has extended professional experience in Design and Analysis of Networks, Protocols, Telematics and New Services.

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

About the Contributors

He has published 300 papers in various well-known refereed conferences and journals. He is a co-author of 8 books in Greek. He has been a PC member and referee in various international journals and conferences. He has participated in R&D projects such as RACE, ESPRIT, TELEMATICS, EDUCATIONAL MULTIMEDIA, ISPO, EMPLOYMENT, ADAPT, STRIDE, EUROFORM, IST, GROWTH and others. Also he is member of experts in the Greek Research and Technology Network (GRNET), Advisory Committee Member to the World Wide Web Consortium (W3C), IEEE - CS Technical Committee on Learning Technologies, IEEE ComSoc Radio Communications Committee, IASTED Technical Committee on Education WG6.4 Internet Applications Engineering of IFIP, ACM, IEEE, EDEN, AACE, New York Academy of Sciences and Technical Chamber of Greece.. Elaine Brown received her BSc (Hons) biochemistry from the University of Bristol in 1989 (final year project: computer modelling of 3D structure of glycolytic enzyme Pyruvate Kinase). MSc (distinction) computer science in 2001. Current study: MA learning and teaching (eLearning). IT & systems manager in industry. Joined Anglia Ruskin University as senior lecturer in computing in 2003. Teaching courses in system modelling, human-computer interaction. Research interests: representation affordances (models to programs, environments to classroom, avatars to people, and so on), learning and teaching (use of supplementary tools, and innovative methods of assessment & feedback). Antonio Cartelli is researcher in Didactics and Special pedagogy in the University of Cassino, Italy. He teaches Basic computer science and Teaching and learning technologies in the educational courses of the Faculty of Humanities in the University of Cassino. For about four years he has managed the Laboratory for Teaching-Learning Technologies in the Department of Human and social sciences and the Centre for ICT and on line teaching in his Faculty. Among his interests are: misconceptions and mental schemes in human knowledge, the socio-technical approach to ICT use, Information Systems for research and teaching, Web Technologies in teaching research and their everyday use for the improvement of teaching and learning. He took part in several national and international research projects as coordinator of the local unit of Cassino, he also authored more than 130 papers concerning the themes he is interested in and 5 books on the same topics. His most recent international publications are the books “Teaching in the Knowledge Society: New Skills and Instruments for Teachers” (2006) by IGI Global and the “Encyclopedia of Information Communication Technology” (2008) by Information Science Reference. Michael A. Chilton is an assistant professor with the Department of Management at Kansas State University. His research interests include performance measurements of IT personnel, the effects of cognitive preferences on knowledge management and pedagogical issues in information technology. He has published in the Journal of Management Information Systems, the Database for Advances in Information Systems, the Journal of Information Systems Education, the International Journal of Knowledge Management and others. Mário Figueiredo Costa is a researcher at the Brazilian School of Public and Business Administration of Getulio Vargas Foundation and a consultant in the IT industry. He holds a BSc in system analysis from the Pontificial Catholic University of Rio de Janeiro and an MBA from the Getulio Vargas Foundation.

380

About the Contributors

Anastasios A. Economides is an Associate Professor and Chairman of the Information Systems Postgraduate Program at the University of Macedonia, Thessaloniki, Greece. He received the Diploma degree in Electrical Engineering from Aristotle University of Thessaloniki. After receiving a Fulbright and a Greek State Fellowship, he continued for graduate studies at the United States. He received a M.Sc. and a Ph.D. degree in Computer Engineering, from the University of Southern California, Los Angeles. Currently, he is the director of CONTA (Computer Networks and Telematics Applications) laboratory. His research interests are in the area of E-Learning, E-Commerce and Computer Networks. He has published over one hundred fifty peer-reviewed papers. He was the plenary speaker in two International Conferences. Gianluca Elia holds a MSc from the Euro-Mediterranean Incubator in Business Innovation Leadership of Scuola Superiore ISUFI—University of Salento (Italy), where he is the coordinator of the research activities in Learning, Innovation and Value Network. He is also a Researcher at the Faculty of Engineering of the University of Salento. His research is focused on innovative methodologies, strategies and tools to enhance collaborative learning. He’s involved in the management of complex research projects, in collaboration with leading companies, universities and research centers. He had a major role in the design and implementation of the “Virtual eBMS,” a technology platform integrating knowledge management and web learning applications. This platform was awarded the “Brandon Hall Research” prize in learning technology in 2006 (e-mail: [email protected]). Catherine Faron-Zucker is an associate professor at the University of Nice - Sophia Antipolis (UNSA). She carries out her research in the Kewi team at I3S (UMR UNSA - CNRS) and collaborates with the Edelweiss team at INRIA Sophia Antipolis. Her research interests include Knowledge Representation and Reasoning, Ontologies, Semantic Web, and e-Learning. She received her PhD in Computer Science from the University Pierre & Marie Curie in Paris. Ana Isabel González-Tablas Ferreres received the engineer MSc degree from Universidad Politécnica de Madrid (Spain, 1999) and the PhD degree in computer science from Universidad Carlos III de Madrid (Spain, 2005). She has worked as a researcher and university assistant in Universidad Carlos III de Madrid since 1999. Her main research interests are security and privacy for location-based services and digital signature applications. Gérard Fillion is the Accounting Department Chair at the Faculty of Administration of the University of Moncton. He is also a Professor of Management Information Systems. He received his Ph.D. in Management Information Systems from Laval University. His research interests include e-learning, academic teaching pedagogy integrating ICT, IT adoption and diffusion, e-commerce, enterprise resource planning systems, knowledge management, and enhancement of scientific research in organizational sciences. His research has been published in Innovations in Education and Teaching International, Academy of Information and Management Sciences Journal, Academy of Educational Leadership Journal, International Journal of Web-Based Learning and Teaching Technologies, International Journal on ELearning, Management Review: An International Journal, among others, and he presented his research findings at numerous national and international conferences. He also acts as Conference Chair, Division Chair, Session Chair, Discussant, and Reviewer in several national and international conferences, and Associate Editor and Reviewer in scientific journals.

