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IFIP Advances in Information and Communication Technology

324

Editor-in-Chief A. Joe Turner, Seneca, SC, USA

Editorial Board Foundations of Computer Science Mike Hinchey, Lero, Limerick, Ireland Software: Theory and Practice Bertrand Meyer, ETH Zurich, Switzerland Education Bernard Cornu, CNED-EIFAD, Poitiers, France Information Technology Applications Ronald Waxman, EDA Standards Consulting, Beachwood, OH, USA Communication Systems Guy Leduc, Université de Liège, Belgium System Modeling and Optimization Jacques Henry, Université de Bordeaux, France Information Systems Barbara Pernici, Politecnico di Milano, Italy Relationship between Computers and Society Chrisanthi Avgerou, London School of Economics, UK Computer Systems Technology Paolo Prinetto, Politecnico di Torino, Italy Security and Privacy Protection in Information Processing Systems Kai Rannenberg, Goethe University Frankfurt, Germany Artificial Intelligence Max A. Bramer, University of Portsmouth, UK Human-Computer Interaction Annelise Mark Pejtersen, Center of Cognitive Systems Engineering, Denmark Entertainment Computing Ryohei Nakatsu, National University of Singapore

IFIP – The International Federation for Information Processing IFIP was founded in 1960 under the auspices of UNESCO, following the First World Computer Congress held in Paris the previous year. An umbrella organization for societies working in information processing, IFIP’s aim is two-fold: to support information processing within its member countries and to encourage technology transfer to developing nations. As its mission statement clearly states, IFIP’s mission is to be the leading, truly international, apolitical organization which encourages and assists in the development, exploitation and application of information technology for the benefit of all people. IFIP is a non-profitmaking organization, run almost solely by 2500 volunteers. It operates through a number of technical committees, which organize events and publications. IFIP’s events range from an international congress to local seminars, but the most important are: • The IFIP World Computer Congress, held every second year; • Open conferences; • Working conferences. The flagship event is the IFIP World Computer Congress, at which both invited and contributed papers are presented. Contributed papers are rigorously refereed and the rejection rate is high. As with the Congress, participation in the open conferences is open to all and papers may be invited or submitted. Again, submitted papers are stringently refereed. The working conferences are structured differently. They are usually run by a working group and attendance is small and by invitation only. Their purpose is to create an atmosphere conducive to innovation and development. Refereeing is less rigorous and papers are subjected to extensive group discussion. Publications arising from IFIP events vary. The papers presented at the IFIP World Computer Congress and at open conferences are published as conference proceedings, while the results of the working conferences are often published as collections of selected and edited papers. Any national society whose primary activity is in information may apply to become a full member of IFIP, although full membership is restricted to one society per country. Full members are entitled to vote at the annual General Assembly, National societies preferring a less committed involvement may apply for associate or corresponding membership. Associate members enjoy the same benefits as full members, but without voting rights. Corresponding members are not represented in IFIP bodies. Affiliated membership is open to non-national societies, and individual and honorary membership schemes are also offered.

Nicholas Reynolds Márta Turcsányi-Szabó (Eds.)

Key Competencies in the Knowledge Society IFIP TC 3 International Conference, KCKS 2010 Held as Part of WCC 2010 Brisbane, Australia, September 20-23, 2010 Proceedings

13

Volume Editors Nicholas Reynolds The University of Melbourne, Melbourne Graduate School of Education Melbourne, VIC 3010, Australia E-mail: [email protected] Márta Turcsányi-Szabó Eötvös Loránd University Faculty of Informatics, Informatics Methodology Group Pázmány Péter Sétány 1/C, 1117 Budapest, Hungary E-mail: [email protected]

Library of Congress Control Number: 2010932675 CR Subject Classification (1998): I.2.6, H.5, H.4, H.3, J.1, K.4 ISSN ISBN-10 ISBN-13

1868-4238 3-642-15377-1 Springer Berlin Heidelberg New York 978-3-642-15377-8 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. springer.com © International Federation for Information Processing 2010 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper 06/3180

IFIP World Computer Congress 2010 (WCC 2010) Message from the Chairs Every two years, the International Federation for Information Processing (IFIP) hosts a major event which showcases the scientific endeavors of its over one hundred technical committees and working groups. On the occasion of IFIP’s 50th anniversary, 2010 saw the 21st IFIP World Computer Congress (WCC 2010) take place in Australia for the third time, at the Brisbane Convention and Exhibition Centre, Brisbane, Queensland, September 20–23, 2010. The congress was hosted by the Australian Computer Society, ACS. It was run as a federation of co-located conferences offered by the different IFIP technical committees, working groups and special interest groups, under the coordination of the International Program Committee. The event was larger than ever before, consisting of 17 parallel conferences, focusing on topics ranging from artificial intelligence to entertainment computing, human choice and computers, security, networks of the future and theoretical computer science. The conference History of Computing was a valuable contribution to IFIPs 50th anniversary, as it specifically addressed IT developments during those years. The conference e-Health was organized jointly with the International Medical Informatics Association (IMIA), which evolved from IFIP Technical Committee TC-4 “Medical Informatics”. Some of these were established conferences that run at regular intervals, e.g., annually, and some represented new, groundbreaking areas of computing. Each conference had a call for papers, an International Program Committee of experts and a thorough peer reviewing process of full papers. The congress received 642 papers for the 17 conferences, and selected 319 from those, representing an acceptance rate of 49.69% (averaged over all conferences). To support interoperation between events, conferences were grouped into 8 areas: Deliver IT, Govern IT, Learn IT, Play IT, Sustain IT, Treat IT, Trust IT, and Value IT. This volume is one of 13 volumes associated with the 17 scientific conferences. Each volume covers a specific topic and separately or together they form a valuable record of the state of computing research in the world in 2010. Each volume was prepared for publication in the Springer IFIP Advances in Information and Communication Technology series by the conference’s volume editors. The overall Publications Chair for all volumes published for this congress is Mike Hinchey. For full details of the World Computer Congress, please refer to the webpage at http://www.ifip.org. June 2010

Augusto Casaca, Portugal, Chair, International Program Committee Phillip Nyssen, Australia, Co-chair, International Program Committee Nick Tate, Australia, Chair, Organizing Committee Mike Hinchey, Ireland, Publications Chair Klaus Brunnstein, Germany, General Congress Chair

Preface

It is fitting that there was a World Computer Congress in the 50th anniversary year of IFIP. Within the Learn IT Stream of WCC2010, the conference, Key Competencies in the Knowledge Society (KCKS), brought together some 43 papers from around the world covering many areas of ICT and its role in education. Of the papers presented here, three were selected as key theme papers for the KCKS conference. These papers’ by Adams and Tatnall, Tarragó and Wilson, Diethelm and Dörge, are included in these proceedings. We congratulate these authors for the quality of their work that led to selection. The range of issues covered within this volume is too broad to set out here but covers, amongst other things, e-examination, Twitter, teacher education, school-based learning, methodological frameworks and human development theories. It has been an exciting and rewarding task to put these papers together. They represent a coming together of great minds and cutting-edge research. We thank our contributors and our reviewers for producing such an impressive body of work.

Nicholas Reynolds Márta Turcsányi-Szabó

Organization

Program Chairs M. Turcsanyi-Szabo N.J. Reynolds

Eotvos Lorand University, Hungary University of Melbourne, Australia

Program Committee R.M. Bottino A. Breiter B. Cornu V. Dagiene B. Davey M. Kendall U. Kortenkamp D. Kumar K.-W. Lai J. Magenheim J. Moonen W. Mueller C. Quinn C. Redman S. Rosvik E. Stacey L. Stergioulas A. Tatnall B. Thompson S. Wheeler J. Zhang

Istituto Tecnologie Didattiche Consiglio Nazionale Ricerche, Italy University of Bremen, Germany CNED, France Institute of Mathematics and Informatics, Lithuania RMIT University, Australia East Midlands Broadband Community, UK University of Education Karlsruhe, Germany Bryn Mawr College, USA University of Otago, New Zealand University of Paderborn, Germany Moonen & Collis Learning Technology Consultants University of Education Weingarten, Germany Quinnovation, USA University of Melbourne, Australia Giske Municipality, Norway Deakin University, Australia Brunel University, UK Victoria University, Australia University of Sunderland, UK University of Plymouth, UK University at Albany, USA

Table of Contents

Use of ICT to Assist Students with Learning Difficulties: An Actor-Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tas Adam and Arthur Tatnall

1

Use of Graph2Go in M-Learning: A View from the Pedagogical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Silvia Batista, Patricia Behar, and Liliana Passerino

12

ICT in Teacher Education: Developing Key Competencies in Face-to-Face and Distance Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ana A. Carvalho

23

Recommendation of Learning Objects Applying Collaborative Filtering and Competencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S´ılvio César Cazella, Eliseo Berni Reategui, and Patr´ıcia Behar

35

Teaching Practice from the Perspective of ICT Student Teachers at the Faculty of Education, Charles University in Prague . . . . . . . . . . . . . . . . . . . ˇ Miroslava Cernochov´ a

44

Intentions to Pursue a Career in Information Systems and Technology: An Empirical Study of South African Students . . . . . . . . . . . . . . . . . . . . . . Jason F. Cohen and Poonam Parsotam

56

From Context to Competencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ira Diethelm and Christina D¨ orge

67

Competencies and Skills: Filling Old Skins with New Wine . . . . . . . . . . . . Christina D¨ orge

78

Coping with Complex Real-World Problems: Strategies for Developing the Competency of Transdisciplinary Collaboration . . . . . . . . . . . . . . . . . . . Gitta Domik and Gerhard Fischer

90

Getting Granular on Twitter: Tweets from a Conference and Their Limited Usefulness for Non-participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martin Ebner, Herbert M¨ uhlburger, Sandra Schaffert, Mandy Schiefner, Wolfgang Reinhardt, and Steve Wheeler

102

Beyond Pedagogical Content Knowledge: The Importance of TPACK for Informing Preservice Teacher Education in Australia . . . . . . . . . . . . . . Glenn Finger, Romina Jamieson-Proctor, and Peter Albion

114

eExaminations Development and Acceptance . . . . . . . . . . . . . . . . . . . . . . . . Andrew E. Fluck

126

XII

Table of Contents

Grundtvig Partnership Case Study 2009-2011 LifeLong Learning for Active Citizenship and Capacity Building LLLab . . . . . . . . . . . . . . . . . . . . Anna Grabowska

136

A Theoretical Framework to Foster Digital Literacy: The Case of Digital Learning Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Said Hadjerrouit

144

Conditions for Successful Learning of Programming Skills . . . . . . . . . . . . . Jaana Holvikivi

155

Assessment of Knowledge and Competencies in 3D Virtual Worlds: A Proposal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mar´ıa Blanca Ib´ an ˜ez, Raquel M. Crespo, and Carlos Delgado Kloos

165

IP3 – Progress towards a Global ICT Profession . . . . . . . . . . . . . . . . . . . . . Roger G. Johnson

177

A Teacher’s Perspective of Interacting with Long-Term Absent Students through Digital Communications Technologies . . . . . . . . . . . . . . . . . . . . . . . Anthony Jones and Karina Wilkie

187

University as an Environment for Shaping Key Teachers’ Competence for Knowledge Society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Barbara Kedzierska

193

Are Wikis and Weblogs an Appropriate Approach to Foster Collaboration, Reflection and Students’ Motivation? . . . . . . . . . . . . . . . . . . Mathias Krebs, Christian Schmidt, Michael Henninger, Matthias Ludwig, and Wolfgang M¨ uller Digital Literacy: A Vital Competence for 2010? . . . . . . . . . . . . . . . . . . . . . . Denise Leahy and Dudley Dolan Informatics Systems and Modelling – Case Studies of Expert Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leopold Lehner, Johannes Magenheim, Wolfgang Nelles, Thomas Rhode, Niclas Schaper, Sigrid Schubert, and Peer Stechert In Search of the Affective Subject Interacting in the ROODA Virtual Learning Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Magal´ı Teresinha Longhi, Patricia Alejandra Behar, and Magda Bercht Learning under Uncertainty: A Grounded Theory Study . . . . . . . . . . . . . . Eurico Lopes

200

210

222

234

246

Table of Contents

Integration of a Video Annotation Tool into a Coactive Learning and Working Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Johannes Magenheim, Wolfgang Reinhardt, Alexander Roth, Matthias Moi, and Dieter Engbring

XIII

257

Application of the Multiple Perspectives Model in an Undergraduate Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Célio Gon¸calo Marques and Ana Amélia A. Carvalho

269

Mobile Learning: Using SMS in Educational Contexts . . . . . . . . . . . . . . . . Adelina Moura and Ana Amélia A. Carvalho

281

Making Computer Learning Easier for Older Adults: A Community Study of Tuition Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michael Nycyk and Margaret Redsell

292

The Paradox of More Flexibility in Education: Better Control of Educational Activities as a Prerequisite for More Flexibility . . . . . . . . . . . Henk Plessius and Pascal Ravesteyn

301

Design and Implementation of Business Process Management Curriculum: A Case in Dutch Higher Education . . . . . . . . . . . . . . . . . . . . . . Pascal Ravesteyn and Johan Versendaal

310

All I Need to Know about Twitter in Education I Learned in Kindergarten . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wolfgang Reinhardt, Steve Wheeler, and Martin Ebner

322

Technology and Computers in Music and Music Education . . . . . . . . . . . . Nicholas Reynolds

333

Collaborative Research Training Based on Virtual Spaces . . . . . . . . . . . . . Dar´ıo Rodr´ıguez, Rodolfo Bertone, and Ram´ on Garc´ıa-Mart´ınez

344

The Human Development Process and Informatics Education in the 21st Century . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Toshinori Saito

354

More for Less – Live Systems Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andy Schaer

361

ALEF: A Framework for Adaptive Web-Based Learning 2.0 . . . . . . . . . . . ˇ Mari´ an Simko, Michal Barla, and M´ aria Bielikov´ a

367

Design and Development of Virtual Learning Environment Using Open Source Virtual World Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Steven Suman, Ardavan Amini, Bruce Elson, and Patricia Reynolds

379

Educational Management Challenges for the 21st Century . . . . . . . . . . . . . Ferran Ruiz Tarrag´ o and Ann Elizabeth Wilson

389

XIV

Table of Contents

Supporting Productive Integration of Web 2.0-Mediated Collaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mun Fie Tsoi Evaluating the Cisco Networking Academy Program’s Instructional Model against Bloom’s Taxonomy for the Purpose of Information Security Education for Organizational End-Users. . . . . . . . . . . . . . . . . . . . . Johan F. van Niekerk and Kerry-Lynn Thomson T’aint What You Do (It’s the Way That You Do It): ICT and Creativity in the Primary School Classroom . . . . . . . . . . . . . . . . . . . . . . . . . Mary Welsh and Rae Condie Need for the Intercultural Awareness in Erasmus Mobility – Administrative Point of View . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tatjana Welzer, Marjan Druˇzovec, Marko H¨ olbl, and Mirjam Bonaˇcić

401

412

424

435

Home Access: Providing Computers to Families via a National Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nicola Yelland, Greg Neal, and Eva Dakich

440

Working with Wikis: Collaborative Writing in the 21st Century . . . . . . . . Katina Zammit

447

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

457

Use of ICT to Assist Students with Learning Difficulties: An Actor-Network Analysis Tas Adam and Arthur Tatnall School of Management and Information Systems, Victoria University, Australia {Tas.Adam,Arthur.Tatnall}@vu.edu.au

Abstract. This paper reports on an investigation of the use of Information and Communications Technologies (ICT) to aid in the teaching of students with learning disabilities. The term ‘learning difficulties’ is used in reference to a heterogeneous group of students who are seen to have significant difficulties in the acquisition of literacy and numeracy skills. Other terms sometimes used in this context are ‘learning disabilities’ and ‘special needs’. The study involved participant observation of the use of ICT in two outer suburban Melbourne Special Schools, and an investigation of the role and impact of Education Department policies on these school environments. Research at the two Special Schools revealed that use of ICT can have a very beneficial impact on these students by improving their self esteem and facilitating their acquisition of useful life skills. The study was framed by the use of actor-network theory. Keywords: Information and Communications Technologies, Actor-Network Theory, Learning Difficulties, Special Schools, Students with Special Needs.

1 Students with Learning Difficulties A significant number of students with learning disabilities, or special needs, require assistance and support in their learning. The introduction of Information and Communications Technologies (ICT) and use of the Internet have played a major part in shaping the knowledge and skills of these students. Assistive technology has introduced awareness for both educators and students and for the past decade there has been a growing effort in the design and development of ICT-based platforms to enhance the learning outcomes of these students [1, 2]. The study described in this paper involved participant observation in two outer suburban special schools in Melbourne, and in addition examined the role and impact of Education Department policies on these school environments [3]. The study also identified different categories of students with special needs, ranging from physical disabilities to mainstream students who have the need to maintain some continuity with their studies while temporarily placed in a hospital. One of the problems in working in this area is terminology, with the terms: Learning Difficulties, Children at Risk, Special Needs and Learning Disabilities all being used in different countries and different contexts to describe these children. In this paper the term Learning Difficulties (LD) will be used to cover all of these other N. Reynolds and M. Turcsányi-Szabó (Eds.): KCKS 2010, IFIP AICT 324, pp. 1–11, 2010. © IFIP International Federation for Information Processing 2010

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T. Adam and A. Tatnall

terms [4]. The definition for the term Learning Difficulties that is used in Australia is similar to that used in the USA: A generic term that refers to a heterogeneous group of students who have significant difficulties in the acquisition of literacy and numeracy and who are not covered in the Commonwealth’s definition of a student/child with a disability… Learning disability is believed to be a difficulty that is intrinsic to the individual and not a direct result of other conditions or influences [5]. Given the complexity of the definition of learning difficulties, one way to represent the differences is with the following Venn diagram where the term Learning Difficulties is used to refer to a large group of children who need extra assistance with schooling and Learning Disabilities refers to students who constitute a small sub-group that exhibit severe and unexplained problems.

