Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding - Volume 151 Frontiers in Artificial Intelligence and Applications

LEARNING BY EFFECTIVE UTILIZATION OF TECHNOLOGIES: FACILITATING INTERCULTURAL UNDERSTANDING Frontiers in Artificial I...

Author: R. Mizoguchi; P. Dillenbourg and Z. Zhu; Editors

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LEARNING BY EFFECTIVE UTILIZATION OF TECHNOLOGIES: FACILITATING INTERCULTURAL UNDERSTANDING

Frontiers in Artificial Intelligence and Applications FAIA covers all aspects of theoretical and applied artificial intelligence research in the form of monographs, doctoral dissertations, textbooks, handbooks and proceedings volumes. The FAIA series contains several sub-series, including “Information Modelling and Knowledge Bases” and “Knowledge-Based Intelligent Engineering Systems”. It also includes the biennial ECAI, the European Conference on Artificial Intelligence, proceedings volumes, and other ECCAI – the European Coordinating Committee on Artificial Intelligence – sponsored publications. An editorial panel of internationally well-known scholars is appointed to provide a high quality selection. Series Editors: J. Breuker, R. Dieng-Kuntz, N. Guarino, J.N. Kok, J. Liu, R. López de Mántaras, R. Mizoguchi, M. Musen and N. Zhong

Volume 151

Recently published in this series Vol. 150. B. Bennett and C. Fellbaum (Eds.), Formal Ontology in Information Systems – Proceedings of the Fourth International Conference (FOIS 2006) Vol. 149. X.F. Zha and R.J. Howlett (Eds.), Integrated Intelligent Systems for Engineering Design Vol. 148. K. Kersting, An Inductive Logic Programming Approach to Statistical Relational Learning Vol. 147. H. Fujita and M. Mejri (Eds.), New Trends in Software Methodologies, Tools and Techniques – Proceedings of the fifth SoMeT_06 Vol. 146. M. Polit et al. (Eds.), Artificial Intelligence Research and Development Vol. 145. A.J. Knobbe, Multi-Relational Data Mining Vol. 144. P.E. Dunne and T.J.M. Bench-Capon (Eds.), Computational Models of Argument – Proceedings of COMMA 2006 Vol. 143. P. Ghodous et al. (Eds.), Leading the Web in Concurrent Engineering – Next Generation Concurrent Engineering Vol. 142. L. Penserini et al. (Eds.), STAIRS 2006 – Proceedings of the Third Starting AI Researchers’ Symposium Vol. 141. G. Brewka et al. (Eds.), ECAI 2006 – 17th European Conference on Artificial Intelligence Vol. 140. E. Tyugu and T. Yamaguchi (Eds.), Knowledge-Based Software Engineering – Proceedings of the Seventh Joint Conference on Knowledge-Based Software Engineering Vol. 139. A. Bundy and S. Wilson (Eds.), Rob Milne: A Tribute to a Pioneering AI Scientist, Entrepreneur and Mountaineer ISSN 0922-6389

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding Edited by

Riichiro Mizoguchi Osaka University, Japan

Pierre Dillenbourg

Swiss Federal Institute of Technology Lausanne, Switzerland

and

Zhiting Zhu

East China Normal University, China

Amsterdam • Berlin • Oxford • Tokyo • Washington, DC

© 2006 The authors. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without prior written permission from the publisher. ISBN 1-58603-687-4 Library of Congress Control Number: 2006936136 Publisher IOS Press Nieuwe Hemweg 6B 1013 BG Amsterdam Netherlands fax: +31 20 687 0019 e-mail: [email protected] Distributor in the UK and Ireland Gazelle Books Services Ltd. White Cross Mills Hightown Lancaster LA1 4XS United Kingdom fax: +44 1524 63232 e-mail: [email protected]

Distributor in the USA and Canada IOS Press, Inc. 4502 Rachael Manor Drive Fairfax, VA 22032 USA fax: +1 703 323 3668 e-mail: [email protected]

LEGAL NOTICE The publisher is not responsible for the use which might be made of the following information. PRINTED IN THE NETHERLANDS

Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding R. Mizoguchi et al. (Eds.) IOS Press, 2006 © 2006 The authors. All rights reserved.

v

Preface Welcome to the 14th International Conference on Computers in Education (ICCE) hosted by Beijing Normal University (BNU) and supported by the Ministry of Education (MOE) and Chinese Association for Artificial Intelligence (CAAI). The series of conferences started in Taiwan in 1989. The three first editions of ICCE occurred there and then the conference moved across many countries in the Asia-Pacific region: China, Singapore, Malaysia, Japan, Korea, New Zealand, Hong Kong and Australia. The ICCE series is organized by the Asia-Pacific Society for Computers in Education (APSCE). The ICCE is an international event, with strong participation from researchers from Asia and Oceania. Since the beginning, there has also been strong involvement from researchers in Europe and North-America. These proceedings include the 45 full papers and 53 short papers that have been selected by an international board of scholars listed hereafter. All papers have been reviewed by three or at least two reviewers and double-checked by the Co-chairs of the programme committee. Altogether 254 papers were submitted, which represents a rate of acceptance of 18% if considering only long papers or 38% if including short papers. The content of this volume hence results from a severe selection process. In addition, 22 posters will be presented at the event. A major theme of this conference is the use of computers for supporting collaborative learning. This is not surprising since computer-supported collaborative learning has become both a widespread educational practice and a main domain of research. Moreover, collaborative learning has deep roots in Asian educational traditions. Given the large number of researchers within this field, its scope has become very broad. Under this umbrella, one finds a variety of more specific topics such as: interaction analysis, collaboration scripts (e.g. the Jigsaw script), communities of practice, sociocognitive conflict resolution, cognitive apprenticeship, various tools for argumentation, on-line discussion or collaborative drawing tools (whiteboards), collaborative writing and the role of facilitators. Most research work on collaborative learning focuses on interactions rather than on the contents of environments, which had been the focus in the previous decades of learning technology research. However, there is no reason to focus on one aspect to the detriment of the other. Hence, we are quite pleased that the selected papers also cover multiple issues related to the storage, representation and retrieval of knowledge: ontologies for learning environments and the semantic web, knowledge bases and data mining, meta-data and content management systems, and so forth. These proceedings also reveal a growing interest for non-verbal educational material, namely pictures and video materials, which are already central to new popular web-based applications. Interestingly, these proceeding include contributions that bridge both research tracks, the one focusing on interactions and the other on contents: the pedagogical use of digital portfolios, both for promoting individual reflections and for scaffolding group interactions. Another specificity of these proceedings, maybe due to regional policies, is the salience of language learning within the range of educational contents covered by learning technologies.

vi

Among the other research trends that appear in the set of contributions, we find the educational use of mobile technologies and the design of educational games. The use of mobile devices and games opens new ways to think about learning technologies, namely the fact that tools that do not a priori appear as learning tools have perhaps more chances to enter into schools. There are nonetheless a limited number of papers on these issues since new conferences emerged in our community, which are devoted to these two subsets of learning technologies. Finally, let us stress the truly interdisciplinary character of this volume. It contains contributions from the field of computer science, psychology and educational sciences, three fields that are sometimes bound together under the label of ‘learning sciences’. The very positive aspect is that computational, pedagogical, cognitive or social factors are not only treated by different papers, but, in many cases, tackled within the same paper. It is our great pleasure to have gathered these rich contributions within this volume. We hope all readers will share our enthusiasm for this exceptional event. Program Committee Co-Chairs Riichiro Mizoguchi, Osaka University, Japan Pierre Dillenbourg, Swiss Federal Institute of Technology Lausanne, Switzerland Zhiting Zhu, East China Normal University, China

vii

Program Committee Conference Chair Fong-Lok Lee, The Chinese University of Hong Kong, Hong Kong Program Co-Chairs Riichiro Mizoguchi, Osaka University Pierre Dillenbourg, Swiss Federal Institute of Technology Lausanne Zhiting Zhu, College of Educational Sciences, East China Normal University Committee Members Lora Aroyo, Eindhoven University of Technology, The Netherlands Nelson Baloia, Chille University, Chile Jacqueline Bourdeau, Tele-universite du Quebec, Canada Tak-Wai Chan, National Central University, Taiwan Yam San Chee, National Institute of Education, Singapore Jim Greer, University of Saskatchewan, Canada Paivi Hakkinen, University of Jyvaskyla, Finland Yusuke Hayashi, Osaka University, Japan Lyn Henderson, James Cook University, Australia Tsukasa Hirashima, Hiroshima University, Japan Chris Hoadley, Penn State University, USA Ulrich Hoppe, University Duisburg, Germany Ronghuai Huang, Beijing Normal University, China Mitsuru Ikeda, JAIST, Japan Michael Jacobson, Nanyang Technological University, Singapore Akihiro Kashihara, The University of Electro-Communications, Japan Yong-Se Kim, Sungkyunkwan University, Korea Gee Kin Yeo, National University of Singapore, Singapore Kinshuk, Massey University, New Zealand Paul Kirschner, University of Utrecht, The Netherlands Piet Kommers, University of Twente, The Netherlands Fong-Lok Lee, The Chinese University of Hong Kong, Hong Kong Insook Lee, Sejong University, Korea Mun Kew Leong, Institute for Infocomm Research, Singapore Robert Lewis, University of Lancaster, UK Chee-Kit Looi, National Institute of Education, Singapore Tatsunori Matsui, Waseda University, Japan Gordon McCalla, University of Saskatchewan, Canada Naomi Miyake, Chukyo University, Japan Kiyoshi Nakabayashi, NTT Resonant, Inc., Japan Eugenia Ng, Hong Kong Institute of Education, Hong Kong Hiroaki Ogata, University of Tokushima, Japan Ron Oliver, Edith Cowan University, Australia Michael Sharples, University of Nottingham, UK

viii

Kathy Sinitsa, International Research and Training Center of Information Technologies and Systems, Ukraine Raymund Sison, De La Salle University, Philippines Amy Soller, Institute for Defense Analyses, Italy Gerry Stahl, Drexel University, USA Dan Suthers, University of Hawai’i at Manoa, USA Seng Chee Tan, Nanyang Technological University, Singapore Pierre Tchounikine, Université du Maine, France Qiong Wang, Peking University, China Lu Wang, Department of Educational Technology, Capital Normal University, China Yoneo Yano, Tokushima University, Japan Shelley Shwu-Ching Young, National Tsing Hua University, Taiwan Fu-Yun Yu, National Cheng-Kung University, Taiwan Shengquan Yu, College of Educational Technology, Beijing Normal University, China

ix

Additional Reviewers TSE Wing-Cheung Alex Liesbeth Baartman Ryan S.J.D. Baker Vincent Barre Moushir Bishouty Lars Bollen Chris Brook Christopher Brooks Ole C. Brudvik Jan M. Van Bruggen Murat Cakir Hyun Jin Cha Sung-Bin Chang Elizabeth S. Charles Elizabeth Charles Yu-Fen Chen Nancy Yu-Chien Chen Weiqin Chen Zhi-Hong Chen Emily Ching Winnie Chow Catherine Cleder Steve Corich Charlie Cox Nathanael Ng Hsien Ern Stuart Garner Giulia Gelmini Bas Giesbers Sabine Graf Yoshiaki Hada Kim Hagen Andreas Harrer Elizabeth Hartnell-Young Shinobu Hasegawa Toshihiro Hayashi Tomoya Horiguchi Chris Houser Gan Li Hui Yang Hui Sebastien Iksal Wai-Hung Ip Hyunju Jeung

Morris S.Y. Jong Laltitha Jonnavithula Ilkka Jormanainen Marco M.A. Kalz Toshinobu Kasai Fengfeng Ke Tomoko Kojiri Siu Cheung Kong Oskar Y.M. Ku Hidenobu Kunichika Pierre Laforcade Richard Chih-Hung Lai Yiu Chi Lai Joey Lee Youngmin Lee Michael Hung-Liang Lee Joey J. Lee Kai Ming Li Yanyan Li Calvin Liao Taiyu Lin Yimei Lin Chiu-Pin Lin Weng-Jeng Liu Joe Luca Wai Wing Ada Ma Noriyuki Matsuda Noboru Matsuda Kenji Matsuura Mark Mcmahon Takahiko Mendori Carrie E. Miller Hiroyuki Mitsuhara Akiyoshi Miyatake Yasuo Miyoshi Tomiaki Morikawa Martin Muhlpfordt Manabu Nakamura Nathanael H.E. Ng Trang Nguyen Youji Ochi Ryo Okamoto

Shuji Okamura Josephine Pelleu-Tchetagni Dominique Py David Raymond Valerie Renault Raul Morales Salcedo Johann W. Sarmiento Hong Khai Seng Junjie Shang Fawaz Shareef Hajime Shirouzu Kati Maekitalo-Siegl Oyvind Smestad Hyo-Jeong So Chiann-Ru Song Soonshik Suh Kazumoto Tanaka Philippe Teutsch Patricia Thornton Satoshi Togawa Ramon Prudencio S. Toledo Yuen Tsui Michael Verhaart Jen-Hang Wang Eric Wang Astrid Wichmann Lung-Hsiang Wong Winston Wu Jingyu Yang Cheng-Yen Yeh Yau Yuen Yeung Chengjiu Yin Eric Yu Fu-Yun Yu Fei Yuan Alan Zemel Yuejun Zhang Zhenhong Zhang Yan Zhang Guoqing Zhao Nan Zhou

x

Organizing Committee Organizing Committee Chair Ronghuai Huang, Beijing Normal University, China Tutorials Chair Li Chen, Beijing Normal University, China Workshops Chair Tak-Wai Chan, Graduate Institute of Network Learning Technology, Taiwan Workshop Local Organization Chair Huanglingzi Liu, Beijing Normal University, China Doctor Student Consortium Chair Lora Aroyo, Eindhoven University of Technology, The Netherlands Exhibition Chair ShengQuan Yu, Beijing Normal University, China Organizing Committee Secretariat Zheng Chen, Beijing Normal University, China Yan Dong, Beijing Normal University, China Ping Li, Beijing Normal University, China Qian Li, Beijing Normal University, China Jiaoyang Guo, Beijing Normal University, China Jingbin Zhang, Beijing Normal University, China Lanqin Zheng, Beijing Normal University, China

Sponsors Ministry of Education, the People’s Republic of China Chinese Association for Artificial Intelligence

xi

Contents Preface Riichiro Mizoguchi, Pierre Dillenbourg and Zhiting Zhu Program Committee

Additional Reviewers

Organizing Committee

v vii ix

x

Keynote Speeches The Design of Effective Simulation-Based Inquiry Learning Environments Ton de Jong

E-Learning Evolution: From M-Learning to Educational Semantic Web and Beyond Cui Guangzuo, Yang Gongyi, Chen Hu, Fei Chen and Guo Jiuling Designed Collaboration as a Scaffold for Schematic Knowledge Integration Naomi Miyake

Technology Affordances for Intersubjective Meaning-Making Daniel Suthers

3 7 15 21

Modeling and Representation Design of an Environment for Developing Presentation Skills Kazuhisa Seta and Mitsuru Ikeda

Ontological Modeling Approach to Blending Theories for Instructional and Learning Design Yusuke Hayashi, Jacqueline Bourdeau and Riichiro Mizoguchi A Case of Blending Learning in Computer Teaching –– The Model and the Application Li Cuiling, Hong Wang and Huiyu Zhang A Combined Method for Extracting Rules with Improved Quality Fuyan Liu

29 37 45 49

Programming The Impact of CABLE on Teaching Computer Programming Ioana Chan Mow, Wing K. Au and Gregory C.R. Yates