381

About the Contributors

Elena García-Barriocanal obtained a university degree in computer science from the Pontifical University of Salamanca in Madrid (1998) and a PhD from the computer science department of the University of Alcalá. From September 1998 to February 1999 she worked as a lecturer in the computer languages and information systems department of the Pontifical University, and in 1999 she joined the computer science department of University of Alcalá as assistant professor. Starting from 2001, she is associate professor at computer science department of the University of Alcalá and she is a member of the Information Engineering Research Group of this University. Her research interests mainly focus on topics related to the role of knowledge representation in fields like human-computer interaction and learning technologies; concretely she actively works on ontological aspects both in e-learning and in usability and accessibility. She supervises several PhD works in those areas. Elissavet Georgiadou is a researcher in the Center of Educational Technology, Thessaloniki, Greece. Concurrently, she is a lecturer in Economics Department of the University of Macedonia, Thessaloniki, Greece. She studied Graphic Arts and Photography in the Technological Institute of Athens. In 1994 she completed her Masters degree in Image Studies at the University of Kent at Canterbury. Follow that, 1998 she received her PhD on Educational Hypermedia from De Montfort University, UK. She is working on the field of educational hypermedia systems and in particular the design and evaluation of these systems. Her research was presented at a number of international conferences and journals. Eri Giannaka obtained her Diploma from the Informatics Department of the Aristotelian University of Thessaloniki (Greece) and her Master Degree from the Computer Engineering and Informatics Department of Patras University. She is currently a PhD Candidate of the Department of Computer Engineer and Informatics of Patras University. Her interests include Virtual Reality applications, Performance Evaluation of large-scale Virtual Environments, Distributed Virtual Environments, Computer Networks, System Architecture, Internet Applications, Database Implementation and Administration. Marie Gordon recieved her BSc (Hons) computer science from Anglia Ruskin in 2000. Joined Anglia Ruskin in September 2001, senior lecturer since 2004. Teaching courses in Networking and World Wide Web with particular emphasis on free and open source software. Introduction of Linux Lunacy events for University Open Days, providing “Edutainment” for students both current and prospective plus staff. More recently diversifying into the computer gaming and animation technology program (still with a preference for FOSS tools) and introducing Second Life into the main computing/gaming curriculum. A great deal of practical experience and versatile combination of in-world/off-world skills from 18+ months (deep) immersion in SL. Anil Gurung is an assistant professor of business and information management at Neumann College. He received his PhD from the University of Texas at Arlington. Current research interests are in the areas of IT adoption, information security and privacy, ecommerce, and cultural and social aspects of business computing. He has published in various journals and conference proceedings. Thanasis Hadzilacos is a Professor of Information Systems at the Open University of Cyprus where he directs the graduate program in Information Systems. Until September 2007 he was Dean of Science and Technology at the Hellenic Open University where he served as associate professor of software engineering, directed the Open and Distance Laboratory for Educational Material and Educational

382

About the Contributors

Methodology, the graduate course on Information Systems and the undergraduate Computer Science course. Educated at Harvard, USA, he had substantial industrial experience before joining Computer Technology Institute in 1986, where he continues as a researcher with the responsibility of the Educational Technology and the e-Learning Sectors and R&D Unit III “Applied Information Systems”. During 1996-2001 he designed and managed the Greek national project “Odysseia” for the utilization of Information and Communication Technologies in secondary education. He has served as a member of the Council of Europe working group for Teaching and Learning in the Communication Society and the Greek national representative to E.U. DG Education and Culture for building the European portal on educational opportunities. Mike Hobbs gained PhD in computer science from the University of Kent in 1995 (Genetic Algorithms for Spatial Data Analysis in Geographical Information Systems). Senior research assistant for ESRC ‘Analysis of Large and Complex Datasets’ at Essex University in 1994, developing and applying AI techniques to data analysis. Returned to University of Kent as lecturer in applied computing in 1996. Joined Anglia Ruskin University in 2000 as a senior lecturer in computing. Teaching courses in programming languages, artificial intelligence and knowledge management. Researching into applications of AI, knowledge management and more recently involved in learning and teaching with virtual worlds. Luiz Antonio Joia is an associate professor and MBA Head at the Brazilian School of Public and Business Administration of Getulio Vargas Foundation and an adjunct professor at Rio de Janeiro State University. He holds a BSc in civil engineering from the Militar Institute of Engineering, Brazil, and an MSc in civil engineering and a DSc in engineering management from the Federal University of Rio de Janeiro, Brazil. He also holds an MSc in management studies from the Oxford University, U.K. He was a World Bank consultant in educational technology. He is a member of the advisory board of the Journal of Intellectual Capital (Emerald) and Electronic Government (Inderscience). Maria Cristina Rodrigues Azevedo Joly, PhD ([email protected]), PhD in Psychology from the University of São Paulo (USP); works as a professor at the São Francisco University, which is located near São Paulo, Brazil, in a Post Graduate Program of Psychology. She has been orientating master dissertations and doctor’s thesis in psychological evaluation and developing research to develop literacy instruments to evaluate reading comprehension and reading strategies with children, adolescents and university students. She also researches the ICT of teachers and students. Dimitris Kalles was educated in Greece (Diploma) and in the UK (MSc, PhD). He is a researcher and tutor with the Hellenic Open University and the Open University of Cyprus and sits on the board of directors of a software development company. He has also worked as a research manager for a research institute and as an expert evaluator for the European Commission. He has been teaching courses, authoring coursework and supervising diploma theses on AI, complexity, programming and software engineering for several years, at an undergraduate and postgraduate level. He has co-developed several systems and he has published about 40 papers in journals and conferences on the above subjects. Dionysis Karaiskakis was educated in Greece (Diploma and MSc) and is a PhD student in the Hellenic Open University (HOU). He has worked for many years in Research Academic Computer Technology Institute (CTI) and in the HOU in Greece as technical responsible for major IT R&D projects, in

383

About the Contributors

the field of e-learning and network technologies. His research interest focus on Knowledge Discovery and Data Mining on networked communities, in e-learning environments. He has published 3 papers in conferences on the above subjects. Nikos Karousos holds a Diploma (1998) and a M.Sc. (2000) from the Dept. of Computer Engineering and Informatics University of Patras, Greece. He is currently a PhD student in the provision of hypertext services. He works in the E-Learning Sector of the Research Academic Computer Technology Institute, Patras, Greece. His research interests include Hypertext/Hypermedia, Web Services, Software Development, Web based Technologies and Knowledge Management. Andrea Kohlhase holds a diploma in Mathematics from the Technical University Berlin. After graduation she spent ten years in the software industry, but came back to academia via a research programmership at Carnegie Mellon University, Pittsburgh. She has finished her PhD at University Bremen in 2008. Currently she works as a senior research associate at the German Center for Artificial Intelligence (DFKI) and Jacobs University, both Bremen, Germany. Her research interest lies in the field of HumanComputer Interaction, especially Interaction Design with a focus on applications using semantic data. She has developed several software packages offering invasive, semantic applications for MS Office products, e.g., “CPoint” for PowerPoint or a semantic help system called “SACHS” for Excel. Rob Koper is professor in educational technology and director of RTD into learning technologies with the Open University of the Netherlands Educational Technology Expertice Centre. He was, among other things, responsible for the development of the Educational Modelling Language (the predecessor of IMS Learning Design). His research focuses on self-organised distributed learning networks for lifelong learning, including RTD into software agents, educational semantic web, interoperability specifications and standards. A recent book he edited with W. Jochems and J. van Merriënboer is Integrated eLearning (2004, London: RoutledgeFalmer). Thérèse Laferrière is a Professor of Pedagogy at Laval University. She was the leader of the research theme “Educating the Educators” within the TeleLearning Network of Centres of Excellence. Her research activities focus on teacher-student(s) interactions and peer interactions as electronically linked classrooms become reality in elementary and secondary schools as well as in faculties of education and in post-secondary education in general. Mohamed Tayeb Laskri is university professor. He received his Ph.D degree in data processing (France, 1987) and a doctorate of state in data processing (Algeria, 1995). He directs the group of research in artificial intelligence at the LRI laboratory. His present researches focus on artificial intelligence reasoning, image processing, multi-agents systems, Man-Machine engineering interface and automatic processing of natural language. Haifei Li ([email protected]) is an assistant professor in the Department of Computer Science at Union University, Jackson, TN, USA. He received his PhD in Computer Science from the University of Florida, USA. His current research interests include distance learning, scientific computing, and e-business.