Learning Difficulties Learning Disabilities

Fig. 1. Learning Difficulties and Learning Disabilities

Although policies have existed for some time in many countries to integrate students with learning difficulties into the mainstream classroom, this has not always provided the best learning environment for these students [6]; hence the need for some Special Schools. Bulgren [7] and Agran [8], support the view that some students with LD required an alternative approach to their learning and numerous ‘integration’ or ‘remedial’ programs have proved inefficient towards the total learning of this group of students. The literature also shows that in some selected fields, for example in mathematics and social studies, specialist instruction has been applied to this group of individuals with little success.

2 A Case Studies of Two Melbourne Special Schools 2.1 Special School-A School-A is a purpose built ‘Specialist School’ providing a range of educational programs for students with special learning needs including global development delay, autism spectrum disorder, physical, social and emotional disabilities. It is located in

Use of ICT to Assist Students with Learning Difficulties: An Actor-Network Analysis

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Melbourne’s north-west and is one of over 80 Government funded Specialist School Facilities in Victoria. In addition to delivering the key learning areas as part of the curriculum the school provides a broad range of programs that are designed to further enhance the independence of its students. Some of the programs include Augmentative Communication, Work Education, Outdoor Education, Bike and Road Safety Education, Music Therapy, Swimming and Hydrotherapy Programs, Riding for the Disabled, Home crafts, Recreation and Leisure and Health and Human Relations Programs. The school has around 90 students, 8 full-time and 3 part-time teaching staff, 15 part-time school support officers and 2 part-time administration staff. The students are quite diverse in their special needs, both physical and intellectual. In the early 2000s the school had limited ICT resources for their learning with several Acorn machines, but very few PCs. The majority of the software was based on the DOS operating system platform where graphics and sound quality were limited. The library had a PC that was used to allow the students’ access to the Internet, and PCs were used predominantly to reinforce language and numeracy skills. By 2010 the Acorn computers had been replaced by PCs. The list of computer skills as was evidenced by a student survey indicated some key programs were used to assist the students in their learning. The teaching staff were quite happy with these programs as they felt these were adequate for their students’ needs. An examination of the school’s technology policy and curriculum showed that the use of ICT was an integral part of the classroom teaching and learning. The school heavily relied on ICT policies and support from the Department of Education, both for network access and software supplies. The administration systems were also provided and supported by the Department. A research project over several years investigated the infrastructure to set up links between different classes at the local level. Given the limited support and availability for video-conferencing by the Education Department’s resources, the attempt was welcomed by relevant school staff. The students showed a tremendous level of enthusiasm and immediate engagement when they began to communicate via the webcams. The main issue was, as expected, the limited bandwidth from the Local Area Network. Another significant issue was the security constraints and filters that are imposed on the Education Department’s VicOne Network. This matter was further investigated in the following year of the research, and it was discovered that the Education Department had found that video-conferencing was not very much in demand for Victorian schools, and hence its support was downgraded considerably. 2.2 Special School-B The vision of this school community encompasses a commitment to achieving excellence in education for students with additional learning needs through a curriculum which integrates learning technologies with best practice in teaching and learning. The values embraced by the school community are: Respect, Personal Best, Happiness, Cooperation, and Honesty. These values are imbedded in the Student Code of Conduct and the Staff, Principal and School Council Codes of Practice.

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Located on two campuses in the northern suburbs of Melbourne, School-B is a day specialist school which caters for students with mild to moderate intellectual disability between the ages of 5 and 18 years. Students come from a wide geographical area and diverse socio-economic backgrounds. The school’s junior annexe (on another site) provides three classrooms for students aged between 5 and 9 years of age. The school itself is situated in attractive, well maintained grounds with excellent facilities which include: a Technology Centre, fully equipped gymnasium, Healthy Living Centre, Art and Craft room, well-resourced library, modern playground equipment, four school excursion buses, computer networking across the school, multi-purpose room and shaded outdoor playing areas. The school, with 71 teaching and ancillary staff, has an enrolment of approximately 250 students. Enrolment is dependent on eligibility criteria as determined by the Department of Education and Training. A significant number of teachers at the school have post-graduate qualifications in special education. School-B supports integration into and from mainstream schools. Support services available to parent/carers and students include social workers, guidance officers, speech therapists and visiting teacher services. The implementation of the Early Years Literacy and Numeracy Program at Junior and Middle school levels has increased opportunities for improved student skills in literacy and numeracy. The Secondary School has a focus on the enhancement of student engagement through Middle Years strategies based on improved Literacy and Numeracy, and the introduction of a ‘Thinking Curriculum’. The Transition Centre caters for students sixteen to eighteen years of age with the focus on the development of dual pathways to cater for the diverse needs of the students. The school has a high level of commitment to a curriculum which integrates learning technologies with best practice in teaching and learning in order to enhance educational outcomes for its students. The Technology Centre has facilitated important opportunities for the school’s students, its staff and for staff from neighbouring schools [9]. In 2006 School-B introduced a number of social and networked learning activities and practices, with software such as Lumil, WordPressMU, ccHost, Urdit, Gregarius and Scuttle. This paper reports on some of the activities undertaken, technologies used and the progress made during this period. For the purposes of this research the individual items of social software used at School-B have been appraised separately, yet in practice these tools and technologies are complimentary and have been used concurrently. In fact, much of the power of social software is its interoperability. By using these tools the school expects its students to create and publish content and respond to the content creation of others. Created content can be aggregated to show progress and richness and depth of learning. Students can respond to the work of others, provide feedback and learn through their interactions with others online. Not only are the students learning, but they are also learning how to be independent learners [1]. Collaborative Web 2.0 technologies and practices strongly support effective knowledge management practices. By using open web-based standards, such as RSS and XML and open API web services, complimentary software can share data in rich and unique ways. In future years, interoperability may well be the key criteria for introducing new technologies and systems as teachers and administrators become more familiar


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with working in networked environments. In introducing the social software it was attempted to integrate with existing practices, using web-based tools and technologies to construct richer tasks for the students. For example, using web-based photo sharing, students and teachers tagged photos which were then used in student digital portfolios. By using this approach not only did they introduce the required skills and practices but the school also reduced the workload for teachers who had previously organised the resources for use in the previous student digital portfolios [1, 9]. By looking at their existing curriculum they identified opportunities to use web collaboration. For example, they found that they could use the social media sharing website ccHost within their loop-based music creation topic. Having traditionally used the audio samples that came with the software, they discovered they could easily integrate online networked learning to increase learning outcomes. Now students can find audio samples based on tags, use these samples in their composition and then share their composition online, highlighting the samples that they used. Other students could then make derivative works by taking samples from the composition of others. The school has attempted to provide all of the social networking web services on their Intranet to ensure that the students’ privacy and security can be carefully monitored.

3 Actors and Networks: Actor-Network Theory Special Schools are complex socio-technical entities and research into their curriculum needs to take account of this complexity. A significant difficulty arises in framing research in a situation like this that involves both technological and human actors. When dealing with the related contributions of both human and non-human actors, actor-network theory [10-12] provides a useful framework. Actor-network theory (ANT) reacts against the idea that characteristics of humans and social organisations exist which distinguish actions from the inanimate behaviour of technological and natural objects, instead offering a socio-technical approach in which neither social nor technical positions are privileged. The actors involved in the adoption of this technology to assist students with special needs include: students, parents, teachers, school principals, school ICT specialist teachers, the School Council, the Web, computers, Education Department policies, learning technology policy, the school environment, classroom environments, learning approaches and paradigms, delivery methods of instruction, engagement methods, thinking processes, technology infrastructure-bandwidth, curriculum, Internet resources, digital libraries and other schools. In an ANT framework, actors are seen to contest and negotiate with each other in an attempt to influence the final outcome in a direction to their own liking. The Education Department, for example, might want ensure that all schools offer a similar level of service to students and to ensure their accountability. The parents of a student with LD, on the other hand, would want the best for their own child regardless of what was going on in other schools. The technology (both hardware and software) itself acts in the way it was designed, both intentionally and unintentionally, to act.

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Fig. 2. Identification of the Actors

A major challenge to schools of this type is to get all these actors to form a common problematisation [10] of their task – to all see the problem in the same way. If this can be achieved then all the actors can work together to achieve a common goal, and ANT offers some ideas on how this might be achieved. ANT considers associations and interactions between human and non-human actors but its proponents make no claim that this approach can do any more than shed a little light on how a given approach is taken or technology is adopted. Despite this, we believe that if a researcher understands how the factors involved in the adoption of a new technology interact then it is possible to affect the outcome by assisting favourable interactions and doing one’s best to reduce unfavourable interactions. The concept of an actor underlies ANT, where an actor is the term used to represent any physical entity that has an effect on the phenomenon under investigation [10, 13]. An actor is considered as any entity able to associate texts, humans, non-humans and money [14]: “Accordingly, it is any entity which more or less successfully defines and builds a world filled by other entities with histories, identities, and interrelationships of their own” [14]. An actor is an abstraction which enables the analysis of


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situations where heterogeneous entities are encountered [13]. The purely social or technological approaches are essentialist and deterministic in nature, whereas ANT is designed to be anti-essentialist and non-deterministic. The concept of an actor allows sociologists to write about the situatedness of innovation and technology without the need to use demarcations separating the social from the natural; or sociological conventions from technological ones. The abstraction frees the analysis from the boundaries of disciplines, thereby allowing the observer to resist the need to reduce complex phenomena to a few well-defined political, social or technological categories [15]. Preliminary identification of actors

Overview of the adoption

'Interview’ actors to gather data New actors or alliances identified Analyse networks and alliances New actors or alliances identified Look for translations

Begin analysis

Look for missing links

Identify ‘key moments’ for ANT treatment Build up whole picture

Final analysis and write up

Fig. 3. The ANT Research Process [16]

The main method advice offered by Latour in conducting an ANT research project is to “Follow the Actors” [11], but this is just a beginning. When using ANT the research process that is followed is far from linear and not just simply a matter of collecting data, analysing the data then writing it up. The process could better be seen as an iterative one, something more like that shown in Fig 3 above [16, 17]. After the actors have been identified and ‘interviewed’ and networks of interactions between actors have been examined, the process continues to look for new actors and for how the technology may have been translated in the process of adoption. This figure attempts to illustrate something of the complexity and iterative nature of this process. A good deal has been written on how ANT can be employed for socio-technical analysis. For example, Bigum [18] provides one interesting application of ANT, while

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Gilding [19] and Tatnall [20] also provide studies applying ANT to Education and Technology. Other examples include the studies of Hull [21], Law [22] and Walsham and Sahay [23]. More recently, the Queensland education department used ANT to investigate the impact of ICT in education [24, 25]. There are also recent examples of how ANT was used to analyse ICT infrastructure [26], financial institutions [27], callcentres [28] and on-line communities [29].

4 Data Collection and Analysis For this research data was collected by attending and observing specific classes at these two schools on a regular basis. The first school was visited in 2003 and 2004 while the second was approached in 2005 and 2006. At School-B a specific project was set up by the school community, and the researcher was asked to take part in this through the assistance and co-operation of the Principal, as well as several key staff members. He became a team member and worked very closely with a class of senior students who were referred to as the Transition Group which showed varied ability and special needs or handicap. The project was called the 1-2-1 project and it was a significant step to providing the state of the art ‘hands-on’ resources to the group. For example, each student was provided with a Laptop to use and carry in class or to take home. The research was conducted in a very supportive manner. It should be noted that funding was also provided by the school of Information Systems at Victoria University and this allowed an analysis of the ICT infrastructure and provided several PDAs along with Dragon digital voice recording equipment. The school gave access to all available data at the time. The students were assessed in the main domains of knowledge and, in particular, data were provided to the research that showed an analysis of the key indicators for each class in ICT for teaching and learning. The 1-2-1 project was later extended to include many other groups in the middle of the school. The original concept by now had gone beyond what anyone could have imagined with the introduction of Social Networking programs. The main observations highlighted the strong self-esteem and engagement by the groups of students. Other data were collected by the ICT coordinator to determine the background of teachers in ICT. In fact at this time, the Victorian Government introduced the ‘ICT Potential Project’ with the aim to diagnose the various ICT skills of teachers in schools and how these were applied in the curriculum. The researcher became an actor himself in this process whilst endeavouring to discover the impact and existence of other actors and actor-networks or ensembles. Using participant observation the researcher became an observer and participant at school meetings and regular class sessions that involved technology and computer programs through the Web. In addition to observations he interviewed these actors and collected relevant documents in order to identify further actors and their networks. The research found that there are several actors whose work is particularly important to the achievement of a good outcome in the school. Firstly, the ICT system, social software and networked learning activities must be appropriate and also they must be induced to co-operate with the students and teachers. This co-operation can be facilitated by another important actor: the ICT specialist teacher who should have


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both a good idea of what is required educationally, and also of the capabilities of students and teachers to understand and use the software and hardware. It does not take much observation to see that the students are readily willing to work with ICT and enjoy using it: they do not need much convincing. Hopefully the other teachers can also see the benefits of using ICT, even if they do not use it themselves, but this is not always the case and this is where another important actor comes in. The School Principal is crucial to the success of this program as without the Principal’s support, many things will not be possible. One area where the Principal can be of considerable use is in influencing, or perhaps coercing other teachers to support the program. Another is with the provision on funds for hardware and software purchases and the provision of time for the ICT coordinator to find out about new products and services. One thing to come out very clearly from our research is the importance of the school Principal and without an actively supportive Principal, there is little chance that the project of using ICT with these children will succeed.

5 Conclusion Our research shows that there is no doubt that the use of ICT can have a beneficial impact on the education of children with learning difficulties. It can do this both by improving their self esteem by providing the means by which they can achieve something they consider worthwhile and also by facilitating the acquisition of useful life skills. To achieve this result however, the actors involved, both human and nonhuman must be induced to work together to produce the desired result. In this, the role of one particular actor is crucial: the School Principal. This actor has the power to provide appropriate funds when required, to coerce his colleagues into working towards a common goal, to encourage the school ICT specialist to make good use of the software and make it easy to use and to reassure the parents that the school is doing its job well. The use of actor-network theory as a research framework facilitates the holistic analysis of schools such as these and how they operate.

References 1. Adam, T., Tatnall, A.: Using ICT to Improve the Education of Students with Learning Disabilities. In: Kendall, M., Samways, B. (eds.) Learning to Live in the Knowledge Society, pp. 63–70. Springer, New York (2008) 2. Adam, T., Tatnall, A.: Building a Virtual Knowledge Community of Schools for Children with Special Needs. In: Abbott, C., Lustigova, Z. (eds.) Information Technologies for Education and Training (iTET), pp. 185–193, ETIC Prague. Charles University, Prague (2007) 3. Adam, T.: Determining an e-learning Model for Students with Learning Disabilities: An Analysis of Web-based Technologies and Curriculum. In: Management and Information Systems, Doctor of Philosophy. Victoria University, Melbourne (2010) 4. Commonwealth of Australia: Literacy for All: The Challenge for Australian Schools. Commonwealth Literacy Policies for Australian Schools. Australian Schooling Monograph Series No. 1/1998, vol. 2010. Commonwealth of Australia, Canberra (1998) 5. Rivalland, J.: Definitions & identification: Who are the children with learning difficulties? Australian Journal of Learning Difficulties 5, 12–16 (2000)

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6. Shaw, S., Grimes, D., Bulman, J.: Educating Slow Learners: Are Charter Schools the Last, Best Hope for Their Educational Success? The Charter Schools Resource Journal 1 (2005) 7. Bulgren, J.: Effectiveness of a concept teaching routine in enhancing the performance of LD students in secondary-level mainstream classes. Learning Disability Quarterly 11 (1998) 8. Agran, M.: Teaching Self-Instructional Skills to Persons with Mental Retardation: A Descriptive and Experimental Analysis. Education and Training of the Mentally Retarded 21, 273–281 (1997) 9. Adam, T., Tatnall, A.: ICT and Inclusion: Students with Special Needs. In: Lloyd-Walker, B., Burgess, S., Manning, K., Tatnall, A. (eds.) The New 21st Century Workplace, pp. 75– 85. Heidelberg Press, Melbourne (2008) 10. Callon, M.: Some Elements of a Sociology of Translation: Domestication of the Scallops and the Fishermen of St. Brieuc Bay. In: Law, J. (ed.) Power, Action & Belief. A New Sociology of Knowledge?, pp. 196–229. Routledge & Kegan Paul, London (1986) 11. Latour, B.: Aramis or the Love of Technology. Harvard University Press, Cambridge (1996) 12. Law, J., Callon, M.: The Life and Death of an Aircraft: A Network Analysis of Technical Change. In: Bijker, W., Law, J. (eds.) Shaping Technology/Building Society: Studies in Sociological Change, pp. 21–52. MIT Press, Cambridge (1992) 13. Law, J.: Notes on the Theory of the Actor-Network: Ordering, Strategy and Heterogeneity. Systems Practice 5, 379–393 (1992) 14. Callon, M.: Techno-Economic Networks and Irreversibility. In: Law, J. (ed.) A Sociology of Monsters. Essays on Power, Technology and Domination, pp. 132–164. Routledge, London (1991) 15. Callon, M.: The Sociology of an Actor-Network: The Case of the Electric Vehicle. In: Callon, M., Law, J., Rip, A. (eds.) Mapping the Dynamics of Science and Technology, pp. 19–34. Macmillan Press, London (1986) 16. Tatnall, A.: Innovation Translation in a University Curriculum. Heidelberg Press, Melbourne (2007) 17. Tatnall, A.: Researching the Adoption and Implementation of Innovative Enterprise Information Systems. In: Cruz-Cunha, M.M., Varajao, J.E.Q., do Amaral, L.A.M. (eds.) Conference on ENTERprise Information Systems (CENTRIS), pp. 245–254. Universidade de Tras-os-Montes e Alto Douro, Ofir (2009) 18. Bigum, C.: Solutions in Search of Educational Problems: Speaking for Computers in Schools. Educational Policy 12, 586–596 (1998) 19. Gilding, T.: Student Construction of a Knowledge-based System as an Actor Network, School of Education, p. 198. Deakin University, Geelong (1997) 20. Tatnall, A.: Innovation and Change in the Information Systems Curriculum of an Australian University: a Socio-Technical Perspective, Doctor of Philosophy. Central Queensland University, Rockhampton (2000) 21. Hull, R.: Actor network and conduct: The discipline and practices of knowledge management. Organization 6, 405–428 (1999) 22. Law, J., Hassard, J.: Actor Network Theory and After. Blackwell Publishers, Oxford (1999) 23. Walsham, G., Sahay, S.: GIS for District-level Administration in India: Problems and Opportunities. MIS Quarterly 23, 39–65 (1999) 24. Simpson, N.: Studying Innovation in Education: the Case of the ConnectEd Project. In: Jeffery, P.L. (ed.) Australian Association for Research in Education (AARE 2000), Sydney (2000)