Problem Solving Process Oriented Diagnosis in Logic Programming Nguyen-Thinh Le and Wolfgang Menzel

55 63

xii

Programming Teaching Support System Using Student Model KeunWoo Han, EunKyoung Lee and YoungJun Lee

A Method for Creating Teaching Materials of Practical Object-Oriented Methods Education Izuru Kume, Naoya Nitta and Yasuhiro Takemura

71 75

Science Education at School Science Net: Effects of an e-Learning System on Elementary School Students’ Self-Regulated Learning in Science Classes Takeshi Kitazawa, Masahiro Nagai, Hiroshi Kato and Kanji Akahori Using Satellite Resources for Scientific Inquiry Beaumie Kim, Manetta Calinger and Debbie Denise Reese

Experimental Researches on Development of Pupils’ Advanced Cognitions in PRIME Environments Zhou Yueliang, Lianghui Wang and Xiuqin Lin

81 89 97

ITS, et al. Teaching Chinese Handwriting by Automatic Feedback and Analysis for Incorrect Stroke Sequence and Stroke Production Errors Kai-Tai Tang and Howard Leung

Developing a Practical Domain Knowledge Base and Problem Solving System for Intelligent Educational System of High School Chemistry Nana Ishima, Toru Ueda, Tatsuhiro Konishi and Yukihiro Itoh

The COLAC Model: Collaborative Paper-Writing in the Humanities Guillaume Schiltz and Andreas Langlotz

Experimental Investigation and Implementation of Support for Problem Generation by Presenting Cases Kazuaki Kojima and Kazuhisa Miwa A Computer-Based Environment for Learning by Problem-Posing as Sentence-Integration Tsukasa Hirashima, Takuro Yokoyama, Masahiko Okamoto and Akira Takeuchi

107 115 119 123 127

Discussion The Impact of Structured Discussion on Students’ Attitudes and Dispositions Toward Argumentation Khai Seng Hong, Ole C. Brudvik and Yam San Chee

Assessing the Impact of a Structured Argumentation Board on the Quality of Students’ Argumentative Writing Skills Ole C. Brudvik, Khai Seng Hong, Yam San Chee and Libo Guo

133 141

xiii

Incorporating Online Discussion in Classroom Learning: A New Strategy Wenli Chen and Chee Kit Looi

Using Agents for Enhancing Learning Effects in an Advanced Discussion Forum Yuejun Zhang, Kinshuk, Øyvind Smestad, Jingyu Yang and Lynn Jeffery

149 157

Emotion & Personality Research on Personality Mining System in E-Learning by Using Improved Association Rules Luo Qi, Yanwen Wu, Liyong Wan and Ying Yu

Analysis on Relationships of Emotional Transmissions Between Participants and Their Emotional Aspects in Communication Using Bulletin Board System Shogo Kato, Yuuki Kato and Kanji Akahori

Learning Nonverbal Emotion Interaction in 3D Intelligent Virtual Environment for Children Zhen Liu Development of Know-How Information Sharing System in Care Planning Processes Kaoru Eto, Tatsunori Matsui and Yasuo Kabasawa

167 171 175 179

CSCL Student Learning and Team Formation in a Structured CSCL Environment Nobel Khandaker, Leen-Kiat Soh and Hong Jiang

An Integrated Framework for Fine-Grained Analysis and Design of Group Learning Activities Seiji Isotani and Riichiro Mizoguchi

The Development of a Grouping System in a Collaborative Learning Environment Pao-Ta Yu, Yen-Shou Lai, Chia-Ming Liu and Jenq-Muh Hsu

Students’ Understandings and Attitudes Toward Group Learning: An Empirical Study Jianhua Zhao and David McConnell

The Effectiveness of Knowledge Building Through Computer Supported Collaborative Learning Among Elementary Students: A Case Study Wing Cheung Alex Tse, Fong Lok Lee and Yong Ou

Computer-Supported Content Analysis for Collaborative Knowledge Building in CSCL Jian Liao, Yanyan Li, Ying Zhou, Ronghuai Huang and Jingjing Wang

A Novel Web-Based Collaborative Learning Supporting System with Navigation Function Tian Chenyuan, Zuoliang Chen and Shigayeshi Watanabe

185 193 201 205 209 217 225

xiv

Towards Auto-Coding of Collaborative Interaction Texts Based on Maximum Entropy Approach Jian Liao, Ronghuai Huang, Yanyan Li, Jingjing Wang and Jing Leng

Web Based Collaborative Environment for Engineering Graphics Education Lianguan Shen, Mujun Li, Xiaodong Wang, Wei Zhao and J.J. Zheng Time-Based Self-Learning Support Using Collaborative Learning Process Masahide Kakehi, Tomoko Kojiri and Toyohide Watanabe

The Design of a Collaborative Learning Environment in a Mobile Technology Supported Classroom: Concept of Fraction Equivalence Siu Cheung Kong

Scientific Modeling of Technology-Mediated Collaborative Learning Processes Yau-yuen Yeung Exploring the Learning Effect of a Web-Based Learning Community on EMBA Students I-Fan Liu, Meng Chang Chen and Yeali Sun Development of a Discussion Board System Designed for the Group Discussion That Includes Peer-Review Process Shigeru Sasaki and Hiroyoshi Watanabe

229 233 237 241 249 257 261

Interface Improving Creativity for Mathematical Problem Solving Using Web-Based Multimedia Whiteboard System Wu-Yuin Hwang, Nian-Shing Chen, Jian-Jie Dung and Yi-Lun Yang

Developing a VR-Based Projectile System Using Haptic Device for Learning Physics Atsushi Kanbe, Yukihiro Matsubara, Noriyuki Iwane and Kimiko Hirayama Proposal for Digital Partners Project Chris Davies and Jingjing Zhang

267 275 283

Assessment and Evaluation Development of Portfolio Assessment Support System Yasuhiko Morimoto, Isao Kikukawa, Maomi Ueno, Setsuo Yokoyama and Youzou Miyadera

Peer-Assessment in Web-Based ePortfolios System: An Experimental Study Youmei Wang Construction and Performance Evaluation of High Quality Curriculum Integrated with Information Technology YouRu Xie and Rui Yin

Effectiveness of WebQuest in the Teaching of STS in Secondary Biology Ka-leung Tse and Sai-wing Pun

289 297 305 309

xv

Mobile and Web-Based Learning Effects of Using Digital Contents Designed for PDA as a Teaching Aid in an Observational Learning of Planktons for Fieldworks on a Ship Hitoshi Miyata and Mitsuo Ishigami

A Study of Message Reading Efficiency of Color Screen Mobile Phones Xuemin Zhang, Bo Wang, Lilin Rao, Bin Yang, Yongna Li and Xueming Lu

The Design of a Web-Based Learning Platform: A Case Study in Taiwan I-Fan Liu, Meng Chang Chen and Yeali Sun

A Study of Implementing Web-Based Learning Systems to Enhance Learning for the Supply Chain Management (SCM) Course in Higher Education I-Fan Liu and Shelley S.-C. Young

What Is Expected of a Facilitator in a Virtual Learning Environment? Ni Chang

315 323 327 331 335

Social Networking & Blog Proactivity, Autonomy & Social Networking: Transitional Environments for the Japanese Educational Context Deborah C. Turk and John W. Brine

341

Effects of Peers Interactivity and Self-Regulated Learning Strategies on Learning Art Appreciation Through Weblog Sau Hung Cheung and Percy Lai Yin Kwok

349

Understanding Asynchronous Teaching and Learning Dialogues – An Integrative Approach E. Vass, F. Concannon, M. LeVoi, K. Littleton and D. Miell

357

An Application of Social Network Analysis in Evaluation of CSCL Yonggu Wang and Kedong Li

353

Cultural Issues Participatory Agent-Based Gaming Methodology in Cross-Cultural Education: Exploring Efficient and Sustainable Civil Society and Community Reiko Hishiyama and Toru Ishida Internet for Senior Citizens in China: Survey and Proposal Wei Zhou, Takami Yasuda and Shigeki Yokoi

Why Do Students Engage in e-Learning: A Chinese Perspective Zhenhong Zhang and Ronghuai Huang

Enabling a Multilateral Distance Class Between China, Korea and Japan: Effective Utilization of Networking Technologies Yuri Nishihori, Keizo Nagaoka, Nozomu Nishinaga, Kenji Tanaka, Yuichi Yamamoto, Haruhiko Sato, Masahiro Harada, Ruimin Shen, Jinjin Feng and Myunghee Ju Kang

363 371 379 383

xvi

Content and Knowledge Management Digital Video Database: Supporting Student Teachers’ Learning About Teaching During Teaching Practice Winnie So, Vincent Hung and Walker Yip

Building and Evaluation of a Semantic Web System That Provides Teachers with Lesson Plans Toshinobu Kasai, Haruhisa Yamaguchi, Kazuo Nagano and Riichiro Mizoguchi An Improved Learning Content Management System Framework Liyong Wan, Chengling Zhao, Ming Zhao, Luo Qi and Libing Jiang Resource Based Solution to Teachers’ Knowledge Management Sun Hongtao, Lu Wang and Hongwei Dai

Collaborative Building of Japanese Kanji Pronunciation Database for Learning Japanese by Chinese Fei Yuan, Jing Yuan, Rong Wang, Hiroyuki Mitsuhara, Kazuhide Kanenishi and Yoneo Yano

Using IT to Power and Support Problem-Based Engaged Learning Wee-Meng Hoe and Irene Tan

389 397 405 409 413 417

IT at School & Teacher Training A Study of Innovative Uses of ICT in Primary Education Shelley S.-C. Young and Hsin-Ho Ku

Impacts of Grade 7-9 Students’ Computer Usage After School on Academic Achievement: A School Case Study Yiu Fai Wong and Percy Lai Yin Kwok Using Interactive Whiteboards (IWB) to Enhance Learning and Teaching in Hong Kong Schools Fong-lok Lee, Sai-wing Pun, Sandy Siu-cheung Li, Siu-cheung Kong and Wai-hung Ip

Conditions Facilitating the Implementation of Information Communication Technology Integration in Malaysian Smart School Wan Zah Wan Ali, Hajar Mohd Nor, Azimi Hamzah and Nor Hayati Alwi A Study of the Present Status of IT Teachers Training in Microsoft’s ‘Partners in Learning’ Project –– A Content Analysis Approach GuiJing Huang, Yong Xu, JinBao Zhang and XiaoYuan Wang

inPD: An Emerging Theoretical Framework for Educational Professional Development in the Information Age Simon Hughes

423 431 439

443

447 451

xvii

Reflection and Self-Directed Learning Automated Mentoring for Reflection in an Eportfolio Tzemin Chung, Mun Kew Leong and Joel P.L. Loo

Guided Map for Scaffolding Navigation Planning as Meta-Cognitive Activity in Hyperspace Akihiro Kashihara, Mitsuyoshi Nakaya and Koichi Ota Self-Directed Learning in Technology Supported Project Work Allan H.K. Yuen and Liping Deng

Multi-Step Annotation to Promote Reflective Learning with a Mobile Phone N. Gotoda, K. Matsuura, K. Kanenishi, K. Niki and Y. Yano

457 465 473 477

Game and Edutainment Property Exchange Method for Automatic Generation of Computer-Based Learning Games Takanobu Umetsu, Tsukasa Hirashima and Akira Takeuchi Development and Validation of an Animation-Based Test in the Area of Earth Sciences Huang-Ching Wu and Chun-Yen Chang

Weaving Pedagogy into Gaming: Learning Design Principles for Developers Yam San Chee, Yi Liu and Khai Seng Hong VR Edutainment Material Interlude for Dynamics Experiment and the Development Platform Prelude Yuma Hanafusa, Takashi Inoue, Hiroyuki Tominaga, Toshihiro Hayashi and Toshinori Yamasaki Using the “Record-Replay” Function for Elaboration of Knowledge in Educational Games Junjie Shang, Morris S.Y. Jong, Fong-Lok Lee, Jimmy H.M. Lee, Marti K.H. Wong, Eric T.H. Luk and Kevin K.F. Cheung

483 491 495 499

503

Participation/Attitude Toward Learning Investigating Learner Autonomy Toward e-Learning Shu-Sheng Liaw and Hsiu-Mei Huang

Moderating Role of Online Self-Efficacy in Relation Between Learning Strategy and Online Performance Huamao Peng, Ying Wang and Ronghuai Huang

Understanding E-Learners’ Characteristics and Performance in Online Courses Rowena Santiago and Minoru Nakayama An Exploratory Study on Teachers’ Perceptions of Game-Based Situated Learning Morris S.Y. Jong, Junjie Shang, Fong-Lok Lee, Jimmy H.M. Lee and Huk-Yuen Law

509 517 521 525

xviii

Visualization Visualizing Errors for Self-Correcting Discrepancy Between Thinking and Writing Hidenobu Kunichika, Tsukasa Hirashima and Akira Takeuchi

Using Systematic Animation to Teach Dynamic Science Concepts Othman Talib, Christirani Azhar Shah and Nabila Abdullah

Creating Animations in SVG Format for Visualizing Program Execution Koji Kagawa

Effects of the Voice Recognition on the Writing of Students with Learning Disabilities Hu Lailin

535 543 551 555

Curriculum Designing a Teacher Professional Knowledge Base and Its Operation Model Based on School-Based Curriculum Development Yih-Ruey Juang, Tak-Wai Chan and Tzu-Chien Liu

561

Development of a Photo Management System in Schools Which Ensures Students Appear Equally Kyoko Umeda, Shinsuke Takito, Tetsuro Ejima and Hironari Nozaki

577

Dynamic Composition of Curriculum for Personalized E-Learning Yanyan Li and Ronghuai Huang

569

Sense-Making and Facilitation Probing Technology as Affordances for Negotiating Meaning in the Elementary Science Classroom –– A Participation Perspective Fei-Ching Chen, Huo-Ming Jiang, Jie-Chi Yang and Yu-Wei Lee

Analysis of Meaning Making in Online Learning Daniel Suthers, Nathan Dwyer, Richard Medina and Ravi Vatrapu

Facilitating Knowledge Construction by Providing Individualized Services Weidong Pan and Igor Hawryszkiewycz

The Role of On-Line Facilitators: Types of Collaborative Skills for Effective E-Learning Activities Tengku Putri Norishah Tengku Shariman and Habibah Abdul Jalil

587 595 603 611

PBL and Test Fostering Project-Based, Active Learning Through Use of Technology Teresa Bader and Teh-yuan Wan Framework for Problem-Solving Based Learning in Nursing Domain – A Practical Study – Yukie Majima, Yoichiro So and Kazuhisa Seta

621 625

xix

A Polytomous Computerized-Adaptive Testing That Rewards Partial Knowledge Yung-Chin Yen, Rong-Guey Ho and Li-Ju Chen

Research on Algorithm of Computer Adaptive Test Using Optimized MDPLTM Jin-Ling Li, Feng-lin Wang and Wang-Xiu Li

629 637

Doctor Student Consortium (DSC) Papers Cognitive Maps-Based Student Model Alejandro Peña, Humberto Sossa and Agustín Gutiérrez

643

Improving Quality of Online Forum Interactions in Distance Higher Education Zhenhong Zhang and Ronghuai Huang

645

Annotation in Information Research for Decision Making Robert Charles and David Amos

649

Applying Weighted Learning Object to Build Adaptive Course in E-Learning Anh Nguyen Viet and Dam Ho Si

Effects of the Use of Graphic Calculators on Cognitive and Metacognitive Domains in Teaching and Learning of Mathematics N. Mohd. Tajudin, R. Ahmad Tarmizi, W.Z. Wan Ali and M.M. Konting Improve Effectiveness of Dialogue in Learning Communities Jingyu Yang and Kinshuk

Validation of the Mathematics Courseware Usefulness Evaluation Instrument S.A. Noraidah, A.G. Abdul Azim, S. Hasan and M.Y. dan Aida Suraya Expert Tutoring and Natural Language Feedback in Intelligent Tutoring Systems Xin Lu Learning Environment for Designing Physics Experiment: DEEP Takahito Toumoto, Tomoya Horiguchi, Tsukasa Hirashima and Akira Takeuchi

647

651 653 655 657 659

Standards, Adaptation & Pedagogy: Quality Assessment in e-Learning Silvia Sanz-Santamaría, Julián Gutiérrez Serrano and José A. Vadillo Zorita

661

Author Index

663

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Keynote Speeches



3

The Design of Effective Simulation-Based Inquiry Learning Environments Ton de Jong Faculty of Behaviorial Sciences, University of Twente, The Netherlands [email protected] Abstract: Modern theories on learning and instruction call attention to learning environments that create constructivistic, situated, and collaborative learning experiences. Simulations offer specific features that enable self-directed, highly autonomous, high interaction learning. First, learning in these environments differs from learning in expository environments in that it puts a higher emphasis on inquiry processes such as hypothesis generation and testing and on regulative processes such as planning and monitoring. Second, these environments offer specific opportunities to situate learning in realistic settings, but they also offer the possibility to adapt reality to support learning. Third, inquiry learning presents opportunities for negotiation and collaboration. This presentation will set out characteristics of simulations discuss characteristic inquiry processes and associated problems, and examine what is needed to design effective inquiry learning environments.