384

About the Contributors

Moez Limayem is the Management Information Systems Department Chair at the Sam M. Walton College of Business of the University of Arkansas. He received his Ph.D. in Management Information Systems from the University of Minnesota. His research interests include IT adoption and usage, customer relationship management, knowledge management, and e-commerce. He has published several papers in top journals, such as Management Science, MIS Quarterly and Information Systems Research, and presented his research findings at numerous national and international conferences. He also acts as Conference Chair, Division Chair, Session Chair, Discussant, and Reviewer in several national and international conferences, and Associate Editor and Reviewer in scientific journals. Guangdong Liu is a graduate student in the School of Electronic and Information Engineering at Xi’an Jiaotong University, P.R. China. He received his Bachelor’s degree in Computer Science and Technology from Xi’an Jiaotong University, China. His research interests include massive resources management, index construction. Robert Mantha is the Dean of the Faculty of Administration at Laval University. He is also a Professor of Management Information Systems. He received his Ph.D. in Management Information Systems from the University of Minnesota. His research interests include the analysis and design of information systems, and the methods and tools supporting the design of information systems. His research has been published in several information systems journals, among others, and presented at numerous national and international conferences. Ronei Ximenes Martins, PhD. ([email protected]), Professor and head of the Department for Distance Education at Universidade São Francisco, Brazil, Martins is graduated in Mathematics, specialized in Computer Science, has master degree in Educational Technology and PhD in Psychological Evaluation. His professional background includes classroom teaching and online courses, both in higher education settings. Besides developing computerized assessment and learning management systems. Maristella Matera is assistant professor at Politecnico di Milano, where in 2000 she received a PhD in computer science engineering. She has been awarded several fellowships for supporting her research work at Italian and foreign institutions; in particular, she was visiting researcher at the GVU Center (Georgia Institute of Technology, USA). Her research focuses on design methods and tools for Web applications, with particular emphasis on context-awareness, Web-based e-learning, Web usage mining, usability and accessibility. She is author of about 70 papers and is co-author of the book Designing Data-intensive Web Applications (Morgan Kaufmann publisher, 2002). Agustín Orfila is a senior lecturer at the computer science department of the Universidad Carlos III de Madrid and a member of the Information Security Group of this department. He has a bachelor’s degree in physics from Universidad Complutense de Madrid and he obtained his PhD degree in computer science from Universidad Carlos III de Madrid in 2005. Dr. Orfila has several publications in international conference proceeding and journals. His interests are mainly focused on the security of information technology, particularly on intrusion detection systems. Herwig Ostermann is senior researcher at the Institute for Applied Systems Research and Development. His research interests encompass the field of health politics as well as public administration and

385

About the Contributors

management. He received a Master’s degree in International Business Administration from Innsbruck University, Austria, a Master’s degree in Health Sciences and a PhD in Health Sciences from UMIT, Hall/Tyrol, Austria. Gonzalo Macarro Palazuelos is a MSc in computer science student at the University of Alcalá. Since 2006, Gonzalo is working in the fields of Semantic Web and learning technology within the information engineering research unit at that university. Fredrik Paulsson is an Assistant Professor at the department of Interactive Media and Learning at Umeå University in Sweden, where he is the leader of the Learning Infrastructure Research (LIR) group. His main research interests are system architecture for Virtual Learning Environments and the development and use of Semantic Web technology within TEL. Especially with a focus on achieving a higher level of pedagogical and technical flexibility and adaptability. He is also affiliated to the Knowledge Management Research (KMR) group at KTH. Francesca Pozzi graduated at the University of Genoa in 1996 and in 1998 she started working as researcher at the Institute for Educational Technology – National Council for Research (ITD-CNR). At the moment a PhD in Languages, Culture and ICT is also in progress. As far as her main research interests, from the very beginning of her activity she started focusing on Computer Supported Collaborative Learning (CSCL) with a particular attention to tutor’s roles in these contexts. In 2005 she was responsible for ITD of a European project called “DPULS – Design Patterns for Recording and Analysing Usage of Learning Systems”, within which she proposed a framework for the analysis of interactions and collaboration processes in CSCL. During these last years she has been continuously involved in the design, conduction and evaluation of online collaborative learning experiences within various national and international projects. Milena Reichel holds a PhD in Informatics from University of Bremen, Germany, received in 2008. She was also awarded BSc and MSc in Digital Media from the same department. In 2008, Milena Reichel joined the 3CS group in TSSG, Ireland, where she is currently working as a developer in the Feedhenry project that delivers a widget platform to Ireland’s leading companies such as Eircom or the Munster Express. Her career started as research assistant in the research group Digital Media in Education (DiMeB) headed by Prof. Heidi Schelhowe in the Department of Mathematics and Computer Science, University Bremen, Germany. In the European project EduWear - Children Designing Tangible and Wearable Computing for Playful Educational Purposes - Milena Reichel developed a Smart Textiles construction kit focusing on the gap between the physical and virtual world and the chances of Social Software to provide a seam. Social and tangible computing are the areas Milena Reichel’s research interests center around. Arturo Ribagorda is a telecommunication engineer and PhD in computer science from the Polytechnical University of Madrid (SPAIN). At the moment he is full professor and head of the computer science department of the University Carlos III of Madrid (SPAIN). His research area is the security on information technology, field in which he worked in numerous research projects, national and international. He has published more than 40 papers in national magazines and international journals. He