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25. Australian Government Department of Education, E.a.W.R.: Project to Improve the Learning Outcomes of Students with Disabilities in the Early, Middle and Post Compulsory Years of Schooling. Part 1: Research Objectives, Methodology, Analyses, Outcomes and Findings, and Implications for Classroom Practice Canberra (2007) 26. Cordella, A.: Information Infrastructure: An Actor-Network Perspective. International Journal of Actor-Network Theory and Technological Innovation 2, 27 (2010) 27. Iyamu, T., Roode, D.: The Use of Structuration Theory and Actor Network Theory for Analysis Case Study of a Financial Institution in South Africa. International Journal of Actor-Network Theory and Technological Innovation 2, 1–26 (2010) 28. Naidoo, T.R.: A Socio-Technical Account of an Internet-Based Self-Service Technology Implementation: Why Call-Centres Sometimes ‘Prevail’ in a Multi-Channel Context? International Journal of Actor-Network Theory and Technological Innovation 2, 15–34 (2010) 29. Zammar, N.: Social Network Services: the Science of Building and Maintaining Online Communities, a perspective from Actor-Network Theory. International Journal of ActorNetwork Theory and Technological Innovation 2, 49 (2010)

Use of Graph2Go in M-Learning: A View from the Pedagogical Model Silvia Batista1, Patricia Behar2, and Liliana Passerino2 2

1 Instituto Federal Fluminense Campus Campos-Centro, Rio de Janeiro, Brazil Postgraduate Program in Computer Science in Education - Universidade Federal do Rio Grande do Sul, Brazil {[email protected], [email protected], [email protected]}

Abstract. Characteristics such as interactivity, mobility, reaching a higher number of people, learning in real contexts, among others, are considered advantages of using mobile devices in education. M-learning (mobile learning) can favor learning, not only in distance learning, but also in face to face and blended learning. This paper particularly focuses on mathematical learning, considering it can benefit from m-learning potential in several educational modalities. However, this requires structured actions and, in this sense, development of pedagogical models is important. Such models serve as base for the knowledge building process through organized actions, with defined objectives and established strategies to reach them. Within this context, this paper presents a pedagogical model that was built and applied in a pilot study for mathematical learning using Graph2Go in college students. Keywords: M-learning, Mathematics, Pedagogical Models, Graph2Go.

1 Introduction Popularization of mobile devices and wireless Internet access networks has contributed to changes in social practices and to how information is produced and accessed. In this society, which can be understood as the Mobile Communication Society, time and space are the aspects more directly expressing such social change [1]. Within this context, wireless mobile technologies have also raised interest in educational terms. M-learning (mobile learning) is a field that comprehends wireless technologies and mobile computing to enable learning to occur at any time and place, maximizing the student’s freedom [2]. M-learning is not only a matter of learning or mobility, but a totally different concept, which is part of a new conception of mobility of a connected society [3]. Several studies have been published on m-learning, such as those related to learning environments for mobile devices [4, 5, 6] and those aiming at the development of didactical materials [7, 8, 9]. In particular, studies have tried to analyze how m-learning can be useful to the teaching and learning process of Mathematics [10, 11, 12]. In general, the studies above indicate several advantages of using mobile devices in mathematical learning, N. Reynolds and M. Turcsányi-Szabó (Eds.): KCKS 2010, IFIP AICT 324, pp. 12–22, 2010. © IFIP International Federation for Information Processing 2010

Use of Graph2Go in M-Learning: A View from the Pedagogical Model

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such as: i) dynamic visualization and investigation of mathematical facts; ii) different forms of approaching concepts (for example, through videos, working on visual approaches); iii) autonomy in the study of mathematical themes; iv) learning in real situations. Applications for mobile devices have been developed, specifically for Mathematics, such as those designed by the Math4Mobile project.1 Among these applications is Graph2Go, which allows establishing connections between graphic and algebraic representation for a given set of functions through dynamic changes. Development of such applications is essential, but it is also necessary to organize forms of applying them in varied educational modalities (face to face, blended and distance learning). In this sense, development and analysis of pedagogical models for this purpose are important. Such models allow organizing pedagogical practices, making them more objective. Based on a pedagogical model, actions can be structured with defined objectives and established strategies to reach them. Thus, this paper presents a pedagogical model built and applied in a face to face pilot study, using Graph2Go in an m-learning experiment with college students. Section 2 defines what is understood here as pedagogical model, stressing its importance. Section 3 discusses the field of m-learning application in several educational modalities, particularly focusing on mathematical learning. Section 4 describes the pilot study and the pedagogical model applied in its development. Section 5 presents analysis of the pilot study using data obtained from observation and a questionnaire. Finally, section 6 brings some considerations on the present paper.

2 Pedagogical Models A model is a shared mental representation of a set of relations defining a phenomenon, aiming at a better understanding of it [13]. The concept of model is the foundation for scientific activity, enabling comparison, simulation and understanding of phenomena. In education, such concept has been wrongly considered a synonym for learning theory or teaching methodology. Although a pedagogical model can be based on one or more learning theories, they are generally “reinterpretations” of such theories, based on individual conceptions by teachers [13]. A pedagogical model is “a system of theoretical premises that represent, explain and guide the way the curriculum is approached and that is consolidated in the pedagogical practices and in the interactions professor-student-object of knowledge” [13], p. 4. According to Dabbagh [14], pedagogical models are cognitive models or theoretical constructions derived from models of knowledge acquisition or conception about cognition and knowledge, which in turn are the base for learning theories. Figure 1 shows the structure of a pedagogical model, based by one (or possibly more than one) learning theory [15]. According to Behar [15], p. 25, a model is composed by a pedagogical architecture (PA) and strategies for its application. PA is the main structure of a model, in which organizational aspects, study content or object, methodological and technological aspects are included. 1

Math4Mobile is a project of the Institute for Alternatives in Education that operates within the Faculty of Education at the University of Haifa, Israel (http://www.math4mobile.com/).

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S. Batista, P. Behar, and L. Passerino

Fig. 1. Structure of a pedagogical model. Source: Behar [15] - p. 25 - adapted with permission from the author.

Putting a PA into practice requires strategies. They are dependant on the real application context and are influenced by several variables that permeate the educational process. Strategies for PA application are the dynamics of a pedagogical model [15]. They are didactical actions focused on articulation and adaptation of a PA into a given context. Therefore, an application strategy is how the teacher will put PA into practice, already adapted to its reality according to the adopted learning theory [15]. Although the focus discussed by Behar [15] is distance learning, and the model elements are explained from the model itself, it is possible to widen the focus, analyzing them in general terms. In the present study, PA elements were adapted to meet all educational modalities: •

• • •

Organizational aspects: comprehend the basis of a pedagogical planning/proposal and include the purposes of the teaching and learning process, organization of time and space and expectations relative to the participant’s interaction; Aspects regarding study content or object: include didactical materials to be used (texts, booklets, didactical resources, either digital or not); Methodological aspects: include activities, interaction/communication forms, evaluation procedures and organization of such elements into a didactical sequence that favors learning; Technological aspects: refer to the technological support structure to be used in activities (for example, selection of virtual learning environments, communication tools, inclusion of mobile devices, among others).


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Such aspects should not be considered separately, since they form a whole in which a decision about an aspect influences the others. In addition, it is important to consider that the same PA can be applied differently, depending on the strategies used. Application strategies make PA individualized, as they depend on the actors’ personal variables. Development of pedagogical models allows confluence of aspects related to content and organizational, methodological and technological elements, in favor of pedagogical actions with more defined purposes. Pedagogical actions with a better foundation, clear objectives and organized means of achieving them can enable a more adequate use of resources, more coherence with the real context and reduction in unpredictable situations. This study defends the idea that mobile devices can favor not only distance learning, but also face to face and blended learning. In this sense, pedagogical models including use of these devices can be important in the structuring of activities to be promoted in several modalities. Sections 3 and 4 deal with these respective issues.

3 M-Learning and Mathematics: Educational Modalities Several studies associated m-learning with e-learning. Quinn [16] states that mlearning is e-learning2 developed by mobile devices. In agreement with that, Georgiev et al. [17] claim that m-learning can be understood as a new stage in distance learning and e-learning. Wains and Mahmood [3] also state that m-learning aims at meeting some aspects that still impair e-learning, such as lack of infrastructure of Internet access in developing countries and the issue of student mobility. However, some studies have associated m-learning with face to face and blended learning as well [11, 12, 18, 19]. According to Khaddage and Lattemann [18], it is possible to use mobile devices within the classroom context, favoring aspects such as accessibility, collaboration and flexibility. To Zeiller [19], blended learning can use both Web resources and mobile devices in the communication between students and teachers and in collaborative work. Thus, association of m-learning with e-learning no longer reproduces the real context. Considering that research has indicated that m-learning potentiality can also collaborate in face to face and blended learning, this study proposes a wider mapping (Figure 2). In that figure, two-way arrows between m-learning and educational modalities indicate there is benefit in both senses. Studies involving m-learning collaborate to the development of this area, either by identification of new application forms, limitations and advantages, or by increase and enhancement of resources. Within the context of Figure 2, educational modalities are defined as: i) Face to Face Learning - modality in which teacher and students are physically present at the same place, at a preestablished time, to perform pedagogical activities; ii) Distance Learning - modality “[...] in which didactical-pedagogical mediation in teaching and learning processes takes place with use of information and communication means and technologies, with students and teachers developing educational activities at varied places or times” [20], p.1; iii) Blended Learning - modality that aims at combining two educational modalities – face to face and blended learning – into one, supported by digital technologies. 2

Form of distance learning based on computer and the Internet.

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Fig. 2. M-learning: educational modalities

In particular, the teaching and learning process of Mathematics requires forms of making it more accessible to students. Mathematics taught at schools often plays an excluding role, labeling and classifying people as able or unable to participate in society's decision-making processes [21]. In this sense, taking into account the justifications for Figure 2, which point out that m-learning has a potential to contribute to learning in several educational modalities, this study proposes the mapping presented in Figure 3. It is an adaptation of that proposed in Figure 2 to the context of Mathematics.

Fig. 3. M-learning in mathematical learning: educational modalities

It is important that further studies include all three modalities (Figure 3), aiming to analyze the importance of m-learning for the teaching and learning process of Mathematics. In this sense, stress should be given to the need of developing pedagogical models on which to base the knowledge building process in Mathematics through m-learning. Such models, as discussed in section 2, aim at structuring pedagogical practices, defining and outlining several aspects involved in the process.


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4 Pilot Study Using Graph2Go: Building a Pedagogical Model A pilot study using the Graph2Go3 application was performed with the aim of developing and analyzing a pedagogical model involving use of mobile devices at a face to face experiment. This application works as a graphic calculator for a set of functions, allowing to establish connections between graphic and algebraic representation through dynamic changes. The pilot study using Graph2Go was held in November 2009 and it lasted 4 hours. It was performed as part of a research project,4 and the target audience was college students at Instituto Federal Fluminense Campus Campos-Centro (RJ, Brazil): eight students of Control and Industrial Automation Engineering (1st semester), and four undergraduate students in Mathematics (2nd semester).

Fig. 4. Pedagogical model – pilot study using Graph2Go

The pedagogical model (Figure 4) aimed at setting the foundation for elements that would be included in the experiment, making it more structured and better defined. Answering questions in several aspects of the pedagogical model implies analyzing

3

4

Free application (for non-commercial purposes), specific for cell phones, developed by Michal Yerushalmy and Arik Weizman - project Math4Mobile (http://www.math4mobile.com/). Project “Information Technologies and Communication in the Teaching and Learning Process of Mathematics” (http://www.es.cefetcampos.br/softmat/projeto_TIC/portal.html).

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and making decisions that would not often be considered in advance. Such procedures might reflect into safer actions and a lower number of unexpected situations. As shown in Figure 4, the social-historical theory was adopted as theoretical background, providing the basis for the entire model. In this theory, mediation includes use of instruments and signs within the social context, and the combination of use for such resources allows development of superior psychological processes [22]. In addition to instruments and signs, the role of a human mediator, encouraging reflections through assistance, is also essential for student autonomy and knowledge appropriation [23]. The pilot study, defined in organizational aspects, aimed at providing the study of graphic changes of functions using Graph2Go. The target audience, as mentioned earlier, was composed by college students. The booklet5 of activities, mentioned in the content aspect, is divided into three sections: the first has general information on Graph2Go and instructions of use; the second brings activities to study changes in graphs of quadratic functions through changes in coefficients; and the third provides a similar study to that performed with quadratic functions, but involving the sine function. To encourage cooperative work, teacher-mediated pair activities were proposed, providing questions and reflections (methodological aspects). Interaction between individuals, according to the social-historical theory, plays an essential role in human development. A particular type of interaction stands out in this paper – mediation. Mediation takes place in the Zone of Proximal Development (ZPD - distance between real and potential development levels, determined by problem solving supervised by an adult or in collaboration with more skilled partners) and includes use of tools and signs within the social context [22]. Digital technologies are mediating tools that might contribute to creation of ZPDs, in which context Graph2Go is inserted. With regard to technological aspects, mobile devices (cell phones) were chosen with pedagogical purposes (m-learning). Transfer of the application into the students’ cell phone was performed via Bluetooth using a laptop equipped with such technology. Direct transfer of the application, from the Internet to cell phones, would be more practical, but it was avoided due to connection and download costs (the application itself is free). When the participants were selected for the study, they were told it was necessary to have cell phones with Bluetooth and J2ME platform (required by Graph2Go). As mentioned in section 2, putting a pedagogical architecture into practice demands certain strategies. A brief description on how the proposed architecture was effectively applied is provided here. First, the application was transferred. At that time, there were a few problems: one participant who thought he had J2ME in his cell phone did not actually have it, which prevented execution of the application. Furthermore, although all cell phones had Bluetooth, some transfers were only possible through memory card. Thus, two other participants whose cell phones had the necessary requirements, but no memory card, could not have the application due to transfer problems. After the initial stage, nine out of 12 students had the application in their cell phones. However, since the work was performed in pairs, development of activities was not impaired by this problem. The first section of the booklet, which explained Graph2Go features, was then discussed with the students. They had no difficulty 5

Available at: < http://www.es.cefetcampos.br/softmat/projeto_TIC/atividades.html>.


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understanding the application resources, nor using the keys. Next, activities related to graphic changes were performed, after the importance of such theme to learn integral calculus had been highlighted. Having analyzed the graphic influence of changing coefficients in quadratic functions, students performed a similar analysis relative to the sine function. Both analyses were then commented and, based on the perception of association between them, some generalizations were discussed. The following section brings an analysis of the experiment using data obtained from observation of the students’ actions and from a questionnaire.

5 Pilot Study: Analysis of Results In general, the students actively participated in proposed activities. Questions to the teacher – the process mediator – and exchanges with classmates showed their interest in what was being studied. There were no comments or actions that showed difficulties in using the application, and understanding of the issue was achieved. To ensure a deeper qualitative analysis of the experiment, a seven-item questionnaire was organized, with six semi-open questions (including request of comments) and one open question. The questionnaire aimed at collecting data on the application’s learning facility, advantages and disadvantages, importance for learning the theme and the role of classmate’s and teacher’s mediation. In general, data were collected on several aspects of the proposed pedagogical model. None of the participants had used an application for cell phone in the study of an educational theme (not restricted to Mathematics). Even so, 75% of the participants reported being “easy” to learn the Graph2Go resources, and the remaining considered it as “very easy.” Comments generally stressed the fact that the application is well organized, user-friendly, with intuitive, simple and objective resources. The percentage confirms what was mentioned about not having any evidence of difficulty by the participants when using the application. They all considered that the booklet activities, developed with the aid of Graph2Go, collaborated to the understanding of the theme under study. The possibility of visualizing the variation of function graphic behavior by changing coefficients was the most stressed aspect by the participants. Such data reinforces the importance of using tools in the individual’s development, as supported by the social-historical theory. One of the questions was about the advantages and disadvantages of using Graph2Go. In general, advantages included visualization and movement, easy to understand and use, practicability, mobility, being free, graphs for the derivative and integral function (with possibility of changing the integration constant). Disadvantages were limitation of options of functions; impossibility to draw graphs of different functions on the same screen; language (the application is in English, with which not all students were familiar); size of graphs. It is important to analyze the potential and limitations of a didactical resource. As proposed by Cabero [24], thought should be given to adequacy of a given didactical resource to planned objectives, students’ characteristics and adopted pedagogical proposal. With regard to use of applications for cell phones for mathematical learning, 67% of the participants considered it “very important” and the remaining classified it as

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“important.” It was stressed that, with cell phone popularization, such device is a resource that can be useful for learning. In addition, in terms of face to face learning, use of these applications would eliminate the need of going to computer laboratories. The rates are in agreement with studies on m-learning in the process of Mathematics teaching and learning [10, 11, 12]. Classmates’ collaboration during activities using Graph2Go was considered as “very important” by 58% of the participants, while the others considered it as “important.” In general, interaction between students was stressed as being significant to discuss proposed concepts and to better understand them. The participants’ view is coherent with what is proposed by Vygotsky [22]. According to that author, shared activity is important for cognitive development, as it enables living in the external level what will be further internalized. Moysés [25] reinforces that by claiming that learning of Mathematics should always include such interpersonal action. Teacher’s role as a mediator during activities using Graph2Go was classified as “very important” by 75% of the participants; for 17%, it was “important,” and for one participant (8%) it was “little important.” The participants stressed that, although the application was easy to understand and use and activities were clear, the teacher’s work of making students think about the concepts was essential. The justification of the student who classified it as “little important” is as follows: Little important because it is possible to understand the application without the teacher’s help, although extremely important to help with the mathematical thought (Participant 4).