Introduction New types of (on-line) learning environments are becoming available for use in the actual classroom rapidly. Development that nowadays dominate the field of learning and instruction are constructivism, situationism, and collaborative learning. More specifically, we can say that the new view on learning entails that students are encouraged to be active in construcingt their own knowledge, in realistic situations, together with others. Technology plays a major role in implementing these new developments in education. Constructivism is supported by computer environments such as hypertexts, concept mapping, simulation, and modeling tools (see de Jong & van Joolingen, 1998), realistic situations can be brought into the classroom by means of for example video or even virtual reality, and collaborative learning is supported in Internet based learning environments such as Co-Lab (van Joolingen, de Jong, Lazonder, Savelsbergh, & Manlove, 2005). In this presentation I will focus on the riole of computer simulations in relation to inquiry learning.

1. Characteristics of computer simulations Computer simulations are programs that hold a computable model of some kind of reality. Students can manipulate the simulated reality by changing values of input variables, and the output is usually displayed in several formats (e.g., animations, graphical displays, and numbers). These characteristics make simulations well suited for implementing the trends above. First, simulations elicit a learning process that is called inquiry learning. In inquiry learning, a domain is not directly offered to students; rather students have to induce characteristics of the domain from experiences or examples. Inquiry learning can be defined as an approach to learning that involves a process of exploration, that leads to asking

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T. de Jong / The Design of Effective Simulation-Based Inquiry Learning Environments

questions and making discoveries in the search for new understandings (National Science Foundation, 2000). This is a learning approach that is in line with contructivistic principles. Second, simulations can easily be used to introduce realities in the classroom; interfaces of simulations may mimic any reality. Third, inquiry learning is very appropriate for collaborative learning, as is real, scientific, inquiry since it requires students to make decisions along the way (e.g., which hypothesis to test) that are good anchor points for knowledge exchange and negotiation (Gijlers & de Jong, 2005).

2. Inquiry learning Though simulations seem to be able to take a central role in realizing the above mentioned trends in education, students do have considerable trouble in realizing an effective inquiry process. Knowledge about the inquiry process and the related problems students experience may help to design adequate cognitive scaffolds.

2.1 Inquiry learning processes Although there may be some variations, for example in the way data are gathered (e.g., from experimentation or from data sets), and variations in the complexity of the experimentation, there is a fair consensus about which processes basically comprise inquiry learning. The different classifications in the literature differ mainly in their granularity, ranging from very detailed to rather broad, but basically do not differ in the processes that are distinguished. In de Jong (2006b), I introduced a set of learning processes that form a suitable basis for describing inquiry learning: In orientation, the student makes a broad analysis of the domain; in hypothesis generation, a specific statement (or a set of statements, for example, in the form of a model) about the domain is chosen for consideration; in experimentation, a test to investigate the validity of this hypothesis or model is designed and performed, predictions are made and outcomes of the experiments are interpreted; in conclusion, a conclusion about the validity of the hypothesis is drawn or new ideas are formed; and, finally, in evaluation, a reflection on the learning process and the domain knowledge acquired is made.

2.2 Problems that students experience In a review of research, de Jong and van Joolingen (1998) concluded that students may have serious problems with all of the above mentioned inquiry learning processes. In general, students may have trouble stating hypotheses, designing experiments, and interpreting data; they often do not engage in overall planning and do not adequately monitor what they have been doing (de Jong & van Joolingen, 1998). These inquiry process problems may be associated with wrong mental models of systems in general (Kanari & Millar, 2004; Kuhn, Black, Keselman, & Kaplan, 2000) and it may lead to a misinterpretation of experimental outcomes from the experiments that were performed in the inquiry (Chinn & Brewer, 1993). On this basis and also based on overall research in inquiry learning many researchers, therefore, conclude that students need guidance in the discovery process (de Jong, 2006a; Mayer, 2004).


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2.3 Providing students with cognitive scaffolds In the software students can be supported with cognitive tools or cognitive scaffolds to ensure an effective inquiry learning process. An example of this is providing students with assignments. These assignments help to students in their planning activities and they help to focus on relevant aspects of the simulation (van Joolingen & de Jong, 2003). Another example is a monitoring tool. A monitoring tool helps students to save all their experiments, to re-order, and to replay them. A hypothesis scratchpad offers students elements (variables, relations, conditions) for composing hypotheses. Students can also be provided with hints on how to experiment, or on how to reflect over the knowledge that is acquired. Another way to support students is to offer them just-time background information or explanations. Finally, the inquiry process can be subdivide in several phases and for every phase students can be offered a specific structure to work in. Extensive overviews of cognitive tools or cognitive scaffolds can be found in Quintana et al. (2004) and de Jong (2006b).

3. Conclusion Large scale environments show that inquiry learning based on simulations can be an effective learning process (Hickey & Zuiker, 2003; Ketelhut, Dede, Clarke, & Nelson, 2006; White & Frederiksen, 1998) . However, the inquiry process needs to be scaffolded to reach these results. This scaffolding can be offered in the software, but could also be offered by the teacher or a co-learner. Combined with more realistic interfaces simulations can indeed offer educational opportunities that combine contructivist, collaborative, and situational characteristics. For sure, to give inquiry learning a place in the curriculum a balance needs to be found between inquiry learning and other ways of learning and instruction so that an integrated, attractive, and effective curriculum results.

References Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1-51. Cognition and Technology Group at Vanderbilt. (1997). The Jasper project; Lessons in curriculum, instruction, assessment, and professional development. Hillsdale (NJ): Lawrence Erlbaum Associates. de Jong, T. (2006a). Computer simulations - Technological advances in inquiry learning. Science, 312, 532-533. de Jong, T. (2006b). Scaffolds for computer simulation based scientific discovery learning. In J. Elen & R. E. Clark (Eds.), Dealing with complexity in learning environments (pp. 107-128). London: Elsevier Science Publishers. de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179-202. Gijlers, H., & de Jong, T. (2005). The relation between prior knowledge and students’ collaborative discovery learning processes. Journal of Research in Science Teaching, 42, 264-282. Hickey, D. T., & Zuiker, S. (2003). A new perspective for evaluating innovative science learning environments. Science Education, 87, 539-563. Kanari, Z., & Millar, R. (2004). Reasoning from data: How students collect and interpret data in science investigations. Journal of Research in Science Teaching, 41, 748-769. Ketelhut, D. J., Dede, C., Clarke, J., & Nelson, B. (2006). A multi-user virtual environment for building higher order inquiry skills in science. Paper presented at the American Educational Research Association, San Francisco. Kuhn, D., Black, J., Keselman, A., & Kaplan, D. (2000). The development of cognitive skills to support inquiry learning. Cognition and Instruction, 18, 495-523. Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59, 14-19.

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National Science Foundation. (2000). An introduction to inquiry. In Foundations. Inquiry: Thoughts, views and strategies for the K-5 classroom. (Vol. 2, pp. 1-5). Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., et al. (2004). A scaffolding design framework for software to support science inquiry. The Journal of the Learning Sciences, 13, 337-387. van Joolingen, W. R., & de Jong, T. (2003). SimQuest: Authoring educational simulations. In T. Murray, S. Blessing & S. Ainsworth (Eds.), Authoring tools for advanced technology educational software: Toward cost-effective production of adaptive, interactive, and intelligent educational software (pp. 1-31). Dordrecht: Kluwer Academic Publishers. van Joolingen, W. R., de Jong, T., Lazonder, A. W., Savelsbergh, E., & Manlove, S. (2005). Co-Lab: Research and development of an on-line learning environment for collaborative scientific discovery learning. Computers in Human Behavior, 21, 671-688. Vreman-de Olde, C., & de Jong, T. (2006). Scaffolding the design of assignments for a computer simulation. Journal of Computer Assisted Learning, 22, 63-74. White, B. Y., & Frederiksen, J. (1998). Inquiry, modelling, and metacognition: making science accessible to all students. Cognition and Instruction, 16, 3-118.


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E-learning Evolution: From M-learning to Educational Semantic Web and Beyond Cui Guangzuo, Yang Gongyi, Chen Hu, Chen Fei, Guo Jiuling Modern Education Technology Center, Peking University, China [email protected] Abstract: An e-learning model with ICT technology (ELM) is proposed in this paper. With this model, some education technologies and e-learning evolution are interpreted, such as network education, mobile education, ubiquitous education, educational semantic web, and etc. In the meantime, the way of how to combine new ICT technologies into education is also demonstrated. After discussing the convenience and challenge of various education technologies, a new model called intelligent education is introduced and some recent research results are presented. At last, the author looks ahead the future of information technology and human related disciplines and their effects on education. Keywords: E-learning, M-learning, Educational Semantic Web, Intelligent Education

1. E-learning Model with ICT Technology E-learning means the education technology enhanced by ICT technology. With the development of new ICT technologies, e-learning has been improved continuously, and as a result, new education technologies emerged and named after the corresponding ICT technology, such as network education, mobile education, ubiquitous education and educational semantic web. What will the ICT technology tend to and what will be the next education technology? To deal with this, a new model for e-learning technology is proposed, depicted as figure1. In ELM model, ICT technology enhances education in the following way: information technology is used to design and present course content, and communication Figure1 E-learning Technology Model technology is used as an interaction method among educate, Educatee and educational agent. With the information technology’s evolution from data model to information model and now to knowledge representation, and communication technology’s evolution from wire to wireless and now to mobile communication, the e-learning technology evolutes from CAI, network education, mobile education, ubiquitous education to educational semantic web and so on. In the following, these e-learning technologies are demonstrated.

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C. Guangzuo et al. / E-Learning Evolution: From M-Learning to Educational Semantic Web

2. From M-learning to Educational Semantic Web Network Education: with fixed wired network and information model. Mobile Education (also named m-learning) [1-6]: Courseware is designed with data model and information model; communication with mobile network. So, the interaction can be completed anywhere and anytime with text message. On the other hand, the courseware can not be accessed by some devices with different contexts, such as different screen size. So, in m-learning, not all the devices can access the published course content. Ubiquitous Education (also called u-learning): is almost the same as m-learning except Figure2 Portal for PC Screen that in u-learning universal information access (UIA) is required [19]. UIA means that the published courseware can be accessed anywhere, anytime, any network and by any device. UIA has been one unresolved key problem in ubiquitous computing which is also called device independence. The related factors which affect UIA include input device, output device, network type, device screen, OS type, application tool, human preference, dynamic environment, unpredictable action, and etc. To demonstrate the difficulty of UIA, we give an explanation. Figure2 is an education portal designed for PC which looks very well with PC screen. But what about is it when accessed with PDA or Hand Hold? It is terrible. So what kind of resource should course content be for UIA? Let’s look at an example of figure3.

Figure3 Adaptation of SCORM Resource

Figure3 represents a part of courseware resource with SCORM standard. What to present when a device accesses this course at first time? The well solution is that the content of first screen should depend on, at least, the screen size. Figure4 Adaptation with Resource Tree That is, with different screen size, the presented content should match with that size. The above problem is called content adaptation. How to implement content adaptation? This can be demonstrated in figure4 [7]. From figure4, we can see that with resource tree representation, the proper content size can be selected according to the screen size. But the candidate resource sets maybe more than one, which node set should be selected? At the worst case, the selected content may be has none meaning. So, to produce the proper content, other constraints should be


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confined, that is, the selected resource node set should be a meaningful block in logical. How to guarantee that? It should refer to educational semantic web. Educational Semantic Web: Educational Semantic Web (ESW) [9-12] is a new education technology enabled by semantic web technology [8]. In ESW, the education content and activity should be annotated with well-defined meaning which is understandable to human and machine. The well-defined meaning is represented with a special data model, called education ontology [14-16]. In education ontology, the concepts and their relations in course content and education activity are defined in a formal method called description logics. With these logical defined educational resources, intelligent education application can be developed by reasoning with logic programming and rules. The educational semantic web diagram is depicted as figure5 and a draft educational ontology is as figure6 (proposed by author’s group).

3. Intelligent Education With the research and development of ICT, new technologies will come out, and where should education technology go? From the above statement, we can see that e-learning technologies are developed for education practice, that is, only for educate and educatee. It is natural to think that ICT technology should not be confined only for education practice. The problem is: how does ICT technology provide support to education research (theory and method)? Is it possible? And a more challenge problem is: are there any other technologies or theory, except for ICT, that can be used to enhance education? Of course, these are human related disciplines, such as brain science, cognition science, knowledge science, logics, philosophy, and etc. The evolution of education technology with human related disciplines and ICT technology is depicted as figure7. In figure7, D-Com stands for data communication, D-Process stands for data process and K-reasoning stands for knowledge reasoning. From figure7, we see that the

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development trend of information technology is knowledge representation and reasoning (KRR). With KRR, intelligent applications can be developed. On the other hand, from the viewpoint of application, domain users can also model and develop applications without relying on software engineers. In other words, education domain users include educate, educate, researcher, and other ones who are engaged in education. So, as the development of technology, not only education practice, but also education theory can also be enhanced by ICT in an intelligent way. At the meantime, evolution roadmap of ICT is very like the cognition process for human being to learn knowledge, depicted as figure8. As figure8 indicates, the cognitive process is as follows: collect large amount of data; select useful data (information) from large data; extract knowledge about a concept from synthesizing multiple aspects of information and setting up connection with other concepts; the knowledge is kept in long-term store. At the meantime, data selection and information synthesis are controlled by thinking and reasoning with knowledge originated from motivation. On the other hand, with the information model’s evolution from data model, information model to knowledge model, the course model changes from digital course, multimedia course to knowledge representation course. All of these reflect that the development process of information technology is almost consistent with the cognition process. Figure7 also indicates that, besides ICT, human related disciplines are also an important force to improve education. From viewpoint of history of thousands of years, human discipline plays an important role to education. Especially, in recent years, with the development of human related disciplines, we understand human mind more and more deeply, such as how to understand, how to learn, how to think, and etc. As a result, the human related achievements provide human a better way to understand themselves, to guide human to learn and to educate.