386

About the Contributors

has been invited chair in numerous conferences and has written three books. In addition, he has been evaluator in European Research Programs (Advanced Transport Telematics and ESPRIT). Àngels Rius is assistant professor of the Department of Computer Science, Multimedia and Telecommunication at the Universitat Oberta de Catalunya (UOC), Barcelona, Spain. Since 1996 she is also part-time assistant professor of the high engineering polytechnic school at the Universitat Politécnica de Catalunya (UPC), Vilanova I la Geltrú , Barcelona, Spain. She received her BSc degree in computer science from the Facultat d’Informàtica de Barcelona (UPC) and her MSc at the UOC University. Recently she is doing her PhD in the Information and knowledge society program at the UOC University. Her research interest concerns the area of e-Learning, particularly in the technological and applied aspect. In order to contribute to the automation of the learning management systems, she is working on the cataloging of the main e-learning scenarios and their specification in a way oriented to automation; this is from the formal description of this e-learning scenarios provided by ontology to its translation into an executable process language suitable for an e-learning environment. Ellen Rusman holds a Msc in educational science and technology at the University of Twente in the Netherlands. She spend several months as a student at the Centre for Studies in Advanced Learning Technology (C-SALT) at Lancaster University and has been an educational designer at the Educational Technology Expertise Centre of the Open University of the Netherlands since 1998. She designs environments for learning, information and knowledge management. Her fields of expertise are the nurturing of networks for learning and working and educational design for computer supported collaborative learning. Recently she started to combine her work as an educational designer with in-depth research within a PhD trajectory on trust. Interpersonal trust is seen as an important determining factor for the successfulness of human collaboration. In this research she explores how members of a virtual team estimate each others trustworthiness and how they can be supported while making these estimations in mediated environments. Peter Sander is a professor in the School of Engineering of the University of Nice - Sophia-Antipolis. His current research interests include collaborative work environments, e-learning, web frameworks, semantics and presentation of mathematical content on the web. He has participated in European projects including OpenMath, MONET (Math on the Net), TRIAL-SOLUTION (Tools for Reusable Integrated Adaptable Learning...). Giustina Secundo holds a MSc from the Euro-Mediterranean Incubator in Business Innovation Leadership of Scuola Superiore ISUFI—University of Salento (Italy), where she is currently a Researcher. Her research interest concerns the emerging trends in management education and human capital creation process in business schools and corporations, with a special focus on the evolution of corporate university phenomenon. She’s currently involved in the design and experimentation of innovative methodologies and technology platforms to support higher education and corporate learning processes. These research activities are strictly connected to her involvement in the management of the advanced education programs of the Incubator, which involve students coming from Tunisia, Morocco and Jordan (e-mail: [email protected]).

387

About the Contributors

Miguel A. Sicilia obtained a University degree in computer science from the Pontifical University of Salamanca in Madrid, Spain (1996) and a PhD from Carlos III University in Madrid, Spain (2002). In 1997 he joined an object-technology consulting firm, after enjoying a research grant at the Instituto de Automática Industrial (Spanish Research Council). From 1997 to 1999 he worked as assistant professor at the Pontifical University, after which he joined the computer science department of the Carlos III University in Madrid as a lecturer, working simultaneously as a software architect in ecommerce consulting firms, and being a member of the development team of a personalization engine. Since 2002 to October 2003, he worked as a full-time lecturer at Carlos III University working actively in the area of adaptive hypermedia. Currently, he works as a full-time professor at the computer science department, University of Alcalá (Madrid). His research interests are primarily learning technology and Semantic Web, and he is editor of several journals and main research of several projects in this area. Howard Spoelstra studied philosophy and received a master’s degree in knowledge management in 2001. In 1997 he joined the Open University of the Netherlands as an assistant professor at the School of Science where he was involved in the development of the “Virtual Company” educational scenario for competence building in virtual teams. In 2001 he joined the Educational Technology Expertise Centre of the Open University of the Netherlands. His work there focuses on research and development of collaborative learning methods, like learning networks and CSCL environments and online teaching support tools. He is the co-editor of Learning Networks using Learning Design: A first collection of papers (Open Universiteit Nederland, 2004). Roland Staudinger is full professor and chair of the Institute for Applied Systems Research and Development. He received a Master’s degree in Law from Salzburg University, Austria, a Master’s degree in Health Care Management from Innsbruck University, Austria, a PhD in Law from Innsbruck University, Austria and a PhD in Theoretical Medicine from Halle/Wittenberg University, Germany. Cesare Taurino after a Degree in Computer Science Engineering, started his professional experience as a Research Fellow at the Euro-Mediterranean Incubator in Business Innovation Leadership of Scuola Superiore ISUFI—University of Salento (Italy). His research is focused on emerging trends, methodologies and technologies for web learning, including the implication of semantic web paradigm. As a responsible of the Web Learning Lab of the eBMS, he’s involved in the management, customization and administration of the eBMS Web Learning platform, as well as in the creation of learning contents delivered through the platform (e-mail: [email protected]). Evangelos Triantafillou is a researcher in the Center of Educational Technology, Thessaloniki, Greece. Concurrently, he is a lecturer in Economics Department of the University of Macedonia, Thessaloniki. He holds a BSc in Mathematics from Aristotle University of Thessaloniki, Msc in Computer Science from University of East Anglia, UK and Ph.D. degree in Educational Technology from Aristotle University of Thessaloniki. His research interests include Educational Technology, Multimedia Educational Technology, Adaptive Hypermedia Systems and Computerized Adaptive Test. He has published several papers in international scientific journals and conferences. Thrasyvoulos Tsiatsos obtained his Diploma, his Master’s Degree and his PhD from the Computer Engineering and Informatics Department of Patras University (Greece). He is currently Lecturer in the