Therefore, even the participant who considered it as “little important” stressed the teacher’s role as a mediator, as supported by the social-historical theory. Mediated learning goes through an active participation of a more experienced partner that selects, modifies and interprets context conditions present in the learning process of another subject that is less experienced [26]. It should be stressed that, although short, the experiment was significant. In this sense, it was possible to notice the importance of developing a pedagogical model to enable more efficient actions. The pilot study showed that: i) the difference between available devices and resources in cell phones is a setback for their use with educational purposes; ii) Internet-related costs are another factor making use of mobile devices difficult; iii) the participants’ skill to deal with cell phone keys is quite favorable; iv) practicability of cell phone use (very similar to how a calculator is used, being available when needed) is a very interesting aspect in educational terms; v) it is important that pedagogical models take into account the role of teacher and classmate mediation in the learning process.

6 Final Considerations In Mathematics, digital technologies create possibilities, allowing simulations, visualizations, experiments, hypothesis generation, among other actions. M-learning adds extra possibilities, such as practicability, mobility, reaching a higher number of people, learning in real contexts, among others. In this sense, use of mobile devices, specific applications or even general resources, such as text messages, photographs, videos, among others, can favor the process of Mathematics teaching and learning, making it more accessible and closer to the student’s reality.


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Studies indicate that m-learning has potentialities that might enrich the teaching and learning in several educational modalities, going beyond the initial focus on distance learning. In this sense, studies on m-learning and Mathematics should deal with distance, face to face, and blended learning. Such studies can favor not only mathematical learning, but also the very evolution of m-learning through developed resources, identification of forms of use and feedback. Pedagogical models considering use of mobile devices in varied educational modalities can favor more structured practices of m-learning. Development of these models can allow a more consistent foundation and organization of pedagogical practices and a better use of resources, in this case, mobile devices. Analysis of the pedagogical model in the pilot study pointed to a profitable use by the students, taking into account their actions and comments and the data collected by the questionnaire. The model allowed identification of difficulties and stressed potentialities in the use of cell phones with educational purposes.

References 1. Castells, M., Fernandes-Ardevol, M., Qiu, J.L., Sey, A.: The Mobile Communication Society: a across-cultural analysis of available evidence on the social uses of wireless communication technology. In: International Workshop on Wireless Communication Policies and Prospects: a Global Perspective (2004), http://arnic.info/workshop04/MCS.pdf 2. Wains, S.I., Mahmood, W.: Integrating m-learning with e-learning. In: 9th Conference on Information Technology Education, pp. 31–38. ACM, New York (2008) 3. Traxler, J.: Current State of Mobile Learning. In: Ally, M. (ed.) Mobile Learning: transforming the Delivery of Education and Training. AU Press, Canada (2009) 4. Meisenberger, M.: MLE - Mobile Learning Engine. FH Joanneum - University of Applied Sciences, Graz, Austria, Diploma Thesis (2004) 5. Brown, R., Ryu, H., Parsons, D.: Mobile helper for university students: a design for a mobile learning environment. In: 18th Conference on Computer-Human Interaction-Design: Activities, Artefacts and Environments, pp. 297–300. ACM, New York (2006) 6. Tesoriero, R., Fardoun, H., Gallud, J., Lozano, M., Penichet, V.: Interactive learning panels. In: Jacko, J.A. (ed.) HCI 2009. LNCS, vol. 5613, pp. 236–245. Springer, Heidelberg (2009) 7. Costello, F.: The use of Flash Lite and Web authoring tools in mobile learning course design. In: Keegan, D. (org.) Mobile learning: a practical guide, pp. 79–91 (2007), http://www.ericsson.com/ericsson/corpinfo/programs/ incorporating_mobile_learning_into_mainstream_education/ 8. Costabile, M.F., De Angeli, A., Lanzilotti, R., Ardito, C., Buono, P., Pederson, T.: Explore! Possibilities and Challenges of Mobile Learning. In: 26th Conference on Human Factors in Computing Systems, pp. 145–154. ACM, New York (2008) 9. Sá, M., Carriço, L.: Supporting End-User Development of Personalized Mobile Learning Tools. In: Jacko, J.A. (ed.) HCI 2009. LNCS, vol. 5613, pp. 217–225. Springer, Heidelberg (2009) 10. Botzer, G., Yerushalmy, M.: Mobile application for mobile learning. In: International Conference on Cognition and Exploratory Learning in Digital Age (2007), http://www.iadis.net/dl/final_uploads/200714C043.pdf

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11. Franklin, T., Peng, L.-W.: Mobile math: math educators and students engage in mobile learning. Journal of Computing in Higher Education 20(2), 69–80 (2008) 12. Nokia: Mobile learning for Mathematics (2009), http://www.nokia.com/ corporate-responsibility/society/mobile-technology-fordevelopment/mobile-learning-for-mathematics 13. Behar, P.A., Passerino, L.M., Bernardi, M.: Modelos Pedagógicos para Educação a Distância: pressupostos teóricos para a construção de objetos de aprendizagem (Pedagogical Models for Distance Learning: theoretical background for the construction of learning objects). Revista Novas Tecnologias na Educação 5, 1–12 (2007) 14. Dabbagh, N.: Pedagogical models for E-Learning: a theory-based design framework. International Journal of Technology in Teaching and Learning 1(1), 25–44 (2005) 15. Behar, P.A. (org.): Modelos Pedagógicos em Educação a Distância, Pedagogical Models for Distance Learning. ArtMed, Porto Alegre (2009) 16. Quinn, C.: mLearning: mobile, wireless, in-your-pocket learning (2000), http://www.linezine.com/2.1/features/cqmmwiyp.htm 17. Georgiev, T., Georgieva, E., Smrikarov, A.: M-learning - a new stage of e-learning. In: 5th International Conference Computer Systems and Technologies, pp. 1–5. ACM, New York (2004) 18. Khaddage, F., Lattemann, C.: Towards an ad-hoc mobile social learning network using mobile phones. In: 12th Interactive Computer Aided Learning, pp. 374–380. Fachhochschule Kärnten, Villach (2009) 19. Zeiller, M.: Podcasting-based mobile learning in blended learning courses. In: 12th Interactive Computer Aided Learning, pp. 518–527. Fachhochschule Kärnten, Villach (2009) 20. Brasil.: Decreto n° 5.622 (Decree n° 5.622) (2005), http://portal.mec.gov.br/seed/arquivos/pdf/dec_5622.pdf 21. Matos, J.F.L.: Matemática, educação e desenvolvimento social – questionando mitos que sustentam opções atuais em desenvolvimento curricular em Matemática, Mathematics, education and social development - challenging myths that sustain current options in curriculum development in Mathematics (2005), http://www.educ.fc.ul.pt/ docentes/jfmatos/comunicacoes.html 22. Vygotsky, L.S.: Mind in Society: The Development of Higher Psychological Process. President and Fellows of Harvard College, USA (1978) 23. Passerino, L.M., Santarosa, L.M.C.: Autism and Digital Learning Environments: Processes of interaction and mediation. Computers & Education 51, 385–402 (2008) 24. Cabero, J.: Avaliar para melhorar: meios e materiais de ensino, Evaluation for improvement: methods and teaching materials. In: Sancho, J.M. (Org.) Para uma Tecnologia Educacional, pp. 257–284. ArtMed, Porto Alegre (1998) 25. Moysés, L.: Aplicações de Vygotsky à Educação Matemática, Application of Vygotsky to Mathematics Education, 8th edn., Papirus, Campinas-SP, Brasil (2007) 26. Gindis, B.: Remediation through Education: Sociocultural Theory and Children with Special Needs. In: Kozulin, A., Gindis, B., Ageyev, V.S., Miller, S.M. (eds.) Vygotsky’s Educational Theory in Cultural Context, pp. 200–221. Cambridge University Press, New York (2003)

ICT in Teacher Education: Developing Key Competencies in Face-to-Face and Distance Learning Ana A. Carvalho University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal [email protected]

Abstract. This paper reports the training of 56 Teachers in ICT and Education Course. This course included one week face-to-face and seven weeks in distance learning. Students developed several ICT competencies during theses weeks, reporting its use in their classes. A characterization of the subjects’ digital literacy is presented, as well as their participation in chat sessions and in the forum, which were not mandatory but highly recommended. The majority of students did not participate in the synchronous and asynchronous communication facilities. However, those that participated in the chat sessions and forum debates completed all tasks and assignments. The students recognized the importance of learning about ICT and how it changed their teaching methods. Keywords: ICT, Key Competencies, WebQuest, Treasure Hunt, Concept maps, Podcast.

1 Introduction This paper presents the challenge of motivating teachers to use and reflect about ICT, particularly Web 2.0 tools in teaching and learning, during a face to face week and seven weeks of online learning. The key competencies emphasized within this course are particularly related to ICT but also to the three broad categories identified by the DeSeCo Project’s conceptual framework for key competencies: use tools interactively, interact in heterogeneous groups, and act autonomously [1]. The UNESCO’s project “ICT Competency Standards for Teachers” stressed that schools and classrooms “must have teachers who are equipped with technology resources and skills and who can effectively teach the necessary subject matter content while incorporating technology concepts and skills” (p. 1) [2]. The ICT and Education Course belongs to a training program -“Online Teaching and Learning & Professional Development of Teachers in the Republic of the Maldives” - supported by UNICEF Maldives and developed by the University of Minho, in Portugal, during the 2008 and 2009 academic years. It included four compulsory courses and three optional courses (Table 1). The compulsory courses lasted 80 hours, during eight weeks and the optional courses 36 hours during three weeks. All courses used the LMS (Learning Management System) Blackboard and were distance learning courses, but Teaching/ Teacher Education, Supervision and Monitoring, and ICT N. Reynolds and M. Turcsányi-Szabó (Eds.): KCKS 2010, IFIP AICT 324, pp. 23–34, 2010. © IFIP International Federation for Information Processing 2010

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and Education courses had a face to face week in Male, Maldives, in May and seven weeks online. The face to face week was a requirement of UNICEF Maldives. The full program lasted 408 hours. Eighteen Portuguese lecturers participated in this training program along with fiftysix Maldivian’ teachers, who needed to update their knowledge about teaching and ICT. The language of the program was English, a foreign language for both lecturers and students. Table 1. Training Program Structure Compulsory and Optional Course Units Compulsory Course Units Duration: 80 hours each at 10hrs/week

Course Units

Optional Course Units Duration: 36 hours each at 12hrs/week

A

-School Administration -School Management

B

-Change Management in Education -Educational Management and Leadership

C

-Behaviour Management -Special Educational Needs

-Teaching/ Teacher Education -Supervision and Monitoring -ICT and Education -Online and Distance Education

During the five face to face sessions in Male, students attended three two-hour lectures daily. The first went, from 8am to 10am, the second from 10.30am to 12.30pm, and the third from 2pm to 4pm. At the University of Minho we suggested dividing the students into three groups thus giving them more personalized feedback and orientation during the activities. When in Male, we realized that this purpose was fundamental, not only to ICT as each student had access to a computer, but also to the other two courses as most students lack technical and scientific knowledge.

2 ICT and Education Course The ICT and Education Course had four learning outcomes: assess website quality, use Web tools (blogs, podcasts, and Google pages), distinguish collaboration from cooperation and the implications for students’ group work, and select and use WebQuests and Treasure Hunts with students. The syllabus attempted to familiarize students with technical words, tools, and pedagogical uses of resources available online. Concepts such as the Internet, the Web, Web 2.0 tools, social networking, connectivism, synchronous and asynchronous communication, collaboration and cooperation, searching the Web, criteria for evaluating websites, quoting and plagiarism, editing online, concept maps, WebQuest, and Treasure Hunt were approached and their pedagogical benefits discussed.

ICT in Teacher Education: Developing Key Competencies

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The main focus of this course unit was to motivate students to use and integrate ICT appropriately in the classes they teach. Theoretical aspects were discussed in the forum but the main purpose of all discussions was a reflection about its use in teaching contexts. We intended that our students applied the tools (blogs, podcasts, concept maps, etc.) and activities (WebQuest, Treasure Hunt) with their own students reporting their difficulties, enjoyments and reflections regarding its use. We also intended that students work in groups in their own school to experience collaborative and cooperative activities. 2.1 Course Structure As mentioned previously, this course had a face to face week in May 2008, 2 hours per day with each group of students (Table 2). This option proved to be appropriate for students as each one had computer access; however it was somewhat tiring for the tutor. She had six hours per day every day during the week, from Sunday to Thursday. The other 7 weeks online only took place in March and April of the following year. Every week possessed the same structure: a new topic was introduced with readings and activities, there were two chat sessions at 8pm for students (and at 4pm for the tutor). They also had one or two threads in the forum helping them to reflect about the readings and activities of the week. Table 2. ICT and Education Course Structure Course duration Face to face week [May 2008]

Course structure -Participants characterization -Introduction to ICT -Introduction to Blackboard facilities -Create a blog and post comments to their colleagues -Create a podcast (audio) -New literacies.

7 weeks online LMS: Blackboard [March-April 2009]

Each week: -A new topic was introduced -2 chat sessions (1 hour each) -Forum (1 or 2 threads)

Not all of the Maldivian teachers had Internet access at home therefore; they had to go to school at night if they wished to participate in the chat sessions. The time of the chat sessions was based on students’ preference.

3 Students’ ICT Characterization During the first face to face session, students filled out a questionnaire regarding digital literacy. It included information about their gender and age, followed by eight questions: (1) their knowledge about online tools or resources , (2) if they had a blog, podcast or website; (3) if they used some of the tools or resources listed in item 1; (4)

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if their school had a website; (5) frequency of access to Internet resources with their students; (6) if the teachers publish their students’ work online; and finally if they have a computer at home with Internet connection. The questionnaire was filled in by 56 students, 32 male and 24 female. Most of the students (39) were aged between 31 to 40 years old, 12 between 20 and 30, and 5 were older than 41. They were very interested in participating in this training program and were selected by the Maldivian Education Department. They were from different atolls, but a small group of two to five students belonged to the same atoll to support each other and to work collaboratively. All of them knew Google, 48% YouTube, 34% blog, 27% Movie Maker, 20% Wiki and Flickr, 16% hi5, 9% Podcast, 7% WebQuest, 5% Treasure Hunt, and one individual knew Second Life and Delicious (Table 3). These results showed that the students required an ICT update. Table 3. ICT tools or resources known by students (n=56) ICT tools or Resources Google Blog Podcast Wiki YouTube Flickr Delicious Hi5 Movie Maker WebQuest Treasure Hunt Second Life

f 56 19 5 11 27 11 1 9 15 4 3 1

% 100 34 9 20 48 20 2 16 27 7 5 2

When inquired if they had blog, podcast or website, only a few answered positively (Table 4). Only 11% had a blog, 9% a website, and 5% a podcast. Table 4. Students‘ presence online (n=56) Web 2.0 tools and website Blog Podcast Website

f 6 3 5

% 11 5 9

When asked if they used the tools or resources presented in table 3 with their students, only 30 participants answered affirmatively (Table 5). From those (30), almost all of them (29) use Google, 4 students WebQuests, 2 blogs, and one YouTube and Treasure Hunt. These results evidenced the need to update these teachers. This need to update teachers to ICT competencies is supported by UNESCO [2] and by authors such as Richardson [3] or Siemens [4].


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Table 5. Tools and resources used in classes (n=30) Tools and resources used in class Google WebQuest Blog Treasure Hunt YouTube

f

%

29 4 2 1 1

97 13 7 3 3

With regard to a school website, 22 of the participants indicated that their school had one. A large group of teachers (46%) rarely used the Internet with their students, and 11 never did (Table 6). Only 7 used the Internet daily, 8 weekly, and 4 monthly. Later on they explained that some schools had a computer lab but most of them only contained one or two computers for staff use. The government was sensitive to this problem and was making an effort to update the Internet connection and buy computers for the schools. This situation occurred in other countries such as Malaysia reported by Kader [5] and Bhutan by Won [6]. Table 6. Using Internet resources with students (n=56) Frequency of Internet use with students Daily Weekly Monthly Rarely Never

f

%

7 8 4 26 11

13 14 7 46 20

Only 5 participants published their students’ work online. Finally, we asked them if they had a computer at home with an Internet connection (Table 7) because this was a prerequisite for the courses online. As we may verify in Table 7, 93% of the participants had a computer at home, but only 71% had an Internet connection. Some of them had to use school facilities during the course, particularly for the chat sessions at late evening. Table 7. Computer and Internet connection at home (n=56) Computer and Internet connection at home Computer Internet connection

f

%

52 40

93 71

4 The Face to Face Week The students completed a questionnaire regarding Digital Literacy. They were introduced to the ICT and Education course and its learning outcomes. Particular emphasis

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had been placed on the weekly goals that intended to create a new framework about learning methods and Web 2.0 tools, such as blogs and podcast. Students had to distinguish Internet and World Wide Web, create their own blog and podcast, and reflect about the implications of new literacies for learning (Table 8). Simultaneously, it was very important that they felt comfortable and familiar with the LMS Blackboard thus, every day they had something to do to remember its functionalities. The technical preparation for distance learning as well as the comprehension of the role of a distance learner is very important. Mason [7] states: “Those who have poor study habits, lack self-discipline or motivation, have been educationally disadvantaged, or are driven almost solely by extrinsic reasons for wanting a degree, tend to find the student-centred pedagogy bewildering, too demanding, or too much hard work. (…) Students do need a gradual process of learning to be self-directed. They need training and practice in ICT skills such as research, analysis and management of web-based resources; they need a student-friendly online environment that encourages and rewards interaction. They also need supportive tutoring to help them adapt their study patterns from linear working through textbooks and lecture notes to interactive engagement with ideas, resources and other students” (p. 65-66). Table 8. Contents of the first week Session 1

First week contents Internet and World Wide Web. Blackboard: an introduction.