Figure9 Intelligent Education Architecture

In a word, with the rapid development ICT technology and human related disciplines, education will be enhanced in an intelligent way! Figure9 demonstrates more details of this intelligent way which called Intelligent Education Architecture (IEA) [17]. Figure9 indicates that IEA include five parts, they are Human Related Disciplines (HRD), Intelligent ICT (IICT, include Information Science, AI, Knowledge Engineering), Education Theory Research (ETR, includes Education Method), Education Practice and Intelligent Education Domain Engineering. Relations among these five parts are as follows:


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(1) Education Theory Research is supported by Human Related Disciplines, Intelligent ICT and Intelligent Education Domain Engineering. (2) Intelligent Education Domain Engineering is developed with the guide of Education Theory Research and Intelligent ICT. (3) Optimized Education Practice can make teaching process more efficient and the Human Related Disciplines can progress more rapidly. (4) All education activities are supported by Intelligent Education Domain Engineering. In the proposed Intelligent Education, all parts of education will form an ecosystem where every part in education will be enhanced by others. More details refer to [17].

4. Some Researches in Knowledge Science and Intelligent Education Laboratory at Peking University Knowledge Science and Intelligent Education Laboratory at Peking University (KSIE) aim to build an education ecosystem with Intelligent Education Architecture [17]. Such a system will provide scientific education research method and scientific education practice˄includes teaching and learning˅. With this system, the efficiency of education theory and method will be predicted by intelligent simulation or formal verification, not like the nowadays experiment with real person. The goal is good, but implementing such a system depends on a great progress of Human Related Disciplines and Intelligent ICT where the learning principle is understood and the proper model of learning nature is constructed. Indeed, it needs a lot of work and a long time, but we are doing something to proceed to the goal. Some researches on this work in recent years are presented as below.

4.1 Education Software Automation based on Education Ontology This means that, with educational ontology, we can develop an infrastructure platform to generate application system in terms of user requirements. Such an infrastructure is proposed as figure10, more details refer to [17].

4.2 Educational Semantic Web Service Composition Model Semantic Web Service (SWS) technology [13, 22, 23, 24] provides a good choice to realize education software automation. With SWS, every education service is annotated with well-defined meaning which can be understood by

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human and machine [18, 21]. With large amount of educational semantic web services available on www, we can compose some of them according to requirements and generate a realistic system. The above process is called SWS composition [18, 21, 25]. Figure11 is a proposed SWS composition model for education by our group which is called OntoComposer [21]. Figure12 is an implementation and its operation interface of OntoComposer, the labeled numbers in figure12 represent the process to compose a new service with available web services, details refers to [26].

4.3 Ontology-based Search Engine Search engine is an efficient tool to retrieve information from large amount of web resources. With semantic web, web resources are annotated with well-defined meaning understandable to human and machine. From figure5, with educational semantic web, educational ontology provides formal semantic description for educational resources which can be understood by machine. In this way, search engine can understand web resource and select information more efficiently (depicted as figure13), more details refer to [20].

4.4 Ontology-based M-learning Platform One problem in m-learning is context aware application. That is, the presentation of courseware content should be adaptive in terms of the user context. The user context includes device feature and user preference. When user accesses education server, the server selects proper content and style, and returns it to client. In this way, the courseware could be accessed by any device. Figure14 is a proposed design for m-learning with web service. The context-aware adaptation works as following: (1) at sever end, the e-learning


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system is composed of simple services which include operation and resource individually. (2) When server receives access request, it groups some simple services into one page in terms of user context automatically and returns it to client. The adaptive presentations are depicted as figure15. More details refer to [6].

5. Conclusions The advance of ICT would enhance education continuously. E-learning evolution with ICT indicates that M-learning provides a convenient interaction, educational semantic web provides a machine understandable description of education resource, and ontology model provides a knowledge representation model of course content, which will improve cognitive process for learn content in nature. Besides ICT, human related disciplines have become an important force to enhance education recently. A kind of education technology to combine ICT and human related disciplines into education and form a scientific ecosystem education system will be the next generation technology, which is called Intelligent Education.

References [1] Cui Guangzuo, etc. Mobile Education: A New Direction of Education Technology. Ceta Annual Conference of China. 2001.12. [2] Cui Guangzuo, etc. Mobile Virtual Campus: Design and Implementation. China Distance Education. 2002.12. [3] Cui Guangzuo, etc. Study and Implementation of SMS-based Mobile Education Architecture. GCCCE2002, Beijing. [4] Cui Guangzuo, etc. MVClass: Mobile Virtual Class for Open and Distance Education.International Conference of Distance Education " 2nd AEARU Workshop on Network Education”, Taiwan.2003.12 [5] Dong Sheqin, Yue Weining, Cui Guangzuo And Wang Guoping. A Educational Information Platform based on Mobile Device. Computer Application of Chine. 2004.V24.11, Pp142-145. [6] Cui Guangzuo, etc. A Study of Concept and Key Technologies of Mobile Education. China Distance Education. 2005.9. [7] Cui Guangzuo. WebUnify: An Ontology-based Web Site Organization and Publication Platform for Device Adaptation. SNPD2004 International Conference, Beijing. 2004.7. [8] Tim Berners-Lee, James Hendler and Ora Lassila. The Semantic Web. Scientific American 2001.5. [9] Lora Aroyo, Darina Dicheva. The New Challenges for E-learning: The Educational Semantic Web. IEEE Journal of Educational Technology & Society,2004.10.Pages 59-69. [10] Emanuela Moreale, Maria Vargas-Vera. Semantic Services in e-Learning: an Argumentation Case Study. IEEE Journal of Educational Technology & Society,2004.10.Pages 112-128. [11] Terry Anderson and Denise Whitelock The Educational Semantic Web: Visioning and Practicing the Future of Education. Journal of Interactive Media in Education, UK, 2004 (1). [12] Kendall Clark, Bijan Parsia and Jim Hendler Will the Semantic Web Change Education? Journal of Interactive Media in Education, UK, 2004 (1). [13] Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng. Semantic Web Services. IEEE Intelligent Systems, MARCH/APRIL 2001. Pp46-53. [14] Cui Guangzuo. OntoEdu: Ontology based Education Grid System for e-learning. Fifth International Agricultural Ontology Service (AOS) Workshop, Invited report.2004.5. [15] Cui Guangzuo, etc. OntoEdu: Ontology based Education Grid System for e-learning. GCCCE2004,HongKong

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[16] Cui Guangzuo, etc.OntoEdu: A Case Study of Ontology-based Education Grid System for e-Learning . The Official Journal of Global Chinese Society FOR Computers in Education,2004,pp59-72 [17] Cui Guangzuo, etc. Architecture Study of Intelligent Education. Keynote Speaker. GCCCE2006, Beijing. [18] Yang Li-na. A Kind of Task Composition Method Based on Education Ontology. Master Thesis, Peking University, 2004.7. [19] Cui Guangzuo. Application and Challenge of education under Ubiquitous Environment. Ubiquitous Forum of Information Industry Ministry in China. 204.11. [20] Xiao Hong. Study on the Semantic of SCORM based on Knowledge Organization & Retrieval. Master Thesis, Peking University, 2006.7. [21] Liu Yang. A Kind of Ontology based Hybrid Semantic Web Service Composition: Mechinsm and Implementation. Master Thesis, Peking University. 2006.7. [22] Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng,Semantic Web Services. IEEE Intelligent Systems.MARCH/APRIL 2001.pp46-54. [23] Sheila A. McIlraith,David L. Martin. Bringing Semantics to Web Services. IEEE Intelligent System,JANUARY/FEBRUARY 2003.pp90-94. [24] Massimo Paolucci.Autonomous Semantic Web Services. IEEE Intelligent Computing. SEPTEMBER • OCTOBER 2003. pp34-42. [25] Brahim Medjahed, Athman Bouguettaya, A Multilevel Composability Model for Semantic Web Services. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 7, JULY 2005.pp954-969. [26] Cui Guangzuo. OntoComposer: Development Model and Implementation of Semantic Web Service based on Domain Ontology. To be published.


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Designed Collaboration as a Scaffold for Schematic Knowledge Integration Naomi Miyake School of Information System and Technology, Chukyo University, Toyota, JAPAN [email protected] Abstract: Rapid change in modern society requires higher levels of learning such as the acquisition of adaptive or “schematic” knowledge. Rather than the efficiency of simply applying what one has learned, the schematic knowledge acquisition emphasizes portability, sustainability, and dependability of learning outcomes. Schematic knowledge is expected to allow the learners to apply them to solve the wider scope of similar problems, as well as to identify new problems and create new solutions. We have been developing and testing college level learning environments to enhance the acquisition of such schematic knowledge in the domain of cognitive science, by heavily relying on understandings of how and why collaborative reflection benefits learning. In the two-year curricular we have developed, the students are first introduced to the notion of schematic learning by experiencing their own formation of schemata, and then are guided to reflect upon the process, through carefully designed collaborative activities. They will also be encouraged to integrate their experiences to technical literature through collaborative discussion in a dynamically arranged jigsaw variations. I will report on the theoretical bases of our practice, concrete learning activities, technological supports, and some results of the evaluative analyses of the learning processes and the outcomes. Keywords: College level collaborative learning, schema formation, knowledge integration

Introduction: Emerging new learning goals Studies of how people could effectively learn have a long tradition because human beings have constantly been required to re-structure old experiences to accommodate new situations. However, in the current age, where rapid changes are the norm, the degree to which this requirement occurs has increased more than ever. Students, for example, are required not only to acquire routine knowledge but also to apply what they learn to new situations, outside of school and in a distant future. The outcomes are expected to be ‘portable’ so that the learners can bring them to apply problems outside of classrooms; the learning outcomes are also expected to be ‘sustainable’ so that the learners can build on them to keep strengthening their intellectual skills; the outcomes also have to be ‘dependable’ in the sense they are usable whenever and wherever need arises. To meet such requirements, new learning studies with new research methods have been carried out, which differ from those used in laboratory-based, conventional learning studies [1][2] , starting to yield some substantial reports [3][4].

1. Theories behind collaborative learning One of the major changes introduced by learning sciences is a change in the perspective on learning: learning sciences view learning as a collaborative act, where people mutually

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N. Miyake / Designed Collaboration as a Scaffold for Schematic Knowledge Integration

enhance performance by influencing each other. There are two reasons for this change. One is that through many studies, collaboration has been proven to be effective in enhancing learning outcomes, in many disciplines and in different age groups [5][6][7][8][9][10][11]. The other reason is that collaborative situations help promote in-depth, process-oriented research in learning. The current rapid development of information recording technologies is playing a large role in research, by increasing the likelihood of learning studies achieving their potential of bringing significant change to actual education and progressing our understanding of the learning processes themselves. It has been widely acknowledged that a collaborative situation promotes motivation for comprehension [12]. Collaboration has also been a topic of research to see whether “Two heads are better than one,” and if so, how. It has also been repeatedly demonstrated that collaborative learning leads to adaptive acquisition of knowledge [13]. In order to understand the underlying mechanism for such results, some research has focused on the dynamic interaction between individual courses of understanding and their collaboration, while keeping the basic unit of analyses on the pair, by using tasks like jointly comprehending how a sawing machine saws [8] or what would be the area of a square sheet of paper to get the three-forth of its two-thirds, or the two-thirds of its three-forth [10]. On the sewing machine problem, even knowledgeable pairs could spend three to four hours easily to understand the hidden mechanism of how the internal bobbin works, through which each participant eventually constructed his or her own understanding, solidly different from the other partners’ comprehension. The detailed protocol analyses of such interaction also demonstrated that when caught in the “cal-de-sack” it was often the person who was monitoring the situation, not the person who was taking the initiative of the problem solving at the moment, who came up with an idea to radically change the situation to “save” them. These findings suggest that even during highly collaborative comprehension activities, social sharing of the situation does not impede each participant from pursuing individualistic knowledge construction. Rather, the interactive process supports each to realize different perspectives to check and modify their own understandings by making explicit the different perspectives, which are not within their individual repertoire [8]. On the square sheet area problems, paired participants, not the solos, could take alternating perspective-shifts from the local, task oriented view to more abstract perspective, and this alternation of the perspectives helped each participant to explicitly talk about their solutions in more abstracted forms toward the latter part of the interaction, thus enhancing the formation of abstracted solution [10]. This mechanism offers explanation why there could be different learning outcomes in each of the paired learners. It is because the abstraction level for each participant depends on the degree of the integration of the shared task-doing and the monitoring, during and at the end of the interaction. This also explains why a more knowledgeable participant, or the person who preceded the other in his/her understanding, could still benefit from the interaction. This model suggests that for the design of collaborative learning environments it is important to encourage role exchange and to secure ample chances for each individual learner to reflect upon the shared resources. 2. Designed Collaborative Reflection for the schematic knowledge integration In the project to test the theory described above at real classroom situations we have been developing a collaborative undergraduate curriculum to teach cognitive science for nearly 7 years [14][15][16][17][18][19][20][21]. The learning objective is broad because we believe knowledge about cognitive science has pragmatic value for most of what we do in our everyday lives. Cognitive science explains how human beings solve problems, make judgments, memorize events and schematize them, and create new ideas. Knowing how


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people engage in such cognitive processes, with the associated strengths and weaknesses, helps in the development of a reflective, metacognitive viewpoint, which can be utilized in everyday practices of cognition. 2.1. Overall description of our curriculum and classroom activities The curriculum described here is for undergraduate students and covers two semesters per year, taking four years to complete. In the first year, hands-on experiences of simple cognitive tasks are completed and analyzed by the students, first individually and then collectively, in the class. They are then guided to reflect of their experiences to take metacognitive perspectives, to understand how people solve problems. The curriculum toward the end of the first year encourages them to tie their reflection to what is written in the textbooks, to integrate their personal knowledge to the theories of human problem solving. During their second year, these experience-based techniques are gradually meshed into more active reading of technical materials, to help students gain a deeper level of comprehension as well as to grasp the breadth of research in cognitive science. In the third to fourth years they are encouraged to engage in more inquiry-oriented, project-based learning, learning to do their research toward graduation theses. 2.2. Variations of the jigsaw method Throughout the curriculum, we use the various forms of the jigsaw method to enhance collaboration. A social psychologist, E. Aronson, devised this method in order to facilitate cultural merging in classrooms [22]. In his original design, a text may be divided into six parts each of which is read by a group of members, each of whom is responsible for different parts(this is called an “expert” group activity, because each is expected to be an expert of this assigned part). Then one member from each of those six groups gets together, for a group of six, to answer questions covering all six parts,requiring their equal participation to succeed(this is called the “jigsaw” group activity). The method produces a natural setting to explain what one understands to others, often motivating students to further examine their assigned parts. In our curriculum, students are introduced to a simple jigsaw having only two to three parts, gradually guided on to a more structured and dynamic jigsaw that covers twenty to thirty texts explaining different research findings. The jigsaw method is highly flexible, modifiable to facilitate many different types of collaboration. Suppose a complex and highly structured course work needs to be delivered among a class. One form we have developed in our curricula is a “matrix” jigsaw, wherein college students move in and out of hierarchically structured “expert” groups and “jigsaw” groups. Suppose you could prepare the learning materials in an n by m matrix, of perspectives A and B. Assign a student, say S1, to a cell Ai by Bj in the matrix. By doing this, S1 automatically becomes responsible of owing a particular viewpoint Ai on dimension A, as well as of another viewpoint Bj on dimension B. The course work can then be structured to require her to work in one of the expert groups on perspective A at one time, while at other times she is required to be an expert on B. A more dynamically expansive Jigsaw has been devised in our course to enhance collaborative understanding of 20 to 30 learning materials, each representing classic research in three different domains of e-learning research. We expect the students to collaboratively read, explain, exchange, and discuss the materials to integrate them, to the level where each participating student would come out of the course with the ability of verbally explain what the entire field is, with many evidential pieces of knowledge they could cite by identifying relevant research[18][19][20].