388

About the Contributors

Department of Informatics of Aristotle University of Thessaloniki as well as research member at the Research Unit 6 of Research Academic Computer Technology Institute. His research interests include Computer Networks, Telematics, Networked Virtual Environments, Multimedia and Hypermedia. More particular he is engaged in Distant Education with the use of Computer Networks, Real Time Protocols and Networked Virtual Environments. He has published more than 70 papers in Journals and in wellknown refereed conferences and he is co-author in 3 books. He has participated in R&D projects such as OSYDD, RTS-GUNET, ODL-UP, VES, ODL-OTE, INVITE, EdComNet, VirRAD, SAPSAT (Vocational Education and Training program) and Promotion of Broadband in Western Region of Greece. Manolis Tzagarakis holds a Ph.D. in Computer Engineering & Informatics and is currently a researcher at the Research Academic Computer Technology Institute in Patras, Greece. His research interests are in the areas of Hypertext and Hypermedia, Knowledge Management Systems, Collaboration Support Systems, Technology-enhanced Learning, and Group Decision Support Systems. He has served the program committees of several conferences and workshops. More detailed information can be found at http://tel.cti.gr/tzag/. Jan van Bruggen is an associate professor at the Educational Expertise Center (Otec) of the Open University of the Netherlands. He holds a PhD in educational technology. He conducted experimental research in cognitive and metacognitive reading and studying strategies. At the University of Amsterdam (COWO and SCO-Kohnstamm Institute) he co-pioneered one of the first firms in computer-based training and distance training for major Dutch financial institutions. He was involved in several European projects in Esprit (Eurohelp) as well as projects in Delta, where he was project leader of the Delta project ACES. In 1992, he joined the Open University of the Netherlands where he developed knowledge-based systems to support students and staff in curricular planning. Further, he was involved in the development of the “Virtual Company” educational scenario for competence building in virtual teams. At Otec he is involved in the Technology Development Program and holds the position of Deputy Dean in the master degree program “Active Learning”. Shibin Wang ([email protected] ) was born in Shanghai on January 24,1984. He is a graduate student in the School of Electronic and Information Engineering at Xi’an Jiaotong University, P.R. China. He received his Bachelor’s degree in Science School from Xi’an Jiaotong University. His research interests include data mining, user interesting discovery. Xiyuan Wu ([email protected]) is a lector in the School of Electronic and Information Engineering at Xi’an Jiaotong University, P.R. China. She received her MSc in Computer Science and Technology from Xi’an Jiaotong University, China. Her current research interests include intelligent e-learning theory and algorithm, machine learning. Amel Yessad is Ph.D student at the Badji Mokhtar University (Algeria) and works in the Edelweiss team INRIA- Sophia Antipolis (France). She is an assistant professor at the 20 Août 1955 University (Algeria). Her research interests the automatic generation of hypermedia courses in the context of webbased learning and particularly the use of semantic web models and technologies to improve the reuse of learning objects from distributed repositories.

389

About the Contributors

Denis Zampunieris holds a PhD in computer science from the University of Namur (B) and is professor of at the University of Luxembourg (L) in the faculty of Sciences, Technology and Communication (http://www.uni.lu), where he is the director of studies of the Bachelor of Engineering in Computer Science. His current main research interests are the design of new software technologies and tools for e-learning, with a focus on the integration of proactive behaviors. He is the founder and the academic head of the R&D team “CICeL - Cellule ’Ingénierie et de Conseil en e-Learning”. Qinghua Zheng ([email protected]) is a professor in the School of Electronic and Information Engineering at Xi’an Jiaotong University, P.R. China. Concurrently, he heads Shaanxi Province Key Lab. of Satellite and Terrestrial Network Tech. R&D, China. Dr.Zheng received his PhD from Xi’an Jiaotong University, China. His current research interests include intelligent e-learning theory and algorithm, computer network security.

390

391

Index

Symbols \ 317 1-parameter logistic 319 3D-centered tools 38 3D classroom 35, 40 3D collaborative e-learning 23 3D collaborative virtual environments 30 3D CVE platform 31 3D e-collaboration 20 3D environments 20 3D humanoid avatars 20 3D multi-user virtual spaces 38 3D text/bubble chat 32 3D virtual class 34 3D virtual classroom 39 3D virtual environments 38 3D virtual worlds 23 3D Whiteboard 32

A Abstract Conceptualization 9 abstract knowledge 177 abstract learning 161 academic research 200 Access Control Lists 286 actions 258, 259, 260, 261, 262, 263, 264 action scripting 234 activation guards 260, 261 Active Experimentation 9 activist 240 adaptability 277 Adaptive Conceptual Map 160, 161, 165, 166 Adaptive Course Content 167 Adaptive Course Generation 166

Adaptive Educational Hypermedia 159 adaptive educational hypermedia and webbased system 161 adaptive Hypermedia 167 adaptivity 277 advanced mobile technologies 318 affective strategy 139 aggregation model 201 alternativeStrategy 166 Amici 205, 206, 207, 208 anxiety 101, 108 application sharing 28, 40 argumentation systems 329 argumentative collaboration 327, 328 argumentative collaboration environments 328, 329 argumentative collaboration tools 329 assessment 316, 317, 318, 319, 322, 324, 325 atomic actions 305, 306, 311, 312, 314 Atomic Taxonomy 279 attention 245 attribute 145 audio 28 audio chat 32, 40 audio collaboration 40 Audio - video conference 11 augment collaborative sense-making 328 authentication 12, 266, 267, 268, 269, 275 authentication scheme 121 autonomy 101, 104, 108 autoregulation 215 average variance extracted 91 awareness services 329

Copyright © 2010, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

Index

B basic and communication tool 252, 253 behaviorism 212, 213, 215, 230 blackboard 54 blended learning 84, 184 blended mode 84, 88 blogs 176, 202 BPEL 294, 295, 296, 299, 300, 301, 302, 303 BPM 294, 295, 296, 297, 299, 300, 303, 304 BPMN 295, 296, 298, 299, 301, 303, 304 brainstorming 25, 33, 37 Brainstorming Board 32 Brainstorming/Roundtable 39 Bubble chat 34 business arena 226 Business Intelligence (BI) 11 Business Management 14 business process execution language (BPEL) 294 business process management (BPM) 294

C campus constructs 235 CAT 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326 CAT-MD 316, 318, 319, 320, 321, 322, 323, 324 chat 191 Cloze technique 246 cognitive abilities 245, 246, 257 cognitive load 69, 70, 71, 72, 73, 74, 75, 77, 78, 80, 81 cognitive load theory 69, 71, 73, 74, 75, 77, 78, 80, 81 cognitive style 141 cognitivism 212, 213, 215, 230 collaboration 40 collaboration environment 332 collaboration items 329 collaboration services component 11 collaboration space 329 collaboration systems 336 collaborative actions 333

392

collaborative activities 328, 337 collaborative e-learning 21, 23, 30, 31, 41 collaborative e-learning scenarios 38 collaborative environment 331 collaborative learning 38, 39, 216, 217, 221, 225, 231 collaborative virtual environments (CVEs) 21, 22, 23, 24, 27, 35 collaborative workspace 330 collaborative writing 177 commercial tagging systems 201 common database 3 common meaning 77 communication environment 118, 119, 120 Communication Media 35 Communication Technologies 29 Community building 180, 188 compensation and social strategies 138 compensation and social strategy 139, 151 complex 47 ComplexStatement 165 component-based software engineering 278, 279 ComposedStatement 168 computer-based technologies 317, 327 Computer Based Testing 316 Computerized Adaptive Testing 317 Computerized Adaptive Testing on Mobile Devices 316, 318, 324 Computerized Based Testing 317 computer-mediated communication (CMC) 61 computer-mediated learning 233 computer-supported collaborative learning (CSCL) 59, 60, 67, 68 computer-supported collaborative work 327 computer-supported environment 331 concepts and productivity tools 252, 253 Concrete Experience 9 conditions 259, 260, 261 confidentiality 267, 269 confirmatory factor analysis 91 constructionist 233 constructivism 178, 202, 212, 214, 215, 30, 232, 234 Constructivism paradigm 4 constructivist 177, 214, 216, 219, 226, 228, 230