2

Blog: features, and learning implications. Create a blog. Blackboard: Chat.

3

Podcast: characteristics, and implications in learning. Create a podcast. Blackboard: Discussion board (forum).

4

Record a text in Audacity and save it as mp3. Post it in Podomatic. Insert a picture.

5

New literacies and its effect in learning. Blackboard: upload a file to Discussion Board, Digital Dropbox, and Group Pages.

The students were very interested. For some, everything was new and a few were unable to distinguish an e-mail address from a URL. One of the problems that occurred when they created a blog was to remember its URL and password. They were able to upload a picture, embed a video, and incorporate a link into blog posts. I used Richardson´s [3] and Siemens’ [8] ideas about the use of blogs in educational contexts. “Blogging is a genre that engages students and adults in a process of thinking in words, not simply an accounting of the day’s events and feelings” (p. 20) [3]. It is important that blogs be used as places of critical thinking and analytical writing and


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reflection. They can be used as class portals, e-portfolios, collaborative space, etc. Richardson [3] also considered that there is no better way to understand the impact of the Read/Write Web than by becoming a part of it. This was one of the main purposes of the week. The podcasts were introduced, mentioning several ways to use them in education. For example, to record local stories or historical events, to challenge students to do a group project, to invite students to report their group work, to summarize content, and so on. The podcasts created by the teacher or the students have a good impact on listeners, as reported by studies conducted by Carvalho et al. [9], [10], Salmon et al. [11] and Frydenberg [12]. Students like hearing their teachers’ voice as stated by Durbridge [13], Richardson [3] and Carvalho et al. [14]. All students created their first podcast using Podomatic. In the following session, the software Audacity was introduced and students recorded their episode. Next, they uploaded it to Podomatic. In the class, it was referred again “that’s what the Read/Write Web is all about: being able to share what you create with others” (p. 118) [3]. During the last face to face session, we approached the new literacies as a concluding session of the topics focused. We stressed that for more than a hundred years we have defined being literate as being able to read and to write, although those core abilities are central to learning, they are no longer sufficient to ensure understanding [3]. Writing is no longer limited to text, we can use words, audio, video, and photography. It is difficult to deny that more and more we have become a multimedia society. We can combine many forms of writing into a process of “rip, mix, and learn”, taking a piece of content here and another there, combining it to produce powerful text and nontext messages and interpretations [15]. Readers of the Web content must learn to be active consumers instead of just passively accepting it as legitimate. The classroom of the Read/Write Web is one seamless transfer of information, of collaborative, individualized learning, of active participation by all class members. It is marked by the continuous process of creating and sharing content with wide audiences. Our students are learning that their voices matter, that people are reading and responding, and that their ideas count. Moreover, when writing, for example in the blog, “ideas are presented as the starting point for a dialogue, not the ending point” [8]. By inviting students to become active participants in their own learning, we teach them how to be active participants in their lives and future careers. Teachers will have to see themselves as connectors, of content and of people. To use Web 2.0 tools effectively, educators must learn to use them effectively. They need to become bloggers and podcasters, to use other social tools effectively with their students. Teachers need to think of themselves as coaches who model the skills that students need to be successful and motivate them to strive for excellence. Students were invited to keep their blogs and podcasts updated, until the next distance learning sessions of this course.

5 Learning ICT Online The second week of the course started in March 2009. Every Saturday an announcement regarding the weekly activities was posted on the LMS and was sent by email to students via Blackboard. It included the topic that would be learned, the readings, the

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chat sessions timetable, the deadline for answering the forum questions, and students were reminded to read the week’s guidelines in Course Documents. If they preferred, they could listen to a podcast with the announcements of the week. As they were familiar with the tutor, listening to her voice could have a motivational effect on them, as mentioned previously [3], [13], [14], [16]. A slide presentation and the readings were also available in the Course Documents. The second week was devoted to reviewing the concepts approached during the first week and to debate Prensky’s paper about Digital Wisdom [17]. The third week focused on WebQuest which was also the topic of the first assignment. Students had to select a WebQuest and analyse it, based on a grid developed by the tutor. Then, they invited their students to solve it in class and they had to report their reactions – if they liked it, if they solved it autonomously, if they learned the topic addressed in the WebQuest, and finally what were the advantages of using the WebQuest in learning. As during the chat some students mentioned that they did not have Internet connection in the school or computers in class, they only responded to the last item. In her assignment one student wrote: “It was really a great experience. They [the students] were very much involved in the activity. Even the two groups had a competition. Each group wanted to complete the task before the other. I found that the students enjoyed the webquest very much and they wanted to do more tasks. Also, I found that they were solving problems eagerly. I believe that if we could use such activities and facilities in our classrooms more often, the pupils would be more motivated”.

Another student said: “Solving WebQuests has many advantages for students because it includes higher level thinking, questions to be answered or solved and problems which are related to real life. To solve a WebQuest, students have to work in pairs or group, which promotes their social skills. In my opinion solving a WebQuest has the following advantages: WQ encourages critical thinking and cooperative learning, active learning, students have to make sense of what they are reading, it motivates students, and it improves students’ synthesis, analysis, and evaluation skills”.

The fourth week was about Treasure Hunt and students had to compare its structure with that of WebQuest. They were invited to create a Treasure Hunt in their own blog. They could send the tutor their Treasure Hunt before editing it online. After publishing it on the Web they invited their students to solve it. During the fifth week they had to evaluate website quality, based on Alexander and Tate’s criteria [18],Web Site Evaluation [19], 23 Quality Criteria [20], the Ten C’s for Evaluating Internet Resources [21]. The sixth week focused on meaningful learning and modelling with technology according to David Jonassen [22]. Some audio podcasts containing excerpts from the book were available on the Blackboard, particularly for students that did not have access to the book. A native speaker was invited to read the book excerpts. In the following week, the topic was concept maps and students had to read the chapters related to it in Jonassen’s book [22] or listen to the podcasts. Students had to install Cmap tools and create a concept map; afterwards they should use it with their students. A tutorial about Cmap Tools was available for them. The second assignment was a report about the use of concepts maps by teachers or by their students in the classroom.


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During the last week, the connectivism purposed by George Siemens [23], [24] and Stephen Downes [25] was introduced. Two important concepts in the digital age were distinguished: quoting and plagiarism, showing how one cites references. Finally, the collaborative and cooperative learning and its implications in group work was stressed. The students had a final exam in October in Male. 5.1 Students’ Participation in the Chat Sessions and Forums Students’ participation in the two weekly sessions and in the forum threads was not mandatory but highly recommended. Only a small group of students participated in the forums. In all fourteen chat sessions the number of participants ranged from 4 to 15. We realized that the students that participated in the chat sessions could clarify their doubts. Most of the time, they asked for questions about the topic under discussion. Some were checking what they knew regarding the subject at hand while others were learning it as they did not read the proposed d reading material. The seven forums included nine threads. The number of respondents ranged from 12 to 14. They had time to think about their answer, and for each reply the tutor wrote a constructive comment. At the end of the course, after submitting their assignments, the students were invited to complete a course unit evaluation questionnaire, but only six did so. Half of them participated in the forum threads and chat sessions. Those that did not participate provided different reasons. One reported technical problems in accessing the Blackboard platform and he did not have Internet access at home. The student wrote: “During the ICT course I had a dial up connection at School, its speed is 21kbs. I travelled to nearest TRC [Teacher Resource Centre] but uploading and downloading materials is expensive and only during the daytime. Night travelling is not possible and the next day cannot report to school if I do so”. Another student did not participate in the forums or in the chat sessions, because “I was too busy with my job due to certain unavoidable reasons. I wanted to participate in the forums and chat but I could not. But I read the chat recordings”. The third one answered the forums threads but she could not participate in the chat sessions, she said: “I completed all the forum work but it was very difficult for me to participate in the chat sessions as I was in Australia at the time. The time for the chat session for us was 2 o’ clock in the morning. Anyway, I participated in many chat sessions too”. A student commented the following strategy: “you [the tutor] had listed the list of participants for the chat sessions and forums. That was one of the best things you did to get the participants to pay attention to the course unit”.

6 Final Remarks Some particular situations arose during the course due to the students’ lack of ICT key competencies and the failure to read the recommended texts. Sometimes in the chat sessions some students were writing in Dhivehi to help each other, particularly in the first sessions. Some of them did not read the papers before the chat and most of the time the tutor was explaining the subject rather than clarifying their doubts. Due to

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this fact, one student wrote: “the chat session was not much useful as many of the participants were out of the topic”. When Cmap Tools were introduced, they mentioned having some difficulties in installing it. So, during the chat session I was explaining what they should do. Then, the download time generated a sort of race. It became fun because some of them downloaded faster than others, and they were providing details about the download percentage. During this course unit some students were in Australia taking another course and there was a 6 hour difference, so at 8pm (chat session) it was 2am in Australia. Some of those were complaining about the time of the chat sessions. The University sent to Maldives twenty books per course. Some students did not get the Jonassen’s book, but “we managed to get the copy of the units that u recommended for us to read from another school”. The podcasts about the book were intended to help those that had difficulties in getting it. A student that listened to podcasts indicated that he listened to the podcasts about the book, “because I did not get the book, I love reading rather than listening”. One student that read the book also listened to the podcasts. She commented “I preferred listening to the podcasts about the book”. This student is the only one that indicated that in other distance learning courses she would like to hear the teacher’s voice giving advice or guidelines, all other respondents preferred reading the guidelines. All others preferred to get written feedback instead of audio. In a study conducted in Portugal [10] the students liked receiving audio feedback. The different reaction of the Maldivian students may be justified by the fact that the tutor and the students were not native speakers therefore, reading a text would probably be easier to understand. On a five points scale ranging from poor to excellent, students considered very good or excellent: the relevance of the course unit contents, the organization of the course in modules (one per week), the guidelines for the week, the adequacy of the assignments compared to the aims of the course unit, and the support from the teacher. They mentioned that the course unit contents were useful for their job. The students were asked to give a free comment about the ICT in Education course. Their comments were positive and showed the impact of this training in their professional life: “The course unit was very interesting and we learned a lot which can be used in our educational system”. Another student wrote: “I have learnt how to use WebQuests and Blogs for the teaching purposes and in the capacity of a teachers’ educator as well. ICT is something without which we teachers cannot just teach these days”. One student recognized not only the importance of the course but also the need to update the equipment and the Internet access in schools: “ICT course is a very useful one. I learned a lot from this course. I introduced some of the components to my colleagues and they also found them very useful. For me, the main difficulty is that most of our students do not have Internet access. We are unable to use (in the class) what we have learned from this course but gradually parents are also becoming aware of the importance of the Internet and we can use this in the class very soon. It is also important to note that our classrooms are not ICT friendly classrooms. However, we are using the Teacher Resource Centre to teach some lessons using the smart board. Anyway, I found that this course is very useful to me”. This initiative of training teachers first and updating the system after is much better than the other way around. Finally, one last comment to synthesize everything: “The ICT course unit was


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the most useful unit for my personal and professional development during this course. As you can understand, during the face to face week, many of us were not able to explain much about podcasts, blogs, WebQuest, Treasure Hunt, concept maps, web tools etc. But after completing this unit, now we are the first batch of people who can explain to many of the Maldivian teachers and students these useful things for the teaching and learning process of our students and teachers”. We may conclude with John’s and Wheeler’s [26] words, now these “teachers hold the key to future developments” (p. 15) and with their commitment to ICT use, many of the opportunities to innovate and transform education and learning will be seized.

References 1. OECD: The Definition and Selection of Key Competencies. Executive Summary (2005), http://www.oecd.org/dataoecd/47/61/35070367.pdf 2. UNESCO: ICT Competency Standards for Teachers. UNESCO, Paris (2008) 3. Richardson, W.: Blogs, Wikis, Podcasts, and Other Powerful Web Tools for Classrooms. Corwin Press, Thousand Oaks (2006) 4. Siemens, G.: Knowing Knowledge (2006), http://www.knowingknowledge.com 5. Kader, B.: Malaysia’s Experience in Training Teachers to Use ICT. In: ICT in Teacher Education: Case Studies from the Asia-Pacific Region, pp. 10–22. UNESCO, Bangkok (2008) 6. Won, P.: Bhutan “Support for Teacher Education” Project. In: ICT in Teacher Education: Case Studies from the Asia-Pacific Region, pp. 3–9. UNESCO, Bangkok (2008) 7. Mason, R.: The university: current challenges and opportunities. In: D’Antoni, S. (ed.) The Virtual University, pp. 49–69. UNESCO, Paris (2006) 8. Siemens, G.: The art of blogging. Elearnspace: Everything elearning (2002), http://www.connecivism.ca/about 9. Carvalho, A.A., Aguiar, C., Carvalho, C.J., Cabecinhas, R.: Influence of Podcasts Characteristics on Higher Students’ Acceptance. In: Bonk, C.J., Lee, M.M., Reynolds, T.H. (eds.) Proceedings of E-Learn, pp. 3625–3633. AACE, Chesapeake (2008) 10. Carvalho, A.A., Aguiar, C., Maciel, R.: A Taxonomy of Podcasts and its Application to Higher Education. In: Damis, H., Creanor, L. (eds.) ALT-C – Conference Proceedings, pp. 132–140. ALT (2009) 11. Salmon, G., Nie, M., Edirisingha, P.: Informal Mobile Podcasting And Learning Adaptation (IMPALA). e-Learning Research Project Report 06/07, University of Leicester (2007) 12. Frydenberg, M.: Principles and Pedagogy: The Two P’s of Podcasting in the Information Technology Classroom. ISECON – EDSIG, vol. 23, pp. 1–10 (2006) 13. Durbridge, N.: Audio cassettes. In: Bates, A.W. (ed.) The Role of Technology in Distance Education, pp. 99–107. Croom Helm, Kent (1984) 14. Carvalho, A.A., Aguiar, C., Santos, H., Oliveira, L., Marques, A., Maciel, R.: Podcasts in Higher Education: Students and Teachers Perspectives. In: Tatnall, A., Jones, A. (eds.) Education and Technology for a Better World, pp. 417–426. Springer, Berlin (2009) 15. Levine, A.: Rip. Mix. Learn...The digital generation, social technologies, and learning. A presentation for the Training Expo Partners Conference, September 23 (2004), http://graphite.mcli.dist.maricopa.edu/emerging/wiki?RipMixL earn 16. Carvalho, A.A., Lustigova, Z., Lustig, F.: Integrating new technologies into blended learning environments. In: Stacey, E., Gerbic, P. (eds.) Effective Blended Learning Practices: Evidence-Based in Perspectives in ICT-Facilitated Education, pp. 79–104. IGI Global, Hershey (2009)

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17. Prensky, M.: H. Sapiens Digital: From Digital Immigrants and Digital Natives to Digital Wisdom. Innovate 5(3) (2009), http://www.innovateonline.info/index. php?view=article&id=705 18. Alexander, J.E., Tate, M.A.: Web Wisdom: How to evaluate and create information quality on the Web. Erlbaum, Mahwah (1999) 19. Schrock, K.: Web site evaluation & Internet lesson plan guide (2002), http://kathyschrock.net/abceval/teacherwebeval.pdf 20. Treadwell, M.: 23 Quality Criteria. Teacher@work: internet tools for teachers (2006), http://teachers.work.co.nz/23_criteria.htm 21. Richmond, B.: Ten C’s for Evaluating Internet Resources. University of Wiscounsin – Eau Claire, McIntyre Library (2003), http://www.uwec.edu/Library/guides/ tencs.html 22. Jonassen, D.: Modeling with technology: mindtools for conceptual change. Pearson Education, Upper Saddle River (2006) 23. Siemens, G.: Connectivism: A learning theory for the digital age. International Journal of Instructional Technology & Distance Learning 2 (2005), http://www.itdl.org/ Journal/Jan_05/article01.htm 24. Siemens, G.: New structures and spaces of learning: The systematic Impact of Connectivism, and Networked Learning. In: Carvalho, A.A. (ed.) Actas do Encontro sobre Web 2.0, pp. 7–23, CIEd, Braga (2008) 25. Downes, S.: Seven habits of highly connected people. Elearn Magazine (2008), http://www.elearnmag.org/subpage.cfm?section=opinion&article =97-1 26. John, P.D., Wheeler, S.: Teachers and technology. In: The Digital Classroom: Harnessing Technology for the Future, pp. 15–24. Routldge, London (2008)

Recommendation of Learning Objects Applying Collaborative Filtering and Competencies Sílvio César Cazella1, Eliseo Berni Reategui2, and Patrícia Behar2 1 Universidade do Vale do Rio dos Sinos (UNISINOS) Av. Unisinos, 950, CEP 93022-001, São Leopoldo, RS - Brasil [email protected] 2 Universidade Federal do Rio Grande do Sul Av. Paulo Gama 110, prédio 12105, Porto Alegre, RS, Brazil [email protected], [email protected]

Abstract. This paper presents a recommender system for learning objects which uses a collaborative filtering mechanism based on competencies. The model enables students to receive recommendations of learning objects automatically, according to students’ interests but also according to competencies that have to be developed. The prototype implemented was able to recommend relevant contents to students, aiming at helping them in the development of competencies. The paper also presents a couple of experiments showing that the recommender system has a good level of accuracy for the suggestions made. Keywords: Recommender Systems, Collaborative Filtering, Competencies.