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This dynamic jigsaw models common activities of real world researchers. Professional researchers take different responsibilities to study a common theme and exchange their interpretations of existing work and new findings from their own community. They most often do this repeatedly, explaining their thoughts to different audiences so that they can examine them from different perspectives for different integration possibilities. This series of activities help them achieve a coherent comprehension, which could dynamically evolve over time. The dynamic jigsaw models this process and requires each participating student to first become an expert of his/her core material(s), then repeatedly explain the core to different audiences. While they receive information from others, they are also required to expand their explanations to include the new, and relate them to their own older knowledge structure. They are then to integrate these explanations to form a new explanation, to improve understanding. The dynamic jigsaw class replicates this more systematically. In the first phase, a student becomes an expert of one piece of the learning materials. S/he exchanges its explanation with one other student, to form an explanation of two pieces of the materials. Then, they are ready to exchange their two-piece explanations with a neighboring pair, who also is ready to give her two-piece worth. At the end of this phase, each student is expected to know four pieces, two of his/her own plus two more explained by another student. They can now exchange four to four, to cover eight research pieces, enough to cover a sub-domain. Toward the end of the term, such domain experts share by exchanging explanations of the domains, or some eight piece worth of information. 2.3. A concept mapping tool to support collaborative reflection Note-sharing software in the form of a concept-mapping tool could aid the students to express their ideas, integrate them among themselves as well as with others. This kind of technological support works well with the course structure, to promote the students knowledge integration [14]. Students are encouraged to externalize their initial ideas at early stages of their understandings of the learning materials, because it becomes easier to “talk about” or “collaboratively reflect on” externalized forms than to do so on their “ideas”


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in the head. It is also easy to expand he initial concept maps by adding new cards, by connecting once completed map with other maps. The concept mapping tool we devised is called ReCoNote (Reflective Collaboration Note), some of its displays during the course is shown in Fig. 1. We have also devised an evaluation tool to follow their development. The right column on Fig. 1 shows one student’s three concept maps drawn at different stages of a course in year 2004, put together to display the entire map so that both the students and the teaching team can 3. Major findings Some assessment analyses of their performance accumulated over the last 6 years reveal 1) fair amount of retention of the learning materials 4 to 6 months after the end of the course, 2) explicit knowledge integration surrounding each students’ personal needs, and 3) some conscious learning of learning skills such as asking specific, content-driven questions [15][16][18][19][20]. One index we have devised is the degree of integration, or structural coherence, of the final concept maps the students created after the course, before the deadline of the term papers. The maps were categorized into four groups according to their structural coherence and given scores ranging from 1 to 4, with 4 indicating the highest coherence. The term papers are also evaluated in terms of the conciseness and the correctness of the descriptions of the research findings they reported. We also examine the relationship between the implications they drew from what they had learned and their descriptions of possible usage in everyday life. We called this measure “extendibility.” We found that the integrity measures of the concept maps were generally high. Forty-two percent of the final concept maps were categorized as achieving high integrity, close to the performance of novice graduate students. In classes in recent years, all the learning materials were covered in 80 to 85% of the term papers, out of which some 50 to 60% being “concise descriptions” with the necessary components in an expected order. This indicated that the majority of the students had learned both the basic contents of the learning materials as well as how to give concise summaries. The extendibility measure, or the degree to which the students could connect what they learned to their daily experiences, was found to be positively correlated with the quality of concept maps, suggesting that the learning activity of externalizing their integration efforts had a positive effect of fostering their thinking toward applications of what they had learned [18]. The protocol analysis of the students’ conversations during the class revealed that the students’ explanations became more concise in terms of both the amount of time used and the content covered. To take three students as a representative example: their first explanation of one research paper took 400 to 500 utterances on average, which decreased to 20 to 30 utterances toward the end of the term, without losing any necessary components. Their first explanation attempts were closer in wording to the texts of the learning materials than their later explanations, yet the first explanations involved more incorrect, vague, or confused statements. Such confusions about the meanings of the learning materials tended to be resolved during the discussions that occurred while the students were integrating their materials with those of other students’ [19][20]. We also found some cases where the students, in their junior years, talked about their experiences of the dynamic jigsaw as a source of acquiring various research skills (e.g., taking notes, writing reports, and questioning) [15]. These are encouraging signs for further exploration of the conditions that make collaborative learning situations more productive and beneficial to those involved.

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Acknowledgments This series of research has been supported by SORST/JSP 2005-2007 and JSPS Grant-in-Aid 15200020 (2004-2006). References [1] Brown, A. (1992) Design experiments: Theoretical and methodological challenges in creating complex interventions. Journal of the Learning Sciences, 2(x), 141-178. [2] Collins, A. (1992) Toward a design science of education. E. Scanlon and T. O’Shea (eds.), New directions in educational technology, Springer-Verlag, Berlin. [3] Bransford, J., D., Brown, A. L., and Cocking, R. R. (1999). How people learn: Brain, mind, experience, and school (Expanded edition). National Academy Press, Washington, D.C.. [4] Bransford, J. D., & Donovan, S. (2005). How students learn. National Academy Press, Washington, D.C.. [5] Chi, M.T.H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R.G. (2001). Learning from tutoring. Cognitive Science, 25:471-533. [6] Linn, M. C., & Hsi, S. (2000). Computers, teachers, peers: Science learning partners. Erlbaum. [7] Linn, M. C., Davis, B. A., & Bell, P. (Eds.) (2004). Internet environments for science education. Erlbaum, [8] Miyake, N. 1986. Constructive interaction and the iterative processes of understanding. Cognitive Science, 10(2), 151-177. [9] Scardamalia, M., & Bereiter, C., (1991). Higher-levels of agency for children in knowledge building: A challenge for the design of new knowledge media. The Journal of the Learning Sciences, 1(1), 37-68. [10] Shirouzu, H., Miyake, N., & Masukawa, H. (2002). Cognitively active externalization for situated reflection. Cognitive Science, 26(4), 469-501. [11] Roschelle, J. 1992. Learning by collaborating: convergent conceptual change. The Journal of the Learning Sciences, 2, 235-276. [12] Hatano, G., and Inagaki, K. (1991) Sharing cognition through collective comprehension activity. L. Resnick, J. Levin, and S. D. Teasley (eds.), Perspectives on socially shared cognition, American Psychological Association, Washington, D.C., 331-348. [13] Hatano, G., & Inagaki, K. (1986). Two courses of expertise. In H. Stevenson, H. Azuma & K. Hakuta (Eds.), Child development and education in Japan. Freeman & Co., 263-272. [14] Miyake, N., (2001), “Collaboration, technology and the science of learning: Teaching cognitive science to undergraduates,” The Annual Report of Educational Psychology in Japan, 40, 218-228. [15] Miyake, N. (2005a) Multifaceted Outcome of Collaborative Learning: Call for Divergent Evaluation. Paper presented at the meeting of the 13th International Conference on Computers in Education (ICCE2005), Singapore. [16] Miyake, N. (2005b) How can Asian educational psychologists contribute to the advancement of learning sciences?. Invited talk at the meeting of the Korean Society of Educational Psychology 2005 International Conference, Seoul, Korea. [17] Miyake, N., & Shirouzu, H. (2004) Learning from lectures for comprehension. Paper presented at the meeting of International Conference of the Learning Sciences 2004, Los Angeles, CA. [18] Miyake, N. and Shirouzu, H. (2006) A collaborative approach to teach cognitive science to undergraduates: The learning sciences as a means to study and enhance college student learning. Psychologia, 49, 2, 101-113. [19] Miyake, N., Shirouzu, H., & Chukyo Learning Science Group. (2005) Interactive learning cycles to foster knowledge integration. Paper presented at the meeting of the Germany-Japan Joint Workshop 2005, Tokyo. [20] Miyake, N., Shirouzu, H., and Chukyo Learning Science Group. (2005) The dynamic jigsaw: repeated explanation support for collaborative learning of cognitive science. Paper presented at the meeting of the 27th annual meeting of the Cognitive Science Society, Stresa, Italy. [21] Shirouzu, H, & Miyake, N. (2005) “CSCL for lecture comprehension and question asking: Commentable Movie Sheet on BBS”, Computer Supported Collaborative Learning 2005, Taipei, Taiwan. [22] Aronson, E., and Patnoe, S, (1997). The jigsaw classroom: Building cooperation in the classroom. Longman, New York.


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Technology Affordances for Intersubjective Meaning-Making Daniel SUTHERS Department of Information and Computer Sciences, University of Hawai`i, USA [email protected]

Abstract: The broad field of “computers in education” includes a diversity of approaches to using computers for learning. Each approach is based on an epistemology: a theory of how knowledge is gained. In this presentation, I will characterize the uses of technology and their corresponding epistemologies. I will single out intersubjective epistemologies as timely for research and practice, and call for development of technologies that offer social affordances and resources for meaning-making. The study of intersubjective meaning-making requires interactional analyses, but in new forms that transcend some of the assumptions and limitations of microanalysis and that can be coupled with other methodologies. The presentation illustrates these ideas with my research program on representational affordances for collaborative learning. Keywords: epistemologies of learning, representational guidance, technology affordances, interactional construction of meaning, research agenda

1

Introduction

The broad field of "computers in education" includes a diversity of approaches to using computers for learning. For example, we can find technology used as a publication medium, to present information or problems; as task-oriented tools for aiding performance, keeping track of information and organizing the learner's activities; as conceptual tools for relating features of problem instances to useful abstractions or expressing and testing the learner’s own ideas; as a communication medium through and with which learners engage with each other in peer tutoring, argumentation, or collaboration in making sense of a situation; and as a proxy for the teacher, selecting the next problem or activity, selecting learning partners, giving hints or correcting errors during performance, and confirming or correcting learners’ solutions. Each of these approaches is based on assumptions concerning learning and how technology can support it. These assumptions should be identified and used to guide design in a dialogue between theory and practice [26]. New forms of technology-mediated learning are possible if we re-examine our beliefs about learning and the roles of media in learning. My keynote presentation will provide an overview of my own quest. This extended abstract outlines the ideas to be covered and provides a bibliography for those who wish to pursue some of these ideas further. I first summarize relevant theories of how knowledge is gained, called epistemologies. I then single out intersubjective epistemologies as most timely for research and practice, and suggest lines of investigation into social affordances through which technology media can serve intersubjective meaning-making at various scales. The reader is referred to [30] for a more developed account of the material of this presentation, focused on the field of computer-supported collaborative learning (CSCL).

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D. Suthers / Technology Affordances for Intersubjective Meaning-Making

Epistemologies of Learning

When the actual practices of our field are examined, we find that a great deal of work is based on a knowledge-communication epistemology. Knowledge communication is “the ability to cause and/or support the acquisition of one’s knowledge by someone else, via a restricted set of communication operations” [39]. Research conducted under this epistemology examines how to more effectively generate or facilitate communications that “cause and/or support” the desired acquisition of knowledge. The best work in this paradigm (e.g., [2]) eschews a simplistic view of learning as the transfer of information from outside to inside the learner's head, and treats knowledge communication in the context of constructivist and interactional stances, considered below. A constructivist epistemology [21, 37] emphasizes the agency of the individual learner in the learning process. Learning can only happen through the learner’s efforts at making sense of the world, although a mentor might arrange for the learner to have challenging experiences in order to accelerate the change process. Most researchers do not take constructivism to its solipsistic extreme, but instead view social interaction as helpful and even essential. Interactional epistemologies are diverse, and include accounts that emphasize both individual and social agency. With individual agency, the individual is the unit and agent of learning yet this learning can be enhanced through social interaction. Examples include cognitive dissonance theory [8] and socio-cognitive conflict theory [6]. Contribution theory [3] is interactional in its account of the construction of “common ground,” but is yet based on an individual epistemology as it does not explain how knowledge that did not predate the communication is jointly constructed within the communication process. At the boundary of individual and social agency, we find Vygotsky’s [38] oft-cited observation that developmental learning through social interaction can be understood as the internalization of interpersonal processes as intrapersonal processes. Intersubjective epistemologies are interactional epistemologies with social agency: they locate meaning-making and even learning at the group level. In a distributed or group cognition account, the group and its cultural/technological artifacts collectively constitute the proper unit of analysis [11, 28]. Knowledge and meaning can be understood as jointly created through interaction: learning consists of this interaction [16]. An intersubjective epistemology is distinguished from grounding in that interpretations emerge within the interaction, and so are shared from the outset. Learning is also conceived of as a community level phenomenon. A participatory epistemology sees learning as a process of increasing participation in the practices of a community [19], constructing personal and collective identity [40]. Another community level epistemology is knowledge building [25], the enterprise in which a community intentionally expands its cultural capital by reflecting on limits of understanding and choosing actions that address these limitations. 3

Intersubjective Meaning-Making

In my own analysis of CSCL [30], I single out intersubjective epistemologies as those that we most need to understand, at both the interpersonal and community levels. Given the pervasive social nature of learning, I maintain that this emphasis is of importance for other research communities such as the ICCE community. Intersubjective epistemologies lead to challenging unanswered questions. How is it possible for learning, usually conceived of as a cognitive function, to be distributed across people and artifacts [24]? What is the relationship of the change process we call “individual learning” to that individual’s participation in socially accomplished learning? The study of intersubjective learning is


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needed because we already have a substantial body of work on individual learning and on how the cognitive processes of participants are influenced by social interaction, while intersubjective learning is currently not prominent as a topic of study in our field (notable exceptions include [1, 16, 23, 27]). An intersubjective perspective will help designers understand how technologies can function as mediating resources in learning. In [30] I argue that “learning” is a judgment we make about the consequences of an activity, and to understand this accomplishment we must necessarily study the practices (the activity itself) of intersubjective meaning-making: how people in groups make sense of situations and of each other. 4

Implications for a Research Agenda on Social Affordances of Technology

In [30] I identify two distinct ways in which technology is applied to support collaborative learning—as a communication medium and as constraint (see also [10].) Both paradigms are limiting from an intersubjective meaning-making perspective, but both can contribute to a synthesis. Richer communication media are needed, particularly with respect to supporting the indexical nature of human communication [20]. Guidance for a learning agenda is needed for both discipline-specific practices and learning trajectories and for processes of intersubjective meaning-making, but without limiting creativity by excessively rigid scripting of action. In order to achieve advancements in these forms of support, we need to better understand the ways in which practices of meaning-making in the context of joint activity are mediated through designed artifacts [15] and apply this understanding to design fundamentally social technologies that are informed by the affordances and limitations of those technologies for mediating intersubjective meaning-making. The remainder of this paper identifies some unique social affordances of computational technology for intersubjective meaning-making, suggesting lines of investigation in research and design. Negotiation Potentials. Any medium offers certain potentials for action. Participants may feel an obligation to obtain agreement on modifications to shared workspaces. The potentials for action offered by the medium can therefore guide interactions towards ideas associated with the afforded actions [34]. If we would like users of our technology medium to focus on particular aspects of a problem, how can the medium be designed to prompt for actions that require negotiation of these aspects? Referential Resource. Jointly constructed representations become imbued with meanings for the participants by virtue of having been produced through a process of negotiation. These representational constituents then become a rich referential resource for conversation [33], facilitating elaboration on previous conceptions. Rather than being vehicles for communicating expert knowledge, representations become objects about which learners engage in sense-making conversations [23] and can be designed to lead to productive conversation. How can we make salient that which learners would productively interpret, elaborate on and relate to new information or ideas? Integration. The computational medium can leave a persistent record of activity [5]. How can traces of interaction and collaboration be designed to foster appropriate awareness of prior conceptions and the means to reference these in subsequent interactions so that they may be integrated with new information and ideas? (Im)mutable Mobiles. The mobility of digital inscriptions provides opportunities for recruitment of partners in the sense-making process [18] and supports continued engagement in that process. How can we exploit this property of technology for its potential to make new social alignments and their interactions possible?