Index

constructivist environments 47 constructivist learning 22, 191 contemporary collaboration environments 332 content 212, 213, 215, 216, 221, 222, 227, 228 content formats 295, 302 Content Management 10 context-sensitive 330 conventional environment 104, 105 conventional learning 104 conventional learning environment 106 convergent validity 77 COOPER 306, 310, 311, 312, 313, 314 CoPe_it! 327, 328, 331, 332 core set 137, 142, 143, 145 corporate intranet 215, 221 corporate training 211, 212, 217, 219, 220, 222, 223, 224, 226, 229 Course Management Systems 84 creative development 252 Creator Resource Object 280, 281 Cross-disciplinarity 7 cross services layer 12 Curricula-driven approach 11 Customer Relationship Management (CRM) 11

D data acquisition 260, 261, 263 database requests 259, 263 data-intensive and cognitively-complex argumentative collaboration 328 data-intensive argumentative collaboration environments 329 data–intensive collaboration 329 data-intensive environment 330 data transfer object 285 deductive strategy 161, 162 Design Rationale 31 design time 307, 310, 311 dialectical constructivism 25 dichotomous IRT model 319 didactic content delivery 233 digital competence 47 digital comprehension 248 digital learning content 278

digital literacy 47, 175, digital media 253 digital signatures 265, 266, 267, 274, 275 digital signature technology 266 digital technology 247, 248 digital text 247 direct instruction 60, 61, 64 discernibility function 140 discourse facilitation 60, 64 discussion forums 183, 192, 193, 338 distance education 226, 229 distance learning 160, 235, 241, 242 distance training 212, 213, 215, 218, 221, 226, 227, 228, 229 diverse community 29 Diversity 7 Document Management 10 domain knowledge 162 domain model 161, 162, 164, 167 double-loop learning 305, 306, 307, 308, 310, 311, 314 double-loop-learning-based educational scenarios 305 dual folksonomy triad 203 Dynamic Course Generation 161 dynamic environment 332 dynamic folksonomies 202, 204 dynamic loading 284 dynamic rules-based system 258, 259, 263

E earner model 161 e-Business component 11 e-Business services 9 e-collaboration 22, 24, 41 EDETEC 245 effective collaboration 336 efficiency problems 258, 259, 263 e-Framework 279, 283, 291 e-learning 22, 28, 29, 30, 41, 49, 56, 107, 134, 137, 160, 161, 177, 305, 307, 314, 316, 317, 318 e-learning architecture 282 e-learning communities 41 ELearningConcept 163 e-Learning environment 142, 208

393

Index

e-learning platforms 258 eLearningProperty 163 e-Learning scenarios 202 e-learning systems 41, 152, 203, 265, 266 e-learning technology 294 electronic messages 247 electronic multimedia 247 electronic signatures 266 electronic survey 89 electronic text 247 elf-management conception in learning 138 e-Library 10 e-mail 3, 338 email 191 embodied conceptualizations 200, 202, 206, 208, 209 embodied interaction 200, 204 embodiment 200 enhanced flexibility 278 Enterprise Resource Planning (ERP) 11 Escala Fatorial de Realização 248 e-students 259, 262, 263 e-testing 316 ethical issues 251 e-tutor 259, 261, 262, 263 EVAWEB 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275 everyday work 48 EVE Training Area 20, 30, 31, 32, 33 Exercise activity 166 expertise reversal affect 79 exponential phase 127 external learning repositories 166 extraneous load 72, 73, 74, 75, 77, 78, 79 extrinsic cognitive load 69, 70, 72, 73

F face-to-face 100, 107, 137, 175, 191 face-to-face mode 83 face-to-face training 212, 221 factor analysis 69, 70, 74, 76, 77, 78, 80 File Service 285, 287 file system 285 folksonomies 201, 203, 204, 208 folksonomy 200, 204, 206, 208 form-focused strategies 138

394

form-focused strategy 139, 151 forums 183, 185, 186, 188, 190 frontal learning 21 Fundamental Data Object 280 Fundamental Learning Object 280

G geotagging 201 germane load 72, 73, 74, 75, 76, 77, 78, 80 global economy 84 global organizations 3 goal orientation 211, 216, 225, 226, 227 Graphical icons 167 Grouped Learning Object 280 Group-structural awareness 331 group work 233, 234, 235, 236, 237, 239

H half-normal phase 127 hash functions 267 Helper Resource Object 280, 285 high level cognitive abilities 245, 246 high quality visualisation 22 histogram method 142 Historical awareness 331 holistic mode 234 hooks 203 human capital 14 human cognition 70 Human Computer Interaction 199 humanoid avatars 38 human processing theory of information 246 human support 60 hybrid mode 84 “hybrid” typologies 7 hypothetico-deductive scientific research 85

I ICC functions 319 ICT 46, 47, 48, 54, 84, 85, 89, 98, 99, 100 ICT-based Business Configurations 7 ICT-based course 86 ICT-based learning 86 ICT-based learning environments 87

Index

ICT performance 250 ICT-supported learning environments 86 Immersive Virtual environments 23 Inclusive 30 Incubator 6 Informal awareness 330 information and communication technologies 83 Information and Communication Technologies 245 Information Society Technology (IST) 47 Information System for Learners’ Personality Characteristic 140 information systems 86 Information Technologies 191 inherent adaptability 41 Inherent adaptability 22 inherent flexibility 41, 22 instructional design 212, 213, 215, 226, 228, 232, 305, 310, 311, 314 instructional processes 305 instructivism 215 instructivist 216, 219, 226, 227, 228 integrated development environment 205 integrity 267, 269 Interactive Advertising Bureau 253 interactive computing 259 interconnected 47 Internationality 7 International Society for Technology in Education 248 Internet Business Management 14 Internet community 200 Internet/Web based learning 29 interpersonal skills 235 intrinsic load 71, 72 in-world commerce 235 IT 46, 47, 48 Item Characteristic Curve 319 Item Information Function 319 Item Response Theory 318, 319, 324