1 Introduction One of the biggest challenges educators face nowadays is the organization of content and activities to develop certain competencies. This challenge is even bigger when one tries to identify and recommend different material to different students, based on individual needs, interests and skills. This paper presents a recommender system for learning objects which can suggest learning materials according to students’ interests as well as to skills that have to be developed. Learning objects are understood here as digital learning material constructed in a modular way so that they can be used together or separately [1]. In this sense, a scientific paper, a web page, a simulator, a question and answer software, all may be considered learning objects. The recommendation of learning objects focusing on competencies requires a change in traditional educational paradigms where pedagogical models follow rigid curricula and favor transmissive methodologies. The focus on competencies requires that social characteristics, interests, needs and limitations of each individual also be considered. The concept of competence can be understood as “practical intelligence for situations that rely on knowledge and mobilizes, transforms them according to the complexity of the situations” [2]. In this sense, knowledge and skills (know-how) are part of the concept of competence [3]. Although the recommendation of personalized N. Reynolds and M. Turcsányi-Szabó (Eds.): KCKS 2010, IFIP AICT 324, pp. 35–43, 2010. © IFIP International Federation for Information Processing 2010

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S.C. Cazella, E.B. Reategui, and P. Behar

learning objects is a desirable feature for any area of knowledge, our focal point has been the curricula of undergraduate programs in Information Technology (Computer Science, Computer Engineering and Information Systems). These programs have courses distributed throughout eight or nine semesters according to a set of prerequisites. The documents that describe these courses usually list a series of competencies which students should develop. For example, the databases course may require the student to develop competencies in "multidimensional modeling". The artificial intelligence course may require the development of competencies in “multiagent systems design”, and so on. The research presented here is based on this organization and notion of competencies. Our project aims to facilitate the access to learning objetcts that seem to be more appropriate at certain times, according to students’ features as well as to competencies that need to be developed and distributed accross the planningof a course. Among the computational techniques to assist in the search for relevant information, Recommender Systems [4] are able to automatically identify contents that are appropriate for each individual based on their characteristics or "tastes." This paper describes a model for recommender systems that is able to suggest learning objects relevant to undergraduate students, focusing on competencies to be developed. This paper is structured as follows: Section 2 extends the discussion on the concept of competence, emphasizing its implications in the development of the recommender system. Section 3 presents the technique of Collaborative Filtering, while section 4 describes in detail the proposed model, as well as its prototype and a set of experiments carried out to validate the model. Section 5 presents conclusions and proposals for future work.

2 The Concept of Competence It is possible to find in the literature several definitions for the concept of competency. The Cambridge English dictionary defines it as: "an important skill that is needed to do a job”. This definition, however, does not explicit relationships between important concepts such as: skills, issues, knowledge. Other authors enhance the definition: competence can be defined as the set of knowledge, skills and attitudes necessary for a person to develop their roles and responsibilities [5]. Perrenoud [6] defines competence as the ability to mobilize a set of cognitive resources (knowledge, skills, information, etc) to address the appropriateness and effectiveness of a variety of situations. In this way the skills are linked to cultural, professional and social conditions. In all definitions, we can easily see the relationship between the concept of competence and skills (know-how), knowledge and attitudes. Therefore, the question arises as to when and how we can make a recommendation of a learning object which may enable students to build knowledge related to specific issues, to develop particular skills related to given contents, to develop a critical awareness about the importance of competence to to understand how to use it. Thus, our goal in this research has been to use a recommender system to filter relevant information to students, and to select learning objects that are most appropriate in accordance with the competencies to be developed. The next section introduces the concept of recommender systems.

Recommendation of Learning Objects Applying Collaborative Filtering

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3 Recommender Systems Several content retrieval applications try to assist users in identifying items of interest. However, it is common that these applications return irrelevant contents as a result [4]. Trying to minimize this problem, recommender systems have emerged, focusing on the search for relevant information according to users’ features, as well as to certain requirements of the items sought. Different techniques may be used in recommender systems to find the most appropriate contents for users. In Sarwar et al. [7], for example, different algorithms are compared according to accuracy and performance. Here, our focus is the technique of Collaborative Filtering (CF) [8], a technique that is based on information collected about the entire community of users and has already proved suitable for several applications [9]. 3.1 Collaborative Filtering Collaborative Filtering is based on one of the most popular techniques for recommendation and is used in many systems on the Internet [10]. The technique is based on the analysis of preferences in a group of people who have common interests and have a "taste" for similar items. According to this approach, contents that may be recommended are filtered based on the evaluation of users about the items. For each user, a set of "neighbors" with similar behavior is sought [4]. The following subsection presents computational details about how to compute the coefficient of similarity between two users, one of the first steps in the collaborative filtering process. Then, the method to select subsets of users with a high similarity degree is described. In this last step, we present how to compute predictions in order to indicate how appropriate an item is for a particular user. 3.1.1 Computation of the Similarity and Prediction Coefficients To compute the similarity between students, the model proposed in this paper applies the Pearson's coefficient, which is an approach widely used in Recommender Systems based on Collaborative Filtering [8]. This coefficient measures the degree of correlation between two variables, resulting in values between -1 and +1, where the value -1 represents complete lack of correlation between variables, and the value +1 represents a strong correlation between them. Once you get the correlation between the opinions of students on certain learning objects, it is possible to compute the prediction of how much students would appreciate receiving a specific recommendation (prediction refers to the evaluation the student would give the object if he/she had access to it). This is done independently of the coefficient used for computing the similarity degree, since the predictions are computed according to a weighted average of the ratings given by students identified as closest neighbors (individuals who obtained a similarity coefficient less than a predetermined threshold). Through equation 1 one may determine how suitable object i would be for student a (pai), where ra is the average of grades given by student a; n is the number of students; rbi is the grade each student b gave to object i, rb is the average of grades given by student b; and corrab is the correlation of the target student a with a particular student b.

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(1)

3.1.2 The Recommendation Process Applying Competencies After the prediction value of an object has been computed, indicating how a particular student would rate that item, the rules of competencies have to be applied. These are designed to filter out learning objects according to competencies that need to be developed. Through this procedure, the system is able to identify learning objects with a high predictive factor that are likely contribuite to the development of competencies noted as important at certain times. After applying the rules of competencies, the system checks the database for recommendations whenever a student logs in.

4 Proposed Model The recommender model proposed here has been designed as a service to be made available in an application server. The model proposes the following sequence of steps for its operation: 1) Teachers plan their classes based on the competencies detailed in the course description. In their plans, learning objects for the development of those competencies are suggested. For example, in the fifth meeting of a database course, students should develop the competence of "developing projections through the use of relational algebra". Learning objects that can be used in the development of this competence should also be listed by the teacher; 2) At the first time the system is run, the similarity degree (Pearson's coefficient) between all users registered in the database has to be computed. Then, the system calculates the prediction value for all registered contents in the database. Afterwards, only new users, new contents or new objects that were rated are processed; 3) After the computation of the similarity and prediction coefficients (subsection 3.1.1), the system employs the rules of competencies to filter out the content for a particular user. For example, the learning object for "developing projections through the use of relational algebra" may recommended to a student as a consequence of his/her similarities with other students that rated the object positively, and because the student is starting to study topics that demand the development of this competence. 4.1 Prototype and Experiments A prototype of the model was developed in order to evaluate its efficiency in making appropriate predictions. Initially, some students were invited to participate in an experiment for the evaluation of learning objects (in this case scientific papers) that were recommended by the system. The scale used to evaluate learning objects was a


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Likert scale of 5 points. A tool for the evaluation of learning objects was developed as a web page that could be accessed by any device with a browser and an Internet connection. Figure 1 shows the interfaces of the prototype running in a PDA (Personal Digital Assistant).

(a)

(b)

Fig. 1. (a) Prototype interface for mobile devices (b) Interface for the evaluation of papers

The prototype was developed in Java, and the persistence layer was developed using the JDBC API. The database was implemented using MySQL5. 4.1.1 Research Method The prototype was evaluated in two experiments with convenience samples of 10 students that were at the end of the undergraduate course of Computer Engineering. A number of papers in the area of databases were selected by a teacher, and were classified according to the competencies that had to be developed. The experiments had the following goals: 1) To evaluate whether the prediction rate calculated by the prototype matched students’ rates, using the evaluation metric MAE (Mean Absolute Error) [11]; 2) To evaluate the accuracy of the recommendations made by the system through the metrics Recall (coverage) and Precision. In the context of recommender systems, it is said that accuracy is the relationship between the rating given by the user to an item and the rating computed by the prediction system. To evaluate the prototype, the metrics described below have been applied.

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4.1.2 Evaluation Metrics The MAE metric was used to compute the mean absolute deviation between the predictions of the recommender system and the actual rate given by students. The difference obtained was taken as the prediction error [9]. With this metric in hand, it was possible to evaluate how accurate were the recommendations made. Equation (2) presents the MAE formula, where pi represents the values predicted by the system, ri are users’ ratings to items recommended, and n represents the number of items considered. (2) The metric Precision is widely used in information retrieval, and represents the ratio between the number of relevant contents and the total contents returned by a search function. In the context of recommender systems, Precision is the ratio between the number of items that the user considers relevant and the number of items recommended. In turn, the metric Recall is based solely on the number of recommended items, while Precision estimates how much the contents are relevant to the user [11]. Recall (Equation 3) and Precision (Equation 4) are defined as: (3) (4) RA is the number of relevant items recommended by the system, R is the total number of items that should be recommended, A is the number of items actually recommended by the system, taking into account positive results and false positives. 4.2 Experiments and Results The first experiment was set in order to select an initial base of learning objects for the databases course, considering the competencies that had to be developed. Thirty papers were selected and catalogued by the teacher, relating them to the competencies that had to be developed. Thus a database with a 1:N cardinality was built, where each competence had N papers cataloged to be used during the course. The competencies were distributed according to the class plan, and the learning objects were organized according to the competencies they represented. These papers were then reviewed and rated by students, which is a way to minimize a collaborative filtering limitation known as cold start [9]. Students were then requested to evaluate papers that had been allocated randomly. These papers were distributed to students, thereby generating an array of initial assessments "Paper x User", totalling 103 ratings. On the average, 10.3 items were assigned to be evaluated by each student, and these initial assessments were meant to identify the preferences/profile of each student. Based on the assessments provided, the system could compute the coefficient of similarity between the students (Pearson coefficient, ranging from -1 for a weak correlation, to 1 for a strong correlation). For example, the correlation value between students U4 and U6 was 1, showing a strong correlation according to Pearson’s. For students U2 and U4, a correlation value of -1


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was computed, meaning a total lack of similarity between them. As a result, it was found that 27.59% of the correlations computed between students were considered strong (these students had "tastes" that were similar to the objects evaluated), 20.69% were considered weak (these students had "tastes" different from the objects evaluated). For 51.72% of the correlations computed, nothing could be said. In a second experiment, students evaluated a number of scientific papers recommended to them based on the rates provided in the first experiment. The system recommended only items with prediction rates higher than 3, for a Likert scale of 5 points. After computing the correlation values and the similarity prediction values, the rules of competencies were applied to select the most appropriate papers. As reported earlier, the papers were cataloged by competencies and were distributed in the class plan for each meeting. For instance, the lesson about "developing projections through the use of relational algebra" was assigned for the period starting in the eighth meeting and ending in the tenth meeting. Students during this period should receive recommendations of learning objects suitable for the development of this competence. Table 2 shows the MAE and Precision values computed for the second experiment. The MAE metric showed an average difference between the predictions made by the prototype and the assessments made by students (1). The accuracy achieved by the prototype’s predictions was 76%, which was considered satisfactory. Table 1 shows that the integration of collaborative filtering with the filter of competencies produced 16 recommendations, the same as the number of meetings planned for the classes. All of these recommendations were tailored to the development of competencies. It should be noted that the student U4 does not appear in Table 1 as he/she did not evaluate the papers recommended by the system. Table 1. Results of the experiment concerning Precision

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Table 2 presents the experimental results concerning the relevance of the recommendations for the students. Table 2. Results of the experiments concerning the relevance of the recommendations

For 7 out of the 10 students, we can say that the system succeeded to recommend appropriate items that were relevant for the users to develop competencies in the period of time established, as the Precision factor reached 1, and the Recall factor was below this value. Students U6 and U7 received, respectively, one and two recommendations each, recommendations based on collaborative filtering and competencies. However, the ratings that these users gave to the recommendations were not satisfactory for the computation of the prediction values, which had to be at least 3. Therefore, it was not possible to say anything about these students in particular. Again, additional assessment would be necessary to reach any conclusion regarding these students.

5 Conclusion The main goal of this research has been to integrage collaborative filtering with a mechanism based on competencies in order to (a) help students find educational materials related to the development of specific competencies, (b) give some flexibility to the suggestion of materials to be consulted or used by students, to the extent that the interests of each are considered in the process of recommendation. By supporting students in their learning process, the system proposed here also gives support to the work of teachers, by organizing materials and learning situations, and by providing better links between knowledge and competencies. In this sense, the proposed model tries to be aligned with these principles, recommending learning objects that best suit the interests of the student, but in accordance with competencies that need to be developed at any given time. Through empirical experimentation with a group of undergraduate students in Computer Engineering, it was found that the degree of precision achieved by the recommender system was


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satisfactory. The accuracy level of 76% showed that the system was able to recommend learning objects that satisfied the students for their studies, without neglecting the competencies required in the summary of the courses they were taking. As for the evaluation metrics Precision and Recall, it can be said that the prototype succeeded to get the students to have access to those materials that were relevant to the competencies to be developed in that moment, considering the set of learning resources available. As future work, we intend to test the system with other types of learning objects to verify if its performance remains satisfactory. Using information from learning objects’ metadata is another future goal. Such feature should allow us to select objects according to specific requirements also related to competence development (e.g. level of difficulty, level of interaction, etc.). We are also working on the forming of virtual communities with users with a similarity coefficient within a certain range.

References 1. Wiley, D.A.: Learning object design and sequencing theory. Doctoral thesis, Brigham Young University (2000) 2. Zarifian, P.: La politique de la compétence et l’appel aux connaissances à partir de la stratégie d’entreprise post-fordiste. Contribuition to the Coloque of Nantes (December 13, 2002), http://www.scoplepave.org/ledico/auteurs/zarifian%20 competence%201.htm (2002) 3. Fleury, A.C.C., Fleury, M.T.L.: Estratégias empresariais e formação de competências. Atlas, São Paulo (2000) 4. Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005) 5. Dutra, J.S.: Gestão por Competências. Editora Gente, São Paulo (2001) 6. Perrenoud, P.: Construir as competências desde a escola. Porto Alegre, Porto Alegre (1999) 7. Sarwar, B.G., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommender algorithms for ecommerce. In: ACM E-Commerce Conference, Minneapolis, MN, pp. 158–167. ACM Press, New York (2000) 8. Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating “word of mouth”. In: Human Factors in Computing Systems, Denver, Colorado, USA (1995) 9. Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004) 10. Schafer, J.B., Konstan, J., Riedl, J.: E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery 5(5) (2001) 11. Cazella, S.C., Corrêa, I., Reategui, E.: Um modelo para recomendação de conteúdos baseado em filtragem Colaborativa para dispositivos móveis. Revista Novas Tecnologias na Educação 7, 12–22 (2008)

Teaching Practice from the Perspective of ICT Student Teachers at the Faculty of Education, Charles University in Prague Miroslava Černochová Charles University in Prague, Faculty of Education, Dpt. of Information Technology and Technical Education, M.Rettigové 4, 116 39 Praha 1, Czech Republic [email protected]

Abstract. The author worked for some years as a coordinator, methodology consultant and supervisor of teaching practice of ICT student teachers at the Faculty of Education in Prague. In her paper she summarizes experiences and data collected by questionnaires during years 2004-2008 by student teachers within their teaching practice on subjects related to Computer Science, Informatics and ICT Education in Czech Basic and Secondary Schools. The results came out of evaluative questionnaires and comments published in on-line support. A video-record will form a part of the paper presentation. Keywords: Teaching practice, ICT teacher, information education, Informatics, computer technology, teacher education, lesson plan, Primary/Secondary school, Moodle.

1 Introduction The paper is dedicated to a teaching practice of student teachers implemented into a study program of a Master’s Degree study of Information and Technical Education at the Faculty of Education in Prague. During five years of their study ICT student teachers have to successfully get through two periods of teaching practice in basic or secondary schools in subjects dedicated to ICT, Informatics or Computer Science. After graduation they are fully qualified basic or secondary school teachers. They would work as primary or secondary school teachers of ICT, Informatics or Computer Science. They would work at schools also as a computer nets supervisor and they might be employed as ICT coordinators and consultants. They would provide advice how to apply ICT in school practice to teachers or head teachers. 1.1 Teaching Practice of ICT Student Teachers in Primary or Secondary Schools Generally, the teaching practice of student teachers of the Faculty of Education in Prague is organized in the Year 4 and Year 5. Each of them takes four weeks. N. Reynolds and M. Turcsányi-Szabó (Eds.): KCKS 2010, IFIP AICT 324, pp. 44–55, 2010. © IFIP International Federation for Information Processing 2010

Teaching Practice from the Perspective of ICT Student Teachers

In Year 4: 4 week of teaching practice

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In Year 5: 4 weeks of teaching practice

Fig. 1. A position of teaching practice in a study program of student teachers at the Faculty of Education

Within each of four weeks of an obligatory teaching practice the ICT student teachers assist or stand-by teachers in classrooms. During this period they design their own lesson plans and teach a minimum of 12 lessons in the classroom environment. (I.e. during the university study, students teach at least 24 lessons in Basic or Secondary schools). They analyze and evaluate their teaching experience together with their mentors who work in a close collaboration with a didactic consultant from the Faculty of Education. For each lesson ICT student teachers design their lesson plans while using an identical template (see Appendix 1). They can ask a mentor for advice to tailor lesson plans or they can consult with a didactics consultant or their peers from the Faculty of Education on-line. Via the on-line support organized in Moodle, ICT student teachers deliver a set of lesson plans, a questionnaire, reports and news about their teaching practice. Together they can discuss some technological problems or pedagogical situations in forums then. ICT teacher students can complete their four-week teaching practice in Basic or Secondary schools not only in Prague, but also whenever in the Czech Republic. Some students prefer conducting their teaching practice at schools they attended as a child. Their former teachers can become their mentors and colleagues and can monitor their first professional achievements. In such cases ICT student teachers find good relationships with their mentors as very important for their teaching practice. In 2002 we introduced in Moodle on-line support in order to improve coordination and management of teaching practice for our ICT student teachers. The on-line support is very useful and helpful for all, student teachers and mentors, who should visit, monitor and supervise each student teacher in their schools. From 2007 onwards we also started recording our student teachers by a digital camera in schools. After teaching practice in schools all video-recordings are analyzed in a seminar together with all ICT student teachers. Video-records allow them to see the real situation in the classroom of various schools, the equipment of their computer-labs, the interactions between ICT students and pupils etc. Some ICT student teachers, during their university study, are employed in basic or secondary schools as non-qualified teachers of informatics or computer science subjects. In such cases their work is validated as teaching practice; it means they complete their teaching practice in the schools where they work. The ICT students who work as non-qualified teachers, unlike their student colleagues who are not employed in schools, have been prepared not only to teach children, but also in other fields and responsibilities of the teacher profession, primarily in:

M. Černochová

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• • •

assessment of pupils learning outcomes, a legal agenda related to schooling, communication with parents, etc.