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Reflector of Subjectivity. Computational media can be designed to foster group awareness [17], visualize conflict or agreement between members [12], or project representations of self into a social representation [14]. In what ways can we design technology to mediate intersubjectivity by reflecting activity, subjectivity, and identity? Trajectories of Participation. Technologies offer social affordances for patterns of participation over larger spans of time and collections of actors [22]. How can we encourage productive entanglement of multiple individual trajectories of participation by selectively making their contributions salient and hence available for subsequent interpretation by others? My colleagues and I have been engaged in work on social affordances of technology since we first realized that the visualizations and coach of Belvedere had significance primarily (if at all) in how they affected peer interaction [36]. This began a line of work on representational guidance [31] that investigated negotiation potentials [34], referential resources, and integration [33]. More recently, we have brought this work to an asynchronous paradigm [35], and have begun a new line of work examining the fundamental practices by which people appropriate the affordances of certain media for written communication [7]. 5

Analysis of Intersubjective Meaning-Making

Although some of our prior work has been in a quantitative experimental paradigm, we have found that the study of intersubjective meaning-making requires coordinated use of qualitative interactional analyses [4]. Quantitative methods aggregate over many sessions, obscuring the actual procedures by which participants accomplish learning through the affordances of online media [16]. Methods for studying the interactional construction of meaning are available [13, 9], but have largely been developed for brief episodes of face-to-face data, and do not scale well to online learning where media resources, time scale, and synchronicity all differ. This analytic tradeoff between scalability and fidelity must be resolved in order to inform the design of improved online learning environments and participation structures that engage participants more deeply in intersubjective meaning-making during collaborative inquiry. My research group has been working on this problem for several years now [29]. As our current progress is reported elsewhere in this proceeding [32], it will not be detailed here. The short-term objective of the work reported in [32] is to scale up sequential and interactional analysis to distributed and asynchronous interactions while remaining grounded in participants' use of media. The long-term objective of the entire enterprise discussed in this extended abstract is to obtain a deep understanding of how learning is accomplished interactionally in technology-mediated setting, and then to offer learners environments that provide the resources and guidance they need for engaged learning. Acknowledgments This work was supported by the National Science Foundation under award 0093505. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.

References [1]

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M. Baker, Computer-mediated argumentative interactions for the co-elaboration of scientific learning tasks., in Andriessen, Baker and Suthers, eds., Arguing to Learn: Confronting Cognitions in Computer-Supported Collaborative Learning Environments., Kluwer, Dordrecht, 2003, pp. 47-78. R. Bromme, R. Jucks and A. Runde, Barriers and biases in computer-mediated expert-layperson communication: An overview and insights into the field of medical advice, in R. Bromme, F. W. Hesse


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[29] D. D. Suthers, A qualitative analysis of collaborative knowledge construction through shared representations Research and Practice in Technology Enhanced Learning 1(2006), pp. 1-28. [30] D. D. Suthers, Technology affordances for intersubjective meaning-making: A research agenda for CSCL, International Journal of Computers Supported Collaborative Learning, 1 (2006), pp. (in press). [31] D. D. Suthers, Towards a systematic study of representational guidance for collaborative learning discourse, Journal of Universal Computer Science, 7 (2001). [32] D. D. Suthers, N. Dwyer, R. Medina and R. Vatrapu, Analysis of Meaning Making in Online Learning, International Conference for Computers in Education, APSCE, Bejing, 2006. [33] D. D. Suthers, L. Girardeau and C. Hundhausen, Deictic roles of external representations in face-to-face and online collaboration, in B. Wasson, S. Ludvigsen and U. Hoppe, eds., International Conference on Computer Support for Collaborative Learning 2003, Kluwer Academic Publishers, Dordrecht, 2003, pp. 173-182. [34] D. D. Suthers and C. Hundhausen, An experimental study of the effects of representational guidance on collaborative learning, Journal of the Learning Sciences, 12 (2003), pp. 183-219. [35] D. D. Suthers, R. Vatrapu, R. Medina, S. Joseph and N. Dwyer, Beyond threaded discussion: Representational guidance in asynchronous collaborative learning environments, Computers & Education (to appear). [36] D. D. Suthers and A. Weiner, Groupware for developing critical discussion skills, First International Conference on Computer Support for Cooperative Learning, Bloomington, IN, 1995. [37] E. Von Glasersfeld, A Constructivist Approach to Teaching. , in L. S. J. Gale, ed., Constructivism in Education, Lawrence Erlbaum Associates, Inc., New Jersey, 1995, pp. 3-16. [38] L. S. Vygotsky, Mind in society, Harvard University Press, Cambridge, MA, 1978. [39] E. Wenger, Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge, Morgan Kaufmann, Los Altos, 1987. [40] E. Wenger, Communities of Practice: Learning, Meaning and Identity, Cambridge University Press, Cambridge, 1998.

Modeling and Representation



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Design of an Environment for Developing Presentation Skills a

Kazuhisa Setaa and Mitsuru Ikedab Graduate School of Sciences, Osaka Prefecture University, Japan b School of Knowledge Science, JAIST, Japan [email protected], [email protected]

Abstract: Presentation plays an important role in transmitting one’s opinion and encouraging collaborative knowledge creation and decision-making processes. A key to producing persuasive presentation materials is to perform meta-cognitive activities well, but that is difficult for novice learners of presentation tasks. Furthermore, from the system-development viewpoint, it is difficult to develop a learning system with which learners can develop their presentation skills effectively because the cognitive activities in presentation task are typically not clarified. In this paper, we first present a cognitive model of the user who performs presentation task. We then overview two kinds of designed environments based on that model: one is for producing presentation materials that encourage learners to perform meta-cognitive activities; the other is a collaborative learning environment in presentation rehearsal, which encourages the transfer of context-dependent meta-cognitive knowledge among learning partners. Keywords: meta-cognition, scaffolding, presentation task, presentation skill, cognitive model

1. Introduction Presentation plays important roles in transmitting one’s opinions and encouraging collaborative knowledge creation and decision-making processes. In business scenarios, results of presentations often influence important administrative decision-making. It is not too much to say that the presentation skills that enable a person to organize their opinions and transmit them adequately are necessary skills for various applications in business, research, learning, and so on. In western countries, lessons related to rhetoric, speech, and presentation that enable a person to transmit one’s opinion adequately are provided [3][8]. Recently, such lessons have been reintroduced gradually into the university curriculum; numerous books on the subject are published even in Japan. These phenomena reflect the need for fostering well-developed presentation skills. Presentation processes, in general, consist of many activities such as understanding of audiences’ requirements for the presentation, setting presentation objectives and goals, and presentation design. Know-how of these activities can be partially externalized, i.e., general requirements satisfied by the result of each activity and what kind of (sub-)actions should be undertaken by the presenter are described explicitly in a document. The user can therefore acquire such fundamental knowledge. Nevertheless, the know-how strongly depends on one’s presentation scene: the way to build a consistent presentation logic, translate it to presentation sequences in an easily acceptable format for the audience, and build accurate audience models cannot be described explicitly in a document. Therefore, the user must develop such skills through experience.

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K. Seta and M. Ikeda / Design of an Environment for Developing Presentation Skills

Document media that are written in natural language are low-cost and high-performance media for knowledge transmission and spreading a viewpoint. Fundamental rules for presentations are actually written in the document media. However, the describable principle of know-how in a document is essentially generic. Consequently, (1) it is often difficult to specify and adapt to the individual specific scene. In contrast, the described case, which is dependent on know-how in a document, is fundamentally specific. For those reasons, (2) it is often difficult to generalize and transfer it to other individual-specific scenes. Furthermore, (3) it is difficult to determine the descriptions even when appropriate descriptions should be referred. Moreover, because one’s constructed presentation know-how is rooted in experiences and its externalization is strongly influenced by the author’s subjective intentions and viewpoint, (4) the described explicit know-how is often subjective. It is therefore necessary (a) to analyze and clarify the principle knowledge for making high-quality presentations and (b) to develop a learning environment based on them whereby a learner can develop presentation skills through the user’s presentation experiences. Our research goal is to clarify and systematize presentation skills as a presentation skill ontology and to build a support environment with which a novice learner of presentations can produce persuasive presentation materials and develop the required skills. In this paper, we describe a design of a CSCL environment for producing presentation materials as a first step towards achieving our goal. 2. Cognitive Model in Presentation Task Through our surveys of presentation know-how, we have inferred that performing meta-cognitive activities [1][2] such as self-monitoring, self-evaluation, and reflection, plays an important role to produce consistent and persuasive presentation materials and to develop presentation skills continuously. Figure 1 shows a cognitive model that incorporates detailed working processes of a learner that we adopt as a reference model for our system design. We capture presentation tasks as a form of problem-solving oriented learning a learning style in which a learner must not only accumulate sufficient understanding of a subject domain but also acquire the capacity for constructing effective problem-solving and learning processes according to a sophisticated strategy [5][6][7]. In the presentation task, processes of creating presentation materials correspond to problem-solving processes. Figures 1(i) and 1(iii) correspond to the planning process of the presentation plan and learning plan in Fig. 1, respectively, and 1(viii) and 1(x) respectively correspond to presentation rehearsal processes and learning processes in Fig. 1. Figures 1(v), 1(vi) and 1(xi) correspond to the monitoring process. Figure 1(xii) represents performing processes before the audience. We have presented a subject of presentation, say, ‘make a presentation for explaining the role of UML in software development processes’. Two virtual persons in the user, a presentation planner and learning process planner, respectively function in the roles of planning, monitoring and controlling problem-solving, and learning processes. Through the presentation task, a learner first defines a presentation goal with audience models and refines it to sub-goals that contribute to achieving goal G (Fig. 1(i), 1(vii)). They are refined to feasible presentation plans (Fig. 1(ii)); thereafter, the learner performs them to achieve presentation goals (Fig. 1(viii)).


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Fig. 1. Overview of Cognitive Model in Presentation Task The user generates an adequate learning goal (LG) to gain knowledge (Fig. 1(iii)) if the user recognizes a lack of knowledge in sub-goaling and rehearsing presentation plans. The user then might refine it to learning process plans (Fig. 1(iv)). In learning processes (Fig. 1(x)), that learner constructs knowledge (Fig. 1(iv)) that will be required for planning and performs the presentation process. Based on constructed knowledge, the user specifies presentation plans with audience models, creates presentation materials, and rehearses the presentation processes (Fig. 1(viii)) that alter the audience world (Pseudo Audience, Fig. 1(vii)). The user assesses gaps among goal states (GS), current goal states (CGS) of presentation process plans, and the current state (c-state) of the pseudo-audience (Fig. 1(v)) and those among learning goal states (LGS), current learning goal states (CLGS) of learning process plans, and the understanding state (Fig. 1(vi)). The user iterates these processes until the c-state of the real world satisfies the GS of problem solving. It is noteworthy that learners in presentation tasks must produce and execute not only plans for making presentations; they must also learn plans in the process. Furthermore, it is important for them to monitor audience model changes by rehearsing presentation processes and to monitor their own understanding states by performing learning processes and analyzing whether states of the pseudo audience satisfy the defined goal states (Figs. 1(v) and 1(vi)). The gap between current states and goal states dissolve the definition of new goals. Consequently, there exist two types of meta-cognition that a learner must perform in the presentation task. One is self-monitoring and (re-)construction of one’s own understanding states. Performing these meta-cognitive activities encourages building of a deep understanding of the target and clear logic of the presentation. It therefore plays an important role in building the foundation of a consistent and persuasive presentation. The other is self-monitoring and (re-)constructing the audience model by referring to one’s own understanding states (cognition of other people). Performing these

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meta-cognitive activities adequately encourages the setting of reasonable goals, scope, and a presentation grain size. It therefore plays an important role in the design of sophisticated presentations that are acceptable to the audiences. Presenters must perform these meta-cognitive activities accurately to create consistent and persuasive presentation materials; therefore, presentation tasks compel a learner to perform complicated tasks with heavy cognitive loads. A learner must manage and allocate attention resources adequately because of limited human attentional capacity. This explains why a novice learner tends to fall into confusion and learn ineffectively. Novice learners of a presentation task tend to allocate much attention to cognitive activities, especially to material-making processes or representations of materials in general. 3. System Design to Scaffold the Development of Presentation Skills Many authors describe the necessity and importance of performing these meta-cognitive activities in their books, but they do not include the term or concept of meta-cognition. Nevertheless, it is difficult for a novice presenter to learn when, how, and what kind of meta-cognitive activities should be performed in actual processes of presentation-material preparation because meta-cognitive activity is essentially tacit and latent; performing these meta-cognitive activities requires expertise of causal relations among the order of explanation, the presentation grain size, audience characteristics, and how the presentation affects the audience members’ thinking. It is therefore necessary to develop a learning environment in which a learner can improve working knowledge of performing meta-cognitive activities through practice while grasping fundamental principles of presentation through traditional lessons or books. 3.1 Design Principle Our system is designed to realize a learning environment (a) that encourages learners’ spontaneous meta-cognition required to make high-quality presentation materials and, in which (b) a learner can use presentation-material preparation experiences as valuable learning resources, and in which (c) a learner can include expertise of other experienced learners effectively. Based on the cognitive model presented in the previous section, we designed three kinds of learning environment: a learning environment for presentation-material preparation phase, presentation rehearsal phase and post-presentation (reflection) phase. Thus, the user can learn through all presentation processes. We describe the learning environment for the presentation-material preparation phase and the presentation rehearsal phase as follows. 3.2 Learning Environment for Material Preparation Phase Figure 2 shows a learning environment for the presentation-material preparation phase. The environment consists of five interface windows. Figure 2(iii) is a window of a presentation preparation computer program (Power Point, PPT; Microsoft Corp.), which is familiar to many people throughout the world; other windows are designed to function together with the PPT. For example, when the user selects a PPT slide, then domain relations, logical relations, presentation goals, and knowledge resources that are related to the slide are shown and highlighted in each window. Figure 2(i) is named the domain structure view, in which a learner can describe the relations of domain concepts that the user learned in relation to the presentation target. For


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example, the user can describe that “UML plays an important role in enhancing communication among humans who attend the software lifecycle” in the view of using graphical representations (nodes and arcs) and natural language. Furthermore, the user can color each concept (node) in white when the user sufficiently understands the concept. Figure 2(ii) is named a logical relation view in which the user can derive and arrange the logical relations of arguments hierarchically by referring to concept relations described in the domain structure view. For example, the user can describe logical relations that the reason of “UML plays an important role in the software lifecycle” are that “UML provides a common language that is needed throughout the entire software lifecycle” and “it encourages smooth communication among humans who attend the lifecycle” in the view. Figure 2(iv) is named the intention view, in which the user can design a rational presentation structure that is acceptable to audiences to achieve presentation goals by referring to the described domain relations, logical relations, and presentation goal vocabulary, which are shown in the left part of the view. For example, the user can describe the presentation goal of “make audiences understand the roles of UML in software lifecycle” and then specify it to detailed sub-goals, as “make audiences understand the overview of software lifecycle by introducing the concept of software lifecycle” then “make audience understand the roles of UML in each process of software lifecycle by showing an example scenario.” In this case, domain-independent and presentation-task-specific exemplary scenarios that introduce, reject, or overview can be used for describing the presentation goals. They are specified in the presentation goal vocabulary described above. We specify them by surveying popular books and on-line resources related to presentation know-how [3][8][9][10] and by referring to the learning support systems’ goal ontology proposed by Hayashi et al. [4]. Furthermore, the user can specify intentional effects to audiences in greater detail using graphical representations in lower parts of the view.