J Java Applet 286 Jigsaw 39

K Kernel 283, 286 key personality attribute 140, 153 key personality characteristic attribute 140, 142, 143, 147 Key Personality Characteristics 147 KM taxonomy 14 knowledge application 8, 14 knowledge based society 175 knowledge creation 8, 14 knowledge management 1, 2, 9, 10, 202, 203, 329, 331 knowledge management component 10 knowledge management services 329 knowledge organisation 8, 14 Knowledge Pool System 167 knowledge sharing 8, 14 kurtosis 122, 124, 132

L language learning conception 139 language learning styles 138 latent variable environments 95 lazy evaluation 258, 259, 263, 264 Learner 164 learner centered approach 4 Learner Interest 148 Learner Interest Model 146 learner knowledge 162 learner learning conception data 142 learner learning style data 142 learner model 136, 164 Learner Model 165 learner personality characteristic 136 learner personality trait 141 learner personality trait data 142 learners’ personalized learning strategies 140 Learning Activities 181 learning architecture 278 Learning Base 12 learning community 60, 61, 62, 63, 68 learning conception 138, 139, 143 Learning Content Management System 12 Learning Context 35 learning environments 84, 95, 104, 106, 177 learning infrastructure 278

395

Index

learning interest 145 learning management 192 learning management system (LMS) 11, 192, 258, 259, 263, 264, 294 learning models 162, 164 Learning Module 278, 280 learning object 277, 278, 279, 280, 289, 295, 297, 301, 302, 303 Learning Object Metadata 285 learning ontology 161, 163, 164 LearningPedagogy 163 LearningPerson 163 learning platforms 278 learning process 208, 250, 252, 317 learning processes 202, 204, 215, 296, 303 learningResourceAuthor 168 learningResourceRole 168 learning resources 160 learningResourceTopic 168 learning strategies 136, 137, 138, 143, 147, 152 learning strategy 135, 139, 141, 142 learning strategy data 142 learning strategy module 136 learning style 138, 139, 141, 143 LearningTopic 163, 168 Lego Mindstorms system 205 lifelong learning 48, 160, 192 Life-long-learning 180 Linden dollar 235 LMS 258, 259, 260, 261, 262, 263, 264, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303 LoaderApplet 286, 288 Log-In User Service 286 logistics models 319 LOM metadata 160, 161, 167 long term memory 70, 71, 72

M Macromedia Flash 321 Macromedia Standalone-Flash Player 322 macroscopic visualisation 22, 40 Management Information System 52 market dynamics 212

396

massively multiplayer online role-playing games 234 maximum likelihood estimation 320 meaning-focused strategy 138, 139, 151 media performance 253 MediumTopic 165 memory 245 Message Service 284, 287 metacognition 217, 230 metacognitive strategy 138, 139, 152 metacognitive support 211, 216, 217, 225, 226, 227, 228 metadata 203, 285 meta-model 161, 166 meta-model knowledge 162 meta-reflection 62, 64 Microblogging 193 microscopic visualisation 22, 40 Microsoft’s NetMeeting 21 MMORPGs 234 mobile devices 316, 317, 318, 321, 324 mobile technologies 317 moderator variables 84, 87, 97, 104 modular architecture 281 modularity 277, 278, 279, 281, 286, 289 modular knowledge organization 162 Moodle 121 motivation 101, 108, 211, 215, 216, 217, 218, 223, 224, 225, 226, 227, 228, 231 multicultural 29 multidimensional 47 multilingual virtual space 29 Multiple Choice Cloze Test 251 Multiple media 317 Multi-user 21 Multi-user Virtual Reality technology 23 Multi-user VR 21 multi-user VR technology 21 mutability 247 MySpace 234

N National Agency for School Autonomy Development 49 networked classroom 105

Index

Networking 7 New Media Consortium (NMC) 24 newsgroup 3 nocore set 137, 142, 143, 145 nonproctored exams 266 non-repudiation 265, 266, 267, 274, 275 non-repudiation security services 266 normal phase 127

O object construction 234 Object Request Brokers 286 online assessment 265, 266, 274, 276 Online Education 250 online facilities 235, 236 online learning 84 on-line meeting space 3 online mode 83, 88 on the fly 305 Open 30 OpenSSL 266 Operator 163, 165, 167 optimal learning strategies 147 Organisateur de Parcours Adaptatifs de Formation 160 organizational aspects 60, 63, 64 organization planning 48

P packages 264 Partial Least Squares 90 participation 101, 108 Partitioning 25 PBL strategy 11 pedagogical 279, 285 PedagogicalActivity 166, 168 pedagogical community 52 pedagogical content 215 pedagogical flexibility 277 pedagogical formations 102 pedagogical knowledge 162 pedagogical methods 277 pedagogical model 161, 164, 166 Pedagogical Paradigm 52 pedagogical services 291 pedagogical strategy 161, 162

pedagogical value 191 pedagogy 316, 318 People 8 Personal Digital Assistant 318 Personality characteristic 138 personality trait 138, 139, 152 personalized e-learning 135, 151, 153 personalized e-learning environment 135, 136, 137, 138 personalized e-learning system 154 Personalized English Learning System 146 personalized information services 134 personalized learning 160 Personalized Learning Path Organizer 160 personal knowledge 192 Personal Learning Environments 179 personally-managed space 180 personal storage 201 PGP 266, 276 Phase and Activity Management 10 Photobucket 239 platform neutrality 277, 287 podcasts 176 portable media players 321 pragmatist 240 preferedAuthor 164 preferred learning activities 240 Preprocessing 142 prerequisiteOf 163, 165, 169 Presence awareness 331 presence indicators 332 Presentation Board 36 printed texts 247 proactive LMS 258, 259, 263 Problem-Based Learning (PBL) 11, 14 Problem-driven approach 11 problem solution tools 253 Procedure 167 process building blocks 305, 306 Processes 8 Processing Flow 146 professor pedagogy 87 professors’ pedagogy 85, 86, 96, 98, 102 programming language 206, 207 Progress display 37 project making 48

397

Index

project management component 10 Proximity and activity 25 psychological characteristics 105 psychological theory 318 psychometric theory 318 puntoedu 49 Purpose 8

Q qualitative data analysis 96 qualitative measures 74 Queried learning resources 160 Quickwrites / Microthemes 40

R reactive software 258 reading comprehension 245, 246, 247, 249, 250, 252, 253, 254, 255, 256 Really Simple Syndication 337 reasoning 245, 246 reasoning processes 246 Recommendation Engine 10 Reduction Efficiency 141 reflection 235, 241 Reflective Observation 9 reflector 240 Reflexive writing 179 Relational orientation and reciprocity 25 Remote task support 26 repositories 295, 297, 302, 303, 304 repository functions 295, 301 Representational State Transfer 290 resource connectivity 201 Resource Objects 280, 282 RMI Activation System Daemon 286 RMI Servers 286 role-playing games 234 ross-disciplinary approach 7 rules generation 260, 261 rules-running system 258, 259, 260, 263 run time 307, 311, 312