These ICT student teachers who already work in schools are very active in seminars at the Faculty of Education. They ask questions that distinguish them from their university school-mates, exploit their experience from teaching in schools and present interesting ideas on how to implement ICT into education. Additionally, they usually design high-quality teaching or study materials. 1.2 How ICT Student Teachers Develop Lesson Plans During a teaching practice in schools ICT student teachers must develop lesson plans in identical templates (see Appendix 1). The items of the template don’t correspond with a structure of lesson plans that (teachers) students usually prepare. The template serves not only to describe a scenario of their teaching lesson, but also as a didactic exercise to develop some of student teachers’ skills and to support their own thinking about teaching and learning from different perspectives: most of the student teachers have a problem formulating clever questions or suggesting an interesting problem to be solved. Most of student teachers don’t consider a terminology and lexis of their subjects. And it is difficult for them to develop a concept structure of ICT subjects in minds of their pupils. Most of the ICT student teachers (66%) design their teaching without any help of their mentors. The question of how to design a lesson is more complicated. The reason is that in most schools1 ICT teachers do not use any textbook. Most of teachers (about 79%) develop their own study materials for pupils. For making lesson plans ICT students mainly use the Internet (90% of students – see Fig.2). In the lesson planning process the ICT student teachers pay great attention to these aspects: • • •

Time table of teachers and pupils activities and a structure of lesson Educational content of lesson Proposal of activities and assignments for pupils.

The ICT students found out that designing timetables for lessons and motivating pupils to take active participation (see Fig. 3) are the biggest problems. Sometimes activities they prepare for pupils are either too easy or not stimulating tasks without any respect to personal priorities, interests or preferences of their students. Another problem that ICT student teachers met with was how to motivate children for ICT activities. Among Czech schools there are a lot of differences in approaches to teaching ICT subjects. Most of them concentrate on development of fundamental ICT skills of pupils. They mainly focus on training in MS Office software. In some schools children produce animations or multimedia applications using digital cameras and special SW. Rarely the Czech Basic or Secondary schools implement basis of programming into compulsory ICT subjects (see Fig. 4). Some Czech schools organize for children 1

In 2004 - 2007 only 13% ICT teachers in Primary or Secondary schools used text-books in their teaching with children.


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facultative courses in programming. ICT student teachers found out that in basic schools some teachers2 are dedicated to programming in Karel and in secondary schools mainly in HTML or Pascal. Most of ICT student teachers would prefer to teach programming than to train fundamental ICT skills with editors or spreadsheets.

Fig. 2. Types of resources used by ICT student teachers for lesson plan development

Fig. 3. The main problems the ICT student teachers have met during their teaching practice (% student teachers)

2

Imagine and Baltik have become a favorite SW for pupils’ development of programming thinking and algorithm skills in Czech primary schools.

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Fig. 4. Teaching of programming in Czech Basic and Secondary schools

From September 2007 Czech schools apply a new curriculum concept defined by the Framework Educational Program where ICT is in a totally new position. According to this curriculum document, ICT has become a new compulsory educational domain with a similar position as Math, Languages, Art or Science Education traditionally have. Lessons about ICT are integrated into Primary Education in Grades 4 and 5 (for children of age 9- 11). Therefore some ICT teacher students during their practice also taught courses with children of age 11-12. There they could experience how children of that age like activities with computers. During their teaching practice ICT student teachers could see how schools have been applying new curriculum policy, how ICT has been integrated into project works at schools, which topics of Informatics are being taught to children in Grade 4, etc. 1.3 A Situation in Schools Where ICT Student Teachers Complete Teaching Practice Most of the ICT student teachers did their practice in schools involved in the “Governmental Information Policy in Education” (GIPE). In 2002-2006 the GIPE equipped Czech schools with computer technology and SW to integrate ICT into education, to train teachers in ICT skills and to organize educational projects within ICT. But since 2007 the Czech Republic hasn’t had any financial support of that sort and further ICT development at schools doesn’t exist. The ICT student teachers could see the impact of this fact and experience how schools differ in ICT equipment, SW and services for teachers and pupils. Nevertheless, step-by-step most Czech schools have succeeded in getting interactive whiteboards that caused a boom in motivation of Czech teachers to work with computers and Internet. In the last two years most of the ICT student teachers could use interactive whiteboard in schools, too. In 2006 some schools, that facilitate teaching practice to our ICT student teachers, have participated in many EU Projects (eTwinning, CALIBRATE, etc.). Some ICT student teachers could participate in these EU projects activities. SCHOOLS FROM THE ICT STUDENT TEACHERS’ PERSPECTIVE Most of the ICT student teachers appreciate: a) high-tech equipment at schools (data-projector, interactive whiteboard, wireless mouse and keyboards, etc.). Computer labs in schools are arranged as multifunctional classrooms with very modern furniture, where they could be given not only Informatics lessons, but also lessons of Music or Art Education.


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In the last two years student teachers had the opportunity to use an interactive whiteboard in most schools. “I really liked the computer labs; there are three computer labs in the school that are all arranged in non-traditional ways. A teacher in my school designed the furniture. I also liked the Internet cafe and library in the school.”

b) a good “climate” and partnership among teachers in schools. Their open approaches to pupils enable them to find more flexible solutions for unknown problems. Teachers teach pupils skills of teamwork. From ICT student teachers’ point of view there are a lot of kind people at schools. c) teachers’ effort to upgrade HW and SW in schools. ICT student teachers were surprised by how much effort teachers had to make if they wanted to upgrade the equipment and SW applications at the schools. During their university study, the ICT student teachers had no reason to think about it because the Faculty offered them the use of professional SW and high-quality HW. However, at schools today ICT teachers can’t only teach their subjects, but they also have to take care of SW and HW. In many schools there is also a lot of “old fashion” technology; schools don’t have enough money to buy new technology. For maintaining old computers it is necessary to have enough spares and components. ICT student teachers were surprised by “teachers’ active attempts to get needed components and spare parts for old fashioned computers and hardware”. ICT student teachers could get experience with some SW that has not been applied at the Faculty of Education – for example Master Eye that helps teachers to organize work on PC with children and that could be very useful in computer labs without data-projects. d) a chance to use an interactive whiteboard. Till 2007 ICT student teachers haven’t been able to use an interactive whiteboard at the Faculty of Education. However, most of the schools where they do teaching practice are equipped with them. ICT student teachers appreciated the possibility to work with this new type of whiteboard. e) ways how in some primary schools children from age 7 to 10 are being encouraged to work with ICT. These schools decided to find a space in their school timetable to allow teachers to work with young children in computer labs. “I was surprised by a strong-willed effort by my supervisor teacher who was not only an ICT and Art Education teacher, but also a principal of the school. His pupils master graphic techniques and Art thinking – you can see the pupils’ pictures and presentations in school halls, classrooms and teachers rooms – the school looks like a Gallery of Art.”

f) the high interest of children in computers g) a fact that in few schools all teachers could use ICT in all classrooms. In some schools ICT student teachers could see how schools utilize Moodle. Some ICT student teachers had some critical comments to: a) conditions for ICT Education “In my school there were limited technical conditions for ICT education. It is a pity that children couldn’t learn more about WWW.”... “In the computer lab there was clutter and very often some teachers came in to speak with my supervisor teacher. I expected that it would be possible to use a data projector for my teaching as I was accustomed at the Faculty of Education, but the school didn’t have it. It threw me into confusion. Therefore I decided to create my Web pages where I could publish all study and teaching materials and instructions for my pupils (www.vyukarudna.wz.cz).”

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b) and to approaches to teaching of ICT subjects: “In my school, children prefer to play computer games what isn´t so difficult and boring according them therefore they regard ICT lessons as not very important. …Teachers didn’t give special professional attention to gifted pupils who knew a lot about technology and more than their teachers. In the school there dominates a practical orientation of ICT education to develop only fundamental user’s skills and to achieve a general “computer literacy”…. “I don’t agree children cannot learn programming and haven’t any textbooks.”

TECHNOLOGICAL CONDITIONS IN SCHOOLS Teaching practice in schools is a good opportunity for ICT student teachers to see a real situation in schools with all problems that schools have with ICT services and ICT in education: “In my school where I did teaching practice there were three supervisors for administration of computer labs. But I know some schools without any qualified supervisors where supervision of the computer lab is done by a teacher without any qualification in ICT.” … “My supervisor- ICT teacher was very busy; he is the only specialist for ICT subjects in the school and also a supervisor of computer classrooms. He also helps his colleagues with their computers to solve their user problems – this hectic schedule sometimes influences the quality of his teaching.”

According to comments by ICT student teachers in 2005, student teachers were faced with teachers who had very low computer literacy. Therefore teachers who mastered work with ICT were very busy and overloaded by activities with ICT. “In my school some teachers even rejected installing computers in their working room.” Some ICT student teachers struggled with a lot of technical problems. In 2007 some of them had to use very old computer machines without any data projectors and with low speed connectivity to the Internet. For instance, in 2007 one basic school didn’t have a computer-net, there were only 10 isolated computers in a classroom. Generally, some schools have no data projector or have very low speed connectivity to the Internet. Other problems that influenced the teaching practice of ICT student teachers were a lack of scanners, printers or multimedia workstations. Some problems originated in a bad conception of network administration and in setting users´ rights. “In my school non suitable user data security could protect against abuse or their complete discarding by other pupils.” … “For teachers who don’t teach ICT subjects it was very difficult to use a computer lab. The lab could be used only for ICT lessons.” … “I didn’t like the school had two computer labs, but only one of them was connected to the Internet.” … “Computers were situated in the computer lab in a manner that pupils were sitting with their backs to a teacher and to a data-projector.”

WHAT SHOULD BE IMPROVED AND CHANGED IN SCHOOLS? ICT student teachers were asked to propose what could be improved in a school where they did their teaching practice. Most of their recommendations on how to change a situation in their school related to a technological improvement of schools: “I recommend to install a bigger monitor for teachers (in labs for Biology Education and Foreign Language Education) and for students, too.” … “I would reduce a path to a common directory on a server where children can save their work. It was a problem for children.” “I would install Total Commander that could be for children more practical and understandable.” … “I would move the teacher workstation to another place in the computer lab.” … “I would change how the computers are arranged in a net; I would change the administration of user’s profile.”


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“I would support more the Internet activities of students, but it would be necessary also to have better security for the computer network.” … “I would like to recommend to a computer-net administrator to equip schools with advanced technology” ... “and to upgrade SW.” “It would be very useful for teachers to install dataprojectors in all classrooms with computers.” “I would prefer to install in all computer labs of school the same operating system (for example Windows XP).” “I would like to install more educational SW.” “In my school in a computer lab where I had lessons with children there wasn’t a dataprojector, so I always had to borrow it from another classroom and after my lesson I had to put it back. It was not very practical. The security setting did not allow me to open an e-mail account on a public portal and children didn’t have any e-mail accounts in the school server either. No existing e-mail accounts forced the school to break collaboration with other schools.”

In 2005 the ICT student teachers recommended to school ICT administrators and ICT coordinators: To get rid of blackboard and to install a new one in a better place in the classroom. To re-arrange workstations in a classroom. To buy a dataprojector or to install Master Eye. To have computer labs accessible to all teachers and students. To integrate Moodle into school work. To buy more computers. To install other e-mail system (not GroupWise). To use also another graphic editor that could be used also at home by students. To employ more ICT administrators and ICT coordinators. to ICT teachers: To introduce textbooks for students. To teach programming. to head teachers: To recruit qualified ICT teachers for ICT subjects. In 2006 ICT student teachers recommended to school ICT administrators and ICT coordinators: To allow pupils to work in computer labs after their school activities and during breaks. To darken computer labs. To install another computer lab. To buy another SW – also for a teaching basis of database and programming. To change arrangement of computers and furniture in computer labs. to teachers: To extend ICT courses to others grades. To involve pupils (school) into an international or national project activity. To motivate pupils to apply ICT in practice and in life.

In 2007 ICT student teachers advised to school ICT coordinators: To use Vision studio or similar SW for monitoring student’s individual activities. To install a camera-control system (to protect technology equipment against losses). To install dataprojectors in all computer labs. To allow teachers to conncet a notebook. To install an interactive whiteboard

1.4 Student Teacher Professional Development from a Student Teacher Perspective After teaching practice ICT student teachers have to fill evaluative questionnaires where they have to answer some questions related to conditions for information

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education in schools and to the approach of teachers and school management to a new curriculum concept of ICT education. All lesson plans, video-recordings and documents developed by ICT students in their teaching practice form a part of a student portfolio that is applied in some courses oriented on methodology and didactic aspects of information and ICT education. QUESTION: WHAT HAVE ICT STUDENT TEACHERS LEARNT IN SCHOOLS DURING THEIR TEACHING PRACTICE? In 2004: “I have learned very much. As a teacher I met so many problems (SW, computer net, HW, connectivity) that I didn’t expect that I had to think about how to solve them.” … “It was my first experience with young students as my pupils.” … “Teaching practice was super. You could see that theory and practice are two totally different things! It seems a lot of my teachers at the Faculty of Education forgot this fact!” … “I was surprised how easy it was for me to teach!” … “At first I was slightly afraid to teach children a subject that I couldn’t learn personally in my basic school. But at the end it was a fantastic experience for me!” … “I was teaching at a basic school – it was nice practice for me. I am curious about how it would be to teach at a secondary school.” “I completed my practice at a secondary school. I was curious about how my female colleagues would be able to teach teenagers like I had to teach.” … “I designed learning activities for children by my own concept to be interesting also for me personally. I exploited my personal experiences with ICT.” … “I was kindly surprised how teachers, school management and my supervisor-teacher accepted me. I was very happy to be and to teach there. It influenced me to become a teacher!”

In 2006: “The teaching practice is very short, but for student teachers it is very exacting. During four weeks we have to get experience with teaching in two specializations. Fortunately my practice was successful – I am employed in this schools and my teaching practice was realized with my pupils. There was only one problem – my supervisor-teacher is a student of the Technical University, who is an expert in ICT and Computer Science, but he has no pedagogical background like me. He could be more convenient as a supervisor-teacher for ICT subjects with talented children.”

In 2007: “I liked my teaching practice. It was my first experience with being a teacher. At first I was afraid but after my first lesson I discovered that it is a fantastic and creative job to be a teacher. I was glad to see how my pupils collaborate and work. Step by step I have managed to motivate all pupils to do given assignments. Maybe I will succeed in it because I was a new “non-hackneyed element” in a classroom and my pupils did not know what they could expect from me.”

QUESTION: WHO HAS INFLUENCE ON TEACHING STYLES OF ICT STUDENT TEACHERS? In Fig 5 you can see who has the greatest impact on the teaching style of the ICT student teachers. Their own basic or secondary school and university teachers have had the most important influence on their approaches to pupils and teaching strategies. Some ICT students are not thinking about professional patterns and they try to tread their own path without any models or patterns.


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Fig. 5. Professional patterns of who has an influence on ICT student teachers

THE BIGGEST PROBLEMS FOR ICT STUDENT TEACHERS IN TEACHING PRACTICE For most of ICT student teachers there is a problem organizing their teaching into 45 minute lessons. Another problem that they had relates to a motivation of pupils to be active. ICT student teachers did not see any problem in communication with their pupils (Fig. 3). Some ICT student teachers were faced with teaching both ICT beginners and ICT experienced pupils. They didn’t know how to design lesson in class with a big differences in ICT knowledge and skills of teenager students and whom should they dedicate their teaching to in their 45 minute lessons. “I was not ready to teach students with such big differences in ICT skills and knowledge: I hadn’t time and space to develop the advanced ICT users and at the same time I couldn’t teach beginners in ICT.”

2 A Role of Moodle in Teaching Practice of ICT Student Teachers A few years ago we decided to use Moodle for coordinating and managing teaching practice of ICT student teachers. In Moodle we publish instructions for students on what to do and documents (lesson plan template, templates for time-table, etc.). In Moodle ICT student teachers can ask colleagues and didactics specialists and inform them about the situation in their schools. We endeavor to answer students immediately. “I would like to appreciate a clear structure and function of Moodle support. Thanks to very clear instructions I could see how teaching practice proceeds in other schools of my colleagues, what I have to do. Our teachers from the Faculty of Education answered us immediately. Each important question was answered.” ... “I would appreciate the Moodle support which helped me very quickly understand a system on how teaching practice is organized. It is a pity, that other departments of the faculty do not use similar support.”