Fig. 2: Designed learning environment for presentation-material design

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Figure 2(v) portrays knowledge resources. Because the user can bookmark them by connecting with related domain concepts in (i), arguments in (ii), and the slides in (iii), the user can refer to them easily by clicking the nodes or slides in respective windows. The salient advantage of this learning environment from the viewpoint of the improvement of presentation quality and developing her presentation skill is expected to be that the environment encourages learners’ spontaneous meta-cognition during the presentation-material preparation processes. Visually externalized information in (i) and (ii) encourages self-monitoring and confirms processes of the user’s own understanding and self-control processes. Furthermore, visually externalized information in (iv) encourages self-monitoring and confirmation processes of rationality between a goal and its sub-goals and motivates the user to be conscious of constructing an accurate audience model. The described information represents users’ intentions for the presentation. Consequently, it is used as a valuable learning resource for subsequent collaborative learning in the presentation rehearsal phase. 3.3 CSCL Environment for Presentation Rehearsal It is difficult for novice learners to be aware of mistakes embedded in their presentation materials because of the lack of other persons’ viewpoints about it. Furthermore, it is hard for another person to give appropriate instructions to the user because of the difficulties in inferring the creator’s design intentions. It is also difficult to teach knowledge of making sophisticated presentation materials in a general context that includes a variety of meta-cognitive knowledge: it is quite tacit, latent, and highly context-dependent. For those reasons, we designed a collaborative learning environment in which the user and the instructor can share not only presentation material but also learners’ intentions (rich contexts) behind the presentation materials, which encourages well-focused communications among learners. Consequently, it encourages smooth knowledge transfer. Figure 3 shows a collaborative learning environment for presentation rehearsal and reflection phase. Both the presenter and learning partners (playing the audience role) can share the same information shown in the figure. In this environment, the presenter can examine each component of the presentation materials collaboratively or conduct a rehearsal using text-based or speech-based chat. The environment includes three windows. Figure 3(i) is designated as the presentation view based on a PPT slide show. Both the presenter and learning partners (LPs) can advance the slides. Figure 3(ii) is an intention view, in which LPs can refer and understand the presenter’s intentions of the slides set in the presentation-material preparation phase: roles of the slides from the viewpoint of achieving the presentation goals described above, subjects of the slide, what kinds of reactions the presenter expects from audiences, and so on. By clicking a presentation goal node, participants can advance and review a series of slides that are intended to achieve the clicked presentation goal. Furthermore, LPs can evaluate each presentation material by checking the items shown in the lower part of the view. These items are constructed automatically using the system based on the described presentation goal hierarchy. Figure 3(iii) is a text-based chat window with which participants can communicate with each other. Generally speaking, it is difficult to evaluate the presentation materials because of the difficulties in understanding the presenter’s intentions. The major advantage of this collaborative learning environment is that the participants can refer to the presenter’s presentation goals and intentions in addition to presentation materials. This encourages smooth discussions that specifically address whether the presenter’s intentions are valid,


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Fig. 3: Designed collaborative learning environment for presentation rehearsal whether presentation materials adequately reflect the described intentions and so forth, because LPs can have a viewpoint for evaluation and discussion. For example, a presenter’s intention of the slide shown in Fig. 3(i) is described in the upper right part of Fig. 3(ii) in natural language as “understanding the overview of roles and merits of using UML in the software lifecycle.” Furthermore, the user’s intentions of achieving this presentation goal contribute to realization of the upper presentation goal of “explaining the roles of UML in software lifecycle” are represented graphically in the presentation goal hierarchy. Based on the goal hierarchy, the system can construct items for evaluation: “The presenter intended to explain an overview of roles and merits of using UML in software lifecycle. Can you understand?”, “Is this slide valid from the viewpoint of introducing the UML overview?” and so on. The LPs can evaluate the slide according to the evaluation items. The evaluation results are recorded and sent to the presenter. Furthermore, participants can discuss and send their suggestions: “I think it is better to explain the difficulties of each process in greater detail to achieve your goal. Because the audience can understand the roles of UML in greater detail based on their deep understanding of the necessities of UML” and so on. This kind of well-focused discussion can be inspired by referring to presenter’s intentions and items for evaluation. By providing the CSCL environment proposed here, presenters can use experiences of presentation-material preparation as valuable learning resources for developing presentation skills.

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4. Conclusions and Future work This paper presented a description of the outline of our learning support environment for developing presentation skills. The major advantage of our system is that it is designed to encourage the presenter to perform meta-cognitive activities in presentation-material design and import expertise of other experienced learners in presentation rehearsal based on the cognitive model. It is important to produce the model assumed in the system design that is explicit not only for sophisticated learning environment but also for its continuous improvement. We were unable to describe the learning environment for review after performing a presentation, which would allow the user to analyze their presentations collaboratively. This issue will be examined in another paper. We hope to improve the presentation goal vocabulary and build presentation skill ontologies in our future work. Acknowledgments We are very thankful to Mr. Takashi Kajino and Masayoshi Toyokura for their kind help and efforts. This research was supported in part by a grant from the Matsushita Education Foundation. References

[1] Brown, A. L., Bransford, J. D., Ferrara, R. A., and Campione, J. C. (1983) Learning, Remembering, and Understanding, In: E. M. Markman and J. H. Flavell. (Eds.), Handbook of child psychology (4th ed.) Cognitive Development, Vol. 3, Wiley, 515–529. [2] Flavell, J. H. (1976) Metacognitive Aspects of Problem Solving, in L. Resnick (Ed.), The nature of intelligence. Lawrence Erlbaum Associates: Hillsdale, NJ, 231–235. [3] Harvard University Writing Center: http://writing2.richmond.edu/writing/wweb.html [4] Hayashi, Y., Ikeda, M., and Mizoguchi, R. (2004) A Design Environment to Articulate Design Intention of Learning Contents, International Journal of Continuing Engineering Education and Life Long Learning, 14, 3, 276-296. [5] Seta, K., Tachibana, K., Umano, M., and Ikeda M. (2003) Basic consideration on reflection in problem-solving oriented learning, Proc. of the International Conference on Computers in Education (ICCE-03), Hong Kong, China, 160-168. [6] Seta, K., Tachibana, K., Fujisawa I., and Umano M. (2004) An ontological approach to interactive navigation for problem-solving oriented learning processes, International Journal of Interactive Technology and Smart Education, 1, 3, 185-193. [7] Seta, K., Tachibana, K., Umano., M., and Ikeda, M. (2005) Human Factor Modeling for Development of Learning Systems Facilitating Meta-Cognition, in Chee-Kit Looi, David Jonassen, Mitsuru Ikeda (Eds): Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences, Frontiers in Artificial Intelligence and Applications, Vol. 133, IOS Press, 396-403, (also Proc. of the International Conference on Computers in Education (ICCE-05), Singapore). [8] Stanford University Writing Center: http://swc.stanford.edu/library.htm . [9] Takada, T. (2004) Logical Presentation, Eiji Press (in Japanese). [10] Teruya, H., and Okada. K. (2001) Logical Thinking, Toyo Keizai Inc (in Japanese).


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Ontological Modeling Approach to Blending Theories for Instructional and Learning Design Yusuke HAYASHIa, Jacqueline BOURDEAUb, Riichiro MIZOGUCHIa a ISIR, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka, 567-0047 Japan b LICEF, Télé-université, 100 Sherbrooke W., Montréal, (QC) H2X 3P2 Canada [email protected] Abstract: This paper proposes a modeling framework for learning and instructional design from the viewpoint of ontological engineering. One of the characteristics of this framework is a theory/paradigm-independent ontology for modeling learning/instruction. This paper discusses how our modeling framework with the theory/paradigm-independent ontology contributes to modeling learning and instruction from a comprehensive viewpoint of various educational theories. Keywords: Instructional design, Learning design, Ontology, Theory-aware, Design support

Introduction Sharing and reuse of information about not only resources but also their structure such as design intention have been brought to public attention as recent development of IT standards in the areas of learning, education, and training. Representative specifications include IEEE LTSC Learning Object Metadata (LOM) standard [9] and IMS Learning Design (LD) specification [10]. These bring about global benefits of exchange format for sharing and reuse of information about learning contents. However, a problem still remains: how to build “good” design for education using the standards. Of course, considerable achievement has been made in instructional and learning sciences. This being said, even though some educational theories (learning, instructional and instructional design theories) prescribe optimal/desirable methods of learning and instruction, many of the theories are not sufficiently articulated for the use of designers. Such theories allow for diverse interpretation and therefore may be difficult to use in practice. One of the reasons why these problems come up is that the description of educational theories is made in natural language, using different terminology. As Reigeluth points out, although many theories prescribe the same method for the same situation, these are described in different terminology [18]. This leads to a diversity of theories that are all open to interpretation. Even for experts, it is sometimes difficult to appropriately use theories while having a clear understanding of the similarities and differences between them. Consequently, we must first establish a common basis for understanding the theories at a conceptual level, along with organized concepts and vocabulary. The goal of this study is to build an engineering infrastructure that enables designers to select instructional and learning theories and blend them into an instructional/learning design. This paper thus proposes a modeling framework for learning and instructional design from the view point of ontological engineering [14], based on the results of previous

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Y. Hayashi et al. / Ontological Modeling Approach to Blending Theories

research in this respect [1][2][16] . In addition, the purpose of this study is not to expose a scientifically valid basis for organizing educational theories nor to reconstruct them on this basis, but rather to find an engineering approximation that allows the building of an engineering infrastructure that enables practitioners to utilize educational theories. This paper is organized as follows. The next section presents our perspective on modeling of learning and instruction and proposes our resulting modeling framework for them. Section 2 illustrates an example of a model of theoretical knowledge for education based on this modeling framework. Section 3 discusses an application of this framework to design support system for learning scenarios or activities. The final section concludes this paper and states the issues to be examined further.

1. Ontological modeling framework for instructional/learning design This study adapts the ontological modeling framework of functional design knowledge in the world of manufacturing industry by Kitamura et al. [12] to model educational theories. The main characteristic of this framework is independent conceptualization of what is achieved and how to achieve the change in the target things. The former is organized as ontologies of functionality, and the latter is as ways of function achievement. The function of artifacts (devices) is modeled as hierarchical structure of component functions linked with ways of function achievement. This model is called “function decomposition tree”. Kitamura et al. have confirmed the effectiveness of the framework in: (1) affording a better understanding of the functionality of devices, (2) facilitating the sharing of design rationales of devices, (3) supporting the improvement of functionalities, and so on. Although the domain is different from educational knowledge, we believe that it is applicable to the systematization of theoretical knowledge for instructional/learning design. For the educational domain, we have developed an ontology of learning, instruction and instructional design (L/I/ID) [1][2][16] . This study proposes an ontological modeling framework for education based both on the L/I/ID ontology and Kitamura’s framework [8]. It aims at facilitating (1) the sharing of a model of instruction/learning and (2) the application of educational theories appropriately to the modeling. IMS Learning Design (LD) specification [10] has recently focused on the sharing aspect, and we believe that our approach will contribute to make IMS LD specification work with educational theories. Roughly speaking, the L/I/ID ontology is composed of five major concepts: concepts related to Common, Learning, Instructional and Instructional design worlds and Educational event in the learning and the instructional worlds [8]. This ontology is developed in an effort to model learning and instruction in any paradigm - Behaviorism, Cognitivism and Constructivism -. Ertmer and Newby [6] assert that although each of the paradigms has many unique features, each describes the same phenomena (learning). In a similar line of thought, one of the notable features of this ontology is the conceptualization of relation between learning and instruction. The meaning of an instruction is defined by the change of learner state as achieved or by the intended result of learning. However, instruction is defined independently of the change of learner state. That is because an instructional action may have different effects on the change depending on the context. Independence is the key to allow for a variety of combinations of instructional actions and effects and to compare strategies provided by educational theories. The Learning/instruction process is modeled based on our ontological modeling framework for education. A unit of learning/instruction is described as I_L event, which is a sub-class of Educational event. An I_L event is defined as a combination of instructional action and change of learner state caused by a learning action. This definition allows to describe the relation between instruction and learning in a learning/instruction process. The


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(A) Preparing the learner for learning / Being ready to learn

Macro -I_L event

Way of achievement Preinstruction

(B)

is-achieved by

(C) Motivate / Motivated

Stimulate recall of prior learning / Recalling prior learning

I_L event decomposition

Micro -I_L events

Fig. 1 An example of I_L event decomposition whole process is modeled as a function decomposition tree. In our framework, this model is a hierarchical structure of I_L event to achieve a certain change of a learner state. Thus, it is called “I_L event decomposition tree”. In an I_L event decomposition tree, an upper (macro) I_L event is connected with the lower (micro) ones by way of achievement of change of a learner state (referred to just as “Way” hereafter). For example, consider a situation where an instructor wants a learner to be ready to learn and it is necessary that the learner is motivated into the learning and then that he recalls prior learning. Fig.1 illustrates this case as I_L event decomposition. An oval node represents an I_L event. Fig.1 (A) indicates the macro-I_L event, in which “Preparing the learner for learning” is the instructional action and “Being ready to learn” is the change of learner state. A conceivable process to achieve this is to motivate the learner (Fig. 1 (B)) and then to stimulate recall of prior learning (Fig. 1 (C)). The former instructional action brings about the learner state “Motivated” (Fig. 1 (B)), and the latter does “Recalling prior learning” (Fig. 1 (C)). A way is a description of relationship of such a decomposition of the required change into the detailed changes and actions to achieve them. A ‘Way’ has two sorts of interpretations. One, so-called bottom-up manner, is that the sum of the changes of learner state in micro-I_L event promotes the changes of learner state of the macro-I_L event. This manner, which concentrates on states, is descriptive. It describes which outcome is produced by a sequence of changes of learner state. The other, so-called top-down manner, is that an instructional action of a macro-I_L event is decomposed into detailed/concrete instructional actions of micro-I_L events. This manner, which concentrates on action, is prescriptive. It prescribes which sequence of instructional actions is required for performing the intended instructional action. Following the top-down interpretation, this study proposes a method to systematize theoretical knowledge. The theories prescribe strategies for planning instructional and learning process according to supposed situations. These strategies may superficially vary from theory to theory but, as discussed in the introduction, some of the differences just come from the difference in terminology that each theory uses. We assume that some essentials of theories characterizing themselves are disclosed if their strategies are resolved conceptually. In our framework we propose modeling learning/instructional strategies as Way using L/I/ID ontology. A set of Ways derived from a theory characterizes the theory from the prescriptive aspect. In addition, modeling strategies on a common basis, which is L/I/ID ontology, is expected to disclose not only characteristics of each theory but also commonality among theories. We call Ways derived from theories ‘Way-knowledge’. Such Ways can be applied to various instructional/learning designs if the supposed situation is matched. This study aims to support designers blending theories into their own instructional/learning design by

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providing an engineering infrastructure for accumulating and sharing variety of Way-knowledge derived from theories.