S Scaffolding tools 26 SCORM 11

398

SCORM standard 14 Second Life 233, 234, 236, 237, 241, 242 Second Life validation 36 security 265, 266, 269, 270, 274, 275, 276 self-efficacy 247, 248 self-motivation in learning 235 self-steered learning 202 self-steered learning processes 199 self-study 160 semantic conformance profile (SCP) 295 SemanticLanguage 167 Semantic Navigator 10 semantic Web for E-Learning 160 Semantic Web Services 290 Semantic Web technology 290 sequencingStrategy 166 server-based system 323 Service-Oriented Architecture 281 sets of rules 262, 264 Sharable Content Object 278 shared tagging 204 shared workspaces 332 Simple Object Access Protocol 281 SimTeach 235, 244 single attribute 145 Single-loop learning 307 single-sign-on 121 sixteen personality factors 138 skewness 122 smart-card signature 269 SMS messages 338 Snapzilla 239 SOA 277, 279, 281, 282, 283, 284, 287, 289, 290, 291, 293 SOA architecture framework 282 SOA framework 282, 287 social awareness 22, 331 social capital 14 social computing 200 social connections 201 social connectivity 201 social constructivism 178 social interactions 328 socialization phase 65 social negotiation 4 social networks 328

Index

Social Software 175, 181, 183 social tagging 200, 202, 203, 206, 208 Social tagging systems 200, 201, 202, 204, 207, 209 socioconstructivist 59, 60, 66 software engineering 177 Software tools 317 standard deviation 253 standardized architectural frameworks 278 Statement 163, 165 Static-Group Comparison 88, 108 strategic decision-makers 106 structural capital 14 structural equation modeling 90 structured interview 89 students’ characteristics 84 students’ learning outcomes 84 students’ psychological processes 86 subTypeOf 165, 169 success factors 211, 212, 219, 220, 224, 22 7, 228 Supply Chain Management (SCM) 11 Supply Resource Management (SRM) 11 synchronous training 21

T tagging action 206 tagging process 208 tagging rights 201 tagging support 201 tagging systems 201, 202, 204, 206 tag semantics 201 Task awareness 331 task completion 239 task orientation 216, 225 taxonomy 207 TD-SSIS 61, 62, 63, 64, 65, 66 teacher role 216, 217, 225 teacher’s centered approach 4 teaching modalities 249 teaching pedagogy 102 Teaching Transparency Information System 84 technical flexibility 277 technological equipment 253 technological literacy 246

technological resources 249 Technology 8 Technology Acceptance Model 87 technology development 48 Technology Enhanced Learning 277 technology frameworks 282 Technology Management 14 Technology Performance Scale 245, 248, 249, 250 technology platform 281, 282 telephone 191 testing environments 317 test management 317 TETIS 46 TETIS platform 52, 53, 54, 55, 56 text-based 250 text chat 28, 40 thematic analyses 90 thematic unit (TU) 119, 120, 121, 123, 124, 125, 126, 127, 131, 132 theoretically-based research 86 theorist 240 Tool 283 ToolDTO 285 Tool Service 285, 286 traditional lectures 70 traditional mode 234 transformative mode 234 treasure hunt task 237 tripartite networks 200

U United Nation Educational, Scientific and Cultural Organization 248 Universal Description, Discovery, and Integration 288 usage data 118 user-action-oriented software 258 User-centred 30 user-centred awareness services 330 user groups 118, 120, 126 User Management, Personalization 12 User Service 284 Using Universal Unique Identifiers 288

399

Index

V verbal elaboration strategies 246 video 28 videoconference 3, 120 Video presenter 32 virtual businesses 234 Virtual Classroom (VC) 11, 32, 39 virtual communities 3 Virtual Community of Practices (VCoPs) 1, 2, 3 virtual company 305, 306, 308, 309, 310, 311, 314, 315 Virtual eBMS 1, 6, 7, 8, 9, 14 Virtual eBMS”Community 2, 6 virtual environment 27, 56 virtual environments 23, 31 virtual group identity 26 virtuality 3 virtual learning environments (VLEs) 233, 277 Virtual meeting 11 virtual reality technology 28, 40 Virtual Reality (VR) 21, 28 virtual space 28, 30 Virtual Workspace Environment 279 virtual worlds 27, 233, 234, 236, 239, 242, 243 VLE 234, 237, 239 VR applications 23 VR technology 21, 23 VWE architecture framework 279, 285, 289 VWE framework 286, 290 VWE kernel 285, 286 VWE Learning Object taxonomy 277, 279, 280, 281, 282, 288 VWE Resource Objects 288 VWE service-framework 286 VWE SOA architecture framework 290 VWE SOA framework 283 VWE Tools 281, 283, 285, 286, 289 VWE workspaces 284

W web 28 Web 1.0 176 Web 2.0 23, 31, 176, 278 Web 2.0 tools 84

400

web-based argumentative collaboration environments 338 Web-based assessment system 265, 266, 267, 274, 275 web-based classroom 249 web-based collaboration 327 web-based communities 327, 338 Web-based course 87 Web-based distance training 218, 226, 228, 229 web-based education 175 web-based learning 41 web-based services 206 web-based social software 175 web-based sources 317 web-based tool 328, 331 Web-based training 212, 215, 216, 217, 218, 219, 225, 226, 227, 228, 229 web browser 281 WebCT 21, 97 Web environment 104 web-interface 206 Web Learning 1, 2, 9, 10, 12, 14 web learning component 11 Weblog 181 weblogs 175, 176, 183, 190, 192, 193 Weblogs 182, 191 Web Mining 10 web repositories 160 Web Service Description Language 281, 288 Web Service standards 338 Web Service technology 282, 287, 290 Website 3 Web technologies 46 Web tools 84 weight value 140 wiki 187, 191 Wiki 181 Wikipedian Community 187 wikis 176, 177, 183, 185, 186, 188, 190, 192, 202 Wikis 175, 193 wireless technologies 318 workflow 305, 306, 307, 310, 311, 312, 314, 315 Workflow Definition 10

Index

working memory 70, 71, 72, 77 Workspace 283 Workspace awareness 331 WorkspaceDTO 285 Workspace Editor 283

workspace head up display 334 Workspace Service 284, 286 Workspace Teleporting 335 World of Warcraft 234 World Wide Web 134

401