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3 Conclusions The teaching practice has a key role in university teacher education of ICT student teachers. It is a pity at the Faculty of Education in Prague student teachers can experience their teacher profession with pupils only during 8 weeks. It is a too short time period to prepare student teachers for their profession and to demonstrate mastering key professional competences [6]. Some students who study in a full-time study program at the Faculty of Education and at the same time work at schools as part-time non-qualified ICT teachers, achieve in didactic courses and in their diploma thesis significantly better results and apply more professional approaches to given tasks and problems than students who only study in full-time study programs.

References 1. Moodle, http://moodle.pedf.cuni.cz 2. Van der Linde D., van der Hoeff A., van Hans, P: AST Michel ICT Knowledge base. Version 1.0. (2009) 3. JOMITE Group SpriTE Teaching Practice Assessment Checklist (2009) 4. Vlastní hodnocení škol. ČŠI, Praha 5. Janík, Tomáš Didaktické znalosti obsahu a jejich význam pro oborové didaktiky, tvorbu kurikula a učitelské vzdělávání. Brno (2009) 6. Hodnocení studentů studijních programů učitelství při plnění souvislé pedagogické praxe z oboru it (IKT) KITTV, PedF UK, Praha (2010)

Appendix 1 Lesson Plan Subject: Grade: School: Topic: Lesson plan author: Educational program (curriculum document) applied by the school: 1. Preliminary requirements for pupil’s knowledge: Give a list of pupil’s knowledge and skills to be able to understand a topic for a lesson. For example • Pupils are able to..., Pupils understand..., Pupils master in ...) 2. Main aim of the lesson: • Particular goals: • ICT competencies (related to the document of the Framework Educational Program) Give a list of competencies that you will contribute to develop in your lesson. 3. Text-books, study materials that would be used by pupils for your lesson. 4. Aids, tools that would be used in a lesson. 5. A list of technology equipment and HW that would be used in your lesson by a teacher and by pupils. For each tool explain for which activity it will be used. 6. A list of SW that would be used in your lesson and to prepare for your lesson. 7. Draw a scheme of classroom arrangement.) 8. Teaching methods and strategies with the explanation how you will apply teaching methods and which teaching strategies you will prefer for your teaching.


9.

10. 11. 12. 13. 14.

15. 16. 17. 18.

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Procedure: Describe instructions on how to order lesson procedures. Your description ought to be formulated as a manual on how to realize your lesson plan. Questions that pupils should be able to answer at the end of the lesson. Assignments and problems to be solved by students. Give also their solution and numeral account). Homework. Formulate problems and activities for homework. Evaluation: Explain criteria how student outcomes will be evaluated. Addition and extra topics, assignments for talented pupils: Propose how you will organize a lesson with a classroom where you have found some talented pupils. WWW: Give a list of WWW pages used in your teaching. Learning objects: Give a list of learning objects that you will use. Vocabulary of terms from ICT and Informatics related to the topic: Give a list of terms with explanations understandable and dedicated to your pupils. Term and its explanation: …..

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TEACHER SELF-EVALUATION AND AUTO-REFLECTION 1. How did you succeed your lesson? 2. What didn’t you manage?

Intentions to Pursue a Career in Information Systems and Technology: An Empirical Study of South African Students Jason F. Cohen and Poonam Parsotam School of Economic and Business Sciences, University of the Witwatersrand, 2050 Johannesburg, South Africa [email protected]

Abstract. This paper reports on a study of the IT career interests of 263 South African university students. Drawing primarily on social cognitive career theory, a number of variables were selected and their effects on student intentions to pursue an IT career and choice of major were examined. Results revealed very low IT career intentions amongst students. Occupational self-efficacy, computing experience, computer anxiety, computer self-efficacy, and perceived career rewards were found to be important factors. Student perceptions of the core introductory IS course are also strongly linked to their intentions. Demography (gender and race) had mostly indirect effects. Keywords: Careers, choice of major, self-efficacy, computer anxiety, computer attitude, experience, outcome expectations.

1 Introduction A strong information technology (IT) workforce is considered part of the core human capital needs of a knowledge society. However, high school and university students are expressing little interest in pursuing information systems (IS) or IT careers, and enrolment figures in IT related majors has declined globally since the Dot-com failures of 2001 [1], [2]. Although there are some reports that enrolments may soon increase, there remains concern over impending IT workforce and skills shortages [3]. To better predict enrolment trends, IT educators are calling for an improved understanding of factors associated with students’ IT career intentions and choice of major. Initial contributions describe changing enrollment patterns and the outlook for IT graduate demand e.g. [4], and point to external market factors, including the general economy and the outsourcing phenomenon, as driving students away from IT [5]. Some researchers have chosen to direct attention inward at IS/IT faculty. They recommend changes to course design and the use of leading edge technology as ways to attract and retain students [6], [7], [8]. Yet, others consider student backgrounds, attitudes and their perceptions of IT as the primary determinants of their career and study choices. To this end, some exploratory studies are uncovering student awareness and expectations of an IT career [9], the factors important to them in the selection of a major [10], [11], as well as the N. Reynolds and M. Turcsányi-Szabó (Eds.): KCKS 2010, IFIP AICT 324, pp. 56–66, 2010. © IFIP International Federation for Information Processing 2010

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stereotypes they may have of IS/IT professionals [2]. The role of gender and ethnicity in influencing attitudes and IT career intentions has also been explored [12] along with perceptions of job availability, interests in IS, perceptions of the curriculum, and views of family and professors [13]. Recently, social cognitive career theory has been drawn upon to successfully test a model of students’ intentions to select a computing major. This theory models student intentions as a function of their interests, selfefficacy beliefs and outcome expectations [14]. The aim of this study is to contribute to such explanations of student interest in IT oriented careers. We draw on social cognitive career theory (SCCT), and prior work in the IT career literature, to examine a set of factors for their influence on South African students’ interests and intentions in relation to IT careers. The success of the knowledge society will require a committed and dedicated IS/IT workforce and being able to influence, impact and predict the career choices of our students is important to preventing potential labor shortages [10]. Therefore, by identifying the factors that influence student intentions, we can prevent missed opportunities for attracting students into the field, we can define interventions to address those factors within our full or partial control, and we can understand the nature and extent of the impact of those factors outside of our immediate control.

2 Background to the Study This study focuses on both ‘choice of major’ and ‘career intention’ as reflections of student interest in IT. This allows us to make a distinction between the educational aspirations of students and their career aspirations. IT career intention is a career aspiration and is defined as the subjective probability of the student choosing to pursue a career in the IT field [12]. Choice of major, on the other hand, is considered an educational aspiration and thus part of the preparation for a career choice [15] but not a precise determinant [16], [17]. Although a number of degree programs exist to serve the educational aspirations of students interested in IT [18], we focused on the study of Commerce students and their intentions to major in Information Systems. 2.1 Research Variables SCCT describes the career development process by explaining through Bandura’s social cognitive theory how people will develop goals for future activity involvement, such as the intention to select a college major or to pursue a career path, consistent with their interests, self-efficacy assessments and positive outcome expectations [15]. For this reason, this study explores student self-efficacy beliefs, attitudes and interest in IT, and outcome expectations (rewards) of an IT career. SCCT also accounts for perceived barriers to career development that might influence career choices [17]. Even if an individual possesses high levels of selfefficacy and interest, perceived barriers to career entry or advancement may still inhibit the translation of interests into career choice [19]. Perceived barriers may arise from differential opportunities for development (e.g. education), or perceptions of gender or ethnic discrimination in the associated career. For this reason, this study also explores students’ differential experiences and backgrounds in computing as well as their demography in an effort to better understand their career intentions.

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Demography: There is concern that the under-representation of certain demographic groups in the IT field is limiting the human capital potential of the knowledge society [20]. Specifically, participation of females and certain ethnic/racial groups in the IT field has received attention and efforts to understand their perceptions and motivations to study and work in IT have been underway for the past decade e.g. [21]. Our study therefore seeks to provide additional empirical evidence on gender differences and the effects of racial demography on IT career intention and choice to major in IS. Computer experience: Past research considers prior computer education (in high school), personal computer (PC) ownership, computer usage, and years since first contact with computers as key indicators of computing experience [22], [23]. Computer experience and past success may reduce career bias and inspire career choice [9], [24]. We therefore expect student computer experience to associate with IT career intention and the choice to major in IS. Occupational self-efficacy is considered in SCCT to have a direct effect on career choice [16], and is defined as an individual’s belief in their ability to succeed in a given career. Lent et al. [14] consider self-efficacy to influence choice of major directly and indirectly through effects on interest and outcome expectations. Johnson et al. [12] found that students’ perceptions of their ability to work in the IT field correlated with attitude toward IT jobs, and Walstrom et al. [10] identify “I’m not suited for IT type work” amongst the reasons for students not wanting to pursue IT studies or an IT career. We thus expect student occupational self-efficacy to correlate with IT career intention and choice to major in IS. Computer self-efficacy is an individual’s confidence in their computer-related abilities. Research suggests that individuals who have high computer self-efficacy are more likely to be interested in IT studies [8]. We thus expect student computer selfefficacy beliefs to correlate with IT career intention and choice to major in IS. Computer anxiety is the affective response of individuals when they use (or consider the possibility of using) computers and will manifest as worries, apprehensions, tensions and fear [25]. Computer anxious individuals are more reluctant to use computers [23], will generally avoid them [26], and may perform more poorly on computer-based tasks [27]. We consider that a student’s computer anxiety may become a career and educational barrier, which will be inversely associated with IT career intention and choice to major in IS. Attitude toward computers is an individual’s feelings about the usefulness and impacts of computers for society and for their own daily lives [25], [28]. Negative attitudes and lack of interest are frequently cited reasons for why students do not select an IT major [9], [10]. We expect attitude toward computers and their usefulness to correlate with IT career intention and choice of major. Perception of IT career rewards: SCCT suggests that individuals develop interest in activities which they believe will lead to positive outcomes – including anticipated tangible, social and self-evaluative outcomes [15]. Outcome expectations rank amongst the most important considerations in student selection of a major [9], [10], while perceptions of poor career prospects and lack of enjoyable work rank among the reasons cited for not selecting an IS major [29], [30]. We examine student perceptions of both tangible IT career outcomes (e.g. earning an attractive salary, job security) and perceptions of social and self-evaluative outcomes (e.g. doing satisfying work, doing good for society, and having a good home life), and expect them to influence IT career intention and choice of major.

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Through the introductory IS course, IS/IT Faculty can positively influence student attitude, increase perceptions of career rewards [8] and enhance IT interest [6], [7]. Given that the introductory IS course is a core degree requirement for all students participating in this study, we examine whether the introductory course is stimulating student interest in IT.

3 Methods The study was conducted at the authors’ institution - the University of the Witwaters1 rand, Johannesburg . The sampling frame consisted of 536 first year Bachelor of Commerce (BCom) students enrolled in a core introductory information systems course. Each of the students would subsequently have the option to abandon further IS studies or continue with the subject as a major. Students were invited to participate. Participation was completely voluntary and neither penalty nor benefit was attached 2 to their participation . 3.1 Instrument The survey instrument was a structured questionnaire. It was made available to students during their class session and clearance was obtained from the University’s ethics committee. The questionnaire asked demographic questions about age, gender and race. The questionnaire also asked students about their computer experience including a) whether they had taken an IT subject in high-school, b) whether they owned a personal computer (PC), c) the number of years they had been using computers, and d) the average amount of time spent on the computer per day (1=less than one hour to 4=more than 6 hours). Computer self-efficacy was measured using 10 items from Murphy et al. [31] on a 5-point scale. However, after principal components analysis (PCA), five items were removed as they seemed to capture more basic levels of self-efficacy, the remaining five items tapped into a more advanced computing selfefficacy factor and included items measuring student confidence in ability to troubleshoot computer problems, write simple programs, and understand issues related to hardware and software compatibility and the stages of data processing (α = 0.75). General computer anxiety was measured using six items adapted from Nickell and Pinto [28] and Heinssen et al. [25] on a 5-point scale. Items asked about fear and intimidation in relation to computer usage in a work related context (α = 0.87). Computer attitude was measured by asking students the extent to which they agreed with eight statements reflecting positive attitudes toward computers. The scale was adapted from Nickell and Pinto [28]. Following PCA, one item was eliminated and 1

The University of the Witwatersrand, Johannesburg is divided into 5 Faculties comprising 34 Schools. It is a research focused university and, as one of only 5 universities in Africa (universities of Cape Town, Cairo, Natal and Pretoria being the other four) that are listed in the Shanghai Jaio Tong Top 500 rankings, it has an important role to play in Africa’s hopes to become a knowledge society. 2 Over the last decade, enrollments for further study in IS have fallen roughly 70% from their peak at the beginning of the millennium. The largest drop occurred in 2003 where enrollment fell by almost 50%, and in 2004 enrollments toward the major fell below 100 for the first time.

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the remaining seven items captured student attitudes toward the impacts of computers on individuals, business and society (α = 0.83). Perceived IT career rewards were captured through seven items [32] measured on a 5-point scale. Principal components analysis confirmed these items loaded onto two factors with five items reflecting perceived personal and social rewards (α = 0.70) and two items reflecting perceived tangible rewards (α = 0.66). Perceptions of the introductory IS course was measured using three items measured on a 5-point scale to assess the extent to which the various components of the course were stimulating the student’s interest in IT (α = 0.82). Occupational self-efficacy was measured using a single item asking students if they believed they possessed the skills and abilities needed to pursue an IT career. Intention to pursue a career in IT was measured using four items (α = 0.93), which were adapted from Brinkley and Scholar [32]. These items captured student interest and intention to pursue an IT related career. Students were also asked to identify their most likely choice of major, and were provided an opportunity to select from across the options for a BCom major at the university e.g. Accounting, Finance, Economics, Human Resources, Information Systems, Management, and Marketing among others. To facilitate subsequent analysis composite scores were created for the multi-item scales as the arithmetic average of the scale items weighted equally.

4 Findings Roughly half the students (n=263) consented to participate by completing the survey instrument. The proportion of females/males and race groups returning the questionnaire was quite consistent with the overall demographic of the registered students and the sample is considered representative. Approximately 15% of students (n=41) identified themselves as intending to major in IS. This is consistent with the proportion of IS/non-IS majors reported in a recent article [6] but also more promising than others [10]. Students who indicated their intentions were not to major in IS identified their most likely choice of major as Accounting/Finance (n=110) or Business Sciences/Economics (n=100), with very few undecided. 4.1 Demography and Computer Experience Roughly 75% of the students report owning a personal computer (PC). Unfortunately, 2 amongst South African students, PC ownership is not independent of race (χ = 40.15, p 6 hours < 1 year 1-2 years

No Intention to Major in IS (n=222) 199(84.3%) 21(84%) 95(84.1%) 127(84.7%) 142(84.5%) 34 (82.9%) 38 (92.7%) 4 (57.1%) 4 (66.7%) 36 (69.2%) 185 (88.1%) 165 (85.5%) 54 (80.6%) 85(87.6%) 115(82.7%) 15(78.9%) 5(100%) 44(83%) 24(85.7%)

Intention to Major in IS (n=41) 37 (15.7%) 4 (16%) 18 (15.9%) 23 (15.3%) 26 (15.5%) 7 (17.1%) 3 (7.3%) 3 (42.9%) 2 (33.3%) 16 (30.8%) 25 (11.9%) 28 (14.5%) 13(19.4%) 12(12.4%) 24(17.3%) 4(21.1%) 0(0%) 9(17%) 4(14.3%)

2-4 years 4-6 years > 6 years

33(91.7%) 26(81.3%) 93(83%)

3(8.3%) 6(18.7%) 19(17%)

Student Demographics and Computing Experience Age Gender Race

IT subject taken in high school PC ownership Hours of computer use (p/day)

Years of computer use

p-value 0.910 0.895

0.108

0.001 0.344 0.729

0.746

* sum of numbers differs from total n due to missing data in some cases.

Table 2 reveals that intentions to pursue an IT career are generally very low, and in all cases below the scale mid-point of 3 except for those intending to major in IS. Those students that took IT courses in high school are also significantly more interested in pursuing an IT related career. These findings confirm a significant relationship between career intention and educational aspiration. Hours of computer use per day is also associated with intention, with Tukey posthoc analysis confirming a significant difference between the group of students using computers for less than 1 hour per day and the group using computers for between 2 to 3 hours. Similar to Seymour et al. [33], it is interesting to note slightly higher interest amongst students that do not own computers. While SCCT suggests that perceived gender or ethnic discrimination could act as a barrier to career choice, neither gender nor race were directly related to career choice.

62


Table 2. Comparison on Demographics and Computer Experience (Intention to Pursue IT Career) Student Demographics Experience

and

Age Gender Race

IT subject taken in high school PC ownership Hours of computer use (p/day)

Years of computer use

Intention to major in IS

Computing < 20 > 20 Male Female Black White Indian Asian Other Yes No Yes No 0-1 hours 2-3 hours 4-5 hours > 6 hours < 1 year 1-2 years 2-4 years 4-6 years > 6 years Yes No

Mean Career Intention Score ± SD. 2.40 ± 1.1 2.39 ± 1.2 2.49 ± 1.1 2.32 ± 1.1 2.42 ± 1.2 2.26 ± 1.1 2.40 ± 1.1 2.60 ± 0.9 2.55 ± 1.5 2.75 ± 1.3 2.31 ± 1.1 2.36 ± 1.1 2.51 ± 1.9 2.12 ± 1.0 2.56 ± 1.2 2.68 ± 1.3 2.10 ± 1.1 2.60 ± 1.2 2.13 ± 0.9 2.43 ± 1.1 2.59 ± 1.3 2.30 ± 1.2 4.08 ± 0.7 2.08 ± 0.9

t-value (sig.) / F-value (sig.) t=0.027 (p=0.979) t=1.184 (p=0.238)

F=0.237 (p=0.917)

t=2.522 (p=0.012) t=-0.924 (p=0.356) F=3.473 (p=0.017)

F=1.219 (p=0.303)

t=13.188 (p

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