2. An example of comprehensive model of educational theories In our ontological modeling framework for education with the L/I/ID ontology, educational theories are modeled as Way-knowledge. In this section, we will discuss how Way-knowledge describes educational theories comprehensively. Fig. 2 shows an example of an I_L event decomposition tree. This tree represents a decomposition of the I_L event, “Facilitate learning / Knowledge state”. An oval node represents I_L event. Its label expresses combination of instructional action and change of learner state in the form of “instructional action/change of learner state”. The links between I_L events found below and above a square represent a Way. I_L events joined to a square and located below it represent an AND relationship. This means that, according to a Way, an upper I_L event is achieved when all of the lower I_L events are achieved. If more than one Way leads to an I_L event, it represents OR relationship among Ways, that is to say, there are more than one method to achieve the change of learner state. This relationship provides alternatives to make more detailed instructional/learning design for designers. Such an I_L event decomposition tree with OR relationship is called “General I_L event decomposition tree”. The general I_L event decomposition tree shown in Fig. 2 covers the whole process of instruction/learning - from preinstruction to assessment -. The foundation of this process is the five major learning components by Dick et al [5]. They are a summary of Gagne’s nine events of instruction [7]. These theories are basically considered to be based on cognitivism approach but to be somewhat eclectic in view of paradigms - behaviorism, cognitivism and constructivism - [17]. This model is intended to be a comprehensive model of instructional/learning theories by giving a shape to Dick’s components based on various theories in different paradigms. Now, we take a close look at the decomposition below the I_L event, “Exercise the learner / Absorbed (Fig. 2 (B))”, because this decomposition includes both cognitivism and constructivism approaches with OR relationship of Ways. Ways included in this decomposition are based on cognitive approach - Gagne’s nine events of instruction [7] and Merrill’s component display theory [13] - and constructivist approach - Collins’s cognitive apprenticeship [4] and Jonassen’s model for designing constructivist learning environments [11] -. The point of selecting principles from multiple theories is the change of learner state. According to Carey [3] and Ertmer and Newby [6], aspects of constructivist approach can be compatible with aspects of prescriptive approach for specified types of learners and learning outcomes [5]. From this viewpoint, Dick relates constructivist strategy to the five major learning components. In the similar line of these thought, we proposes a modeling method for organizing any theories in terms of what and how their strategies intend to achieve. This study sets up the working hypothesis that the idea of states in the learning process is common while the assumed mechanism of developing knowledge is different for each paradigm 1 . With this consideration, we have organized learner states intended to be shared among different educational theories. “Being ready to learn (Fig.2 (A))” and “Self-aware (Fig.2 (D))” are examples of such learning states. Making attempts to organize common concepts and to interpret (supposed) intention of theories as the change of learner state will afford a better understanding and utilization of educational theories. 1

Note that we are not saying all the learning theories share the same learning states.


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Let us examine blending of these approaches in more detail. The I_L event “Exercise the learner / Absorbed (Fig. 2 (B))” is decomposed into two sub-I_L events: “Eliciting performance / Absorbed (Fig. 2 (C))” and “Providing informative feedback / Self-aware (Fig. 2 (D))”. This decomposition is derived from Gagne’s events. To put it briefly in terms of what to be achieved, the former objective is to develop what is learned through practice and the latter one is to become self-aware in order to make the practice more efficient. These I_L events can be decomposed by both of cognitivist and constructivist approaches. For example, there are two Ways to decompose “Provide informative feedback / Self-aware”. Cognitivist approach (Fig. 2 (10)) gives relatively explanatory feedback, e.g. giving correct answer or help. On the other hand, constructivist approach (Fig. 2 (11)) gives relatively inquiring feedback, e.g. providing hint or assisting learners to articulate. Constructivist approach also has other characteristics such as authenticity and self-construction of knowledge, though this is not shown in Fig. 2 because of presenting a contrast to explanatory methods. These characteristics can be described in a more detailed model but are too complicated to explain in detail here. The key point of the blending of these approaches is, as discussed in the previous section, to take particular note of the change of learner state. These theories indeed have differences in the method. However, at the same time, the objective (intended change of learner state) can be described as common in becoming self-aware in this example. From this viewpoint, we set both the cognitivist and constructivist approaches as a Way to decompose “Providing informative feedback / Self-aware (Fig. 2 (D))”. In the same manner as this, other cognitivist and constructivist methods are also organized as Way knowledge shown in Fig. 2 (1) – (13).

3. Toward a theory-aware instructional/learning design support system One of the remarkable applications of our ontological modeling framework for education is theory-aware design support system for learning contents [8]. By “Theory-aware” [14], we mean the capability of information systems which can support the activities of users based on the understanding of relevant theories. Nkambou et al. [15] discuss the benefits of accessing theories and required functionalities of theory-aware ITS authoring environment. The benefits are: 1) make decisions (macro, micro) after reflection and reasoning, 2) communicate about or explain their design decisions, 3) check consistency among design decisions, intra-theory and inter-theories, 4) produce scrutable learning environments, 5) have heuristic knowledge grounded in theoretical knowledge. The required functionalities are that authors can: 1) ask the system what theories apply best to this or that learning situation/goal, 2) ask the system to show examples, 3) ask the system for advice on whether this element of a theory can be combined to an element from another theory, what is the risk associated to doing so, any preferable other solution, etc. Referring to their argument, this study focuses on the following two points as the requirement for realization of theory-awareness in an instructional/learning design support system: The system can (1) help designers to build theoretically valid model of learning/instruction, and (2) explain the help with the theoretical justification to designers. We now discuss how our ontological modeling framework for education contributes to these two advantages. As discussed in the previous sections, learning/instructional strategies included in educational theories are described as Way-knowledge in our modeling framework. Way-knowledge is a relationship between macro-I_L event and some micro ones with desired change of learner state (goal) and necessary condition for the change (situation). In

42


addition, Way-knowledge is described with not each theory’s own terminology but our L/I/ID ontology. This ontology is designed to accept terminological difference among educational theories and paradigms on a conceptual level as mentioned before. Based on this ontology, Way-knowledge is not only human-readable but also machine-readable. This enables systems to expound educational theories described as Way-knowledge and to interpret models of learning/instruction built by learning/ instructional designers founded on educational theories. These will be quite helpful to learning/instructional designers for in-depth understanding of theories and for communicating about their design decisions and products. Another characteristic of our modeling framework is that a theory is described as a set of Way-knowledge. In other words, a theory is split into pieces of strategy and each strategy is described as a Way-knowledge. For example, in fig. 2, Way-knowledge (1), (2), (4), and (6) is derived from Gagne and Briggs’s theory, and (8) and (12) derives from Merrill’s component display theory. Such modeling of theories helps learning/instructional designers to make a model of learning/instruction from various viewpoints such as those that follow. One viewpoint is that a support system can pick and choose applicable strategies within a theory according to the grain size of process. If a designer wants to decompose complete learning/instructional process by him/herself, Way knowledge (1) is recommended. On the other hand, if he/she wants to do more detailed process, Way knowledge (4) or (6) are recommended. The other is that a support system can also suggest whether an element of a process derived from a theory can be exchanged by an element derived from another theory. Way-knowledge (10) and (11) illustrated below the I_L event “Provide informative feedback /Self-aware (Fig. 2 (D))” is a good example to illustrate the possibility of exchange. Such guidance becomes possible with inter-theory comparison in terms of strategies included in theories or derived from them.

4. Conclusion We have discussed a modeling framework for learning/instructional design based on ontological engineering. The characteristics of this framework include: 1) a theory/ paradigm-independent ontology for modeling learning/instruction, 2) compatibility between prescriptive and descriptive models derived from educational theories, 3) theory-awareness brought out by an ontological modeling framework. In this paper, we have concentrated on how the theory/paradigm-independent ontology contributes to modeling learning/instruction from a comprehensive viewpoint of various educational theories. Our modeling framework based on this ontology will be helpful to blend of educational theories as discussed in section 2, and to enhance the quality of support by information systems for learning/instructional design as discussed in section 3. The following issues to be examined further still remain: 1) investigating the theory/paradigm-independence of the L/I/ID ontology through organizing much more educational theories in our modeling framework, 2) considering the relation between learning objects and abstract design of learning contents described as an I_L event decomposition tree, 3) implementation of a theory-aware design support system for learning contents, and 4) compliance with standards for semantic web and e-learning.


43

Acknowledgments We would like to thank to Dr. Daniele Allard for her helpful comments. References [1] Bourdeau, J. and Mizoguchi, R.: “Collaborative Ontological Engineering of Instructional Design Knowledge for an ITS”, Proc. of ITS2002, pp.399-409, 2002. [2] Bourdeau, J. and Mizoguchi, R.: “Selecting theories in an ontology-based ITS authoring environment”, Proc. of ITS’2004, pp. 150-161, 2004. [3] Carey, J. O.: “Library skills, information skills, and information literacy: Implications for teaching and learning”, School Library Media Quarterly Online, Vol. 1, 1998 Available: http://www.ala.org/aasl/SLMQ/skills.html [4] Collins, A., Brown, J. S., and Newman, S. E.: “Cognitive apprenticeship: Teaching the crafts of reading, writing and mathematics”, In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser. Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 453-494, 1989. [5] Dick, W., Carey, L., and Carey, J. O.: The systematic design of instruction, Sixth edition, Addison-Wesley Educational Publisher Inc., 2004. [6] Ertmer, P. A., and Newby, T. J.: “Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective”, Performance Improvement Quarterly, 6 (4), 50-70, 1993. [7] Gagne, R. M. and Briggs, L. J.: Principles of Instructional Design (2nd Ed.). Holt, Rinehart and Winston, New York, 1979. [8] Hayashi, Y., Bourdeau, J. and Mizoguchi, R.: “Ontological Support for a Theory-Eclectic Approach to Instructional and Learning Design”, Proc. of EC-TEL 2006, (to appear), 2006. [9] IEEE LTSC, The Learning Object Metadata standard. Available: http://ieeeltsc.org/wg12LOM/lomDescription [10] IMS Global Learning Consortium, Inc.: IMS Learning Design. Version 1.0 Final Specification, 2003. Available: http://www.imsglobal.org/learningdesign/ [11] Jonassen, D.: Designing constructivist learning environment, In Reigeluth, C. M. (Eds.): Instructional-design theories and models A new paradigm of instructional theory, Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc., pp. 215-239, 1999. [12] Kitamura, Y., Kashiwase, M., Fuse, M., Mizoguchi, R.: “Deployment of an Ontological Framework of Functional Design Knowledge”, Advanced Engineering Informatics, Vol. 18, Issue 2, pp. 115-127, 2004. [13] Merrill: “Component display theory”, In Reigeluth, C. M. (Ed.), Instructional-design theories and models: An overview of their current status. Hillsdale, New Jersey: Lawrence Erlbaum Associates, Inc., pp. 279-333, 1983. [14] Mizoguchi, R. and Bourdeau, J.: “Using Ontological Engineering to Overcome Common AI-ED Problems”, International Journal of Artificial Intelligence in Education, Vol.11, No.2, pp.107-121, 2000. [15] Nkambou, R., Frasson, C., and Gauthier, G.: “CREAM-Tools : An Authoring Environment for Knowledge Engineering in Intelligent Tutoring Systems”. In: Murray, T., Blessing, S. and Ainsworth, S. (Eds): Authoring Tools for Advanced Technology Learning Environments: Toward cost-effective adaptative, interactive, and intelligent educational software. pp. 93-138. Kluwer Publishers, 2003. [16] Psyche, V., Bourdeau, J., Nkambou, R., and Mizoguchi, R.: “Making Learning Design Standards Work with an Ontology of Educational Theories”, Proc. of AIED2005, pp. 539-546, 2005. [17] Snelbecker, G. E.: “Is instructional theory alive and well ?”, In Reigeluth, C. M. (Ed.), Instructional-design theories and models: An overview of their current status. Hillsdale, New Jersey: Lawrence Erlbaum Associates, Inc., pp. 437-472, 1983. [18] Reigeluth, C. M.: “Instructional-design: What is it and why is it?” In Reigeluth, C. M. (Ed.), Instructional-design theories and models: An overview of their current status. Hillsdale, New Jersey: Lawrence Erlbaum Associates, Inc., 1983.

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Neutral (1)

Neutral

Constructivism

(2)

Cognitivism

Neutral

Constructivism (4)

(5)

(3)

(B)

(6)

(C)

Constructivism (D)

(7)

Cognitivism

Constructivism

Constructivism Cognitivism (8)

(9)

(11)

(10)

Constructivism Cognitivism (12)

Fig. 2 A general function decomposition tree

(13)


(A)


45

A Case of Blending Learning in Computer Teaching üüThe Model and the Application a

Li Cuiling a, Wang Hong b, Zhang Huiyu a School of Computer and Information Technology, Beijing Jiaotong University, China b Higher Education Press, China [email protected]

Abstract: In this paper, firstly we introduce four key elements of application of blending learning in teaching, Dnd then we talk about the application of blending learning in the field of computer teaching based on the practical case of curriculum of ‘Fundamentals of Computer for University’. Keywords: Blending Learning, model, application

1. What is Blending Learning? Simply put, blending learning can be described as a learning mode that mixes different delivery modalities with the objective of combining the advantage of traditional learning method and that of e-learning. That is, the instructor should lead, enlighten and watch the learning course, and also inspire students of initiative, enthusiasm and creativity during the learning course.

2. The Model --Four Radical Elements of a Blending Learning Process Based on the experience of deploying blending learning and the control over the quality of the process, four key ingredients emerge as follows: x Arrange before class. x Strategy analysis. x Performance support service. x Evaluations.

3. The Application of a Case of Blending Learning The curriculum of ‘Fundamental of Computer for University’ is made up of a web station, books, e-learning materials , etc. The web is made up of ‘Curriculum Summary’, ‘Curriculum Content’, ‘Exercises’ and ‘Schoolwork’, ‘Courseware’, ‘Practical Teaching’, and ‘Test Online’, etc. It is the platform to implement the network teaching.

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L. Cuiling et al. / A Case of Blending Learning in Computer Teaching

3.1Preparation before class 3.1.1 Identifying the instruction goals and summary Adding the summary before the learning content can make students understand the whole learning target, and at the mean time help teachers control the learning process and pace of learning. 3.1.2 Analyzing the learners The objects of the curriculum are freshmen, and the starting levels vary widely with the differences among them in the aspect of the mastering of computer knowledge, just as the table shows.

percentage year 2002

Table 1 the Students’ level of Mastering Computer Knowledge Know some Know computer Online and Use Use simple before and can send e-mail Word Excel knowledge operate simply 43ˁ 32ˁ 23ˁ 28ˁ 18ˁ

2003

54ˁ

51ˁ

41ˁ

30ˁ

23ˁ

2004

66ˁ

62%

54ˁ

42ˁ

27ˁ

2005

72ˁ

71%

68ˁ

50ˁ

35ˁ

We found that the general level of the degree that students mastering computer knowledge increased as the time goes. It is very good for our network teaching, but we should recognize the differences among different areas and different individuals. 3.1.3 Dividing students into groups In order to carry out the process of study successfully, we divided them into groups. We considered of the elements such as the characteristics of students, original level of knowledge mastering and skills, and sex, etc. And also we encouraged them to study in cooperation. In this practice, we divided 35 students into 7 groups. We also let them vote one leader in each group. Communication inner groups and between groups were encouraged. In order to know the knowledge basis of the students, teachers conducted a pre-assessment by giving a corresponding test using the system of ‘test online’ on the web. 3.2 Implementing the process With the goals of training students of abilities of self-study, innovation and cooperation, we used the web of ‘Fundamentals of Computer for University’ and adopted different kinds of instruction strategies to develop the practice of teaching of computer for university based on the mode of blending learning. Therefore, we adopted 3 strategies as follows: class instruction, self-study and cooperative study. x Face-to-Face Instruction. According to the approach of classifying teaching results by Gagne, we classifyed instructional goals in the following domains: verbal


47

information, intellectual skill, psychomotor skill, and attitude. Different contents should be presented by different way, as follows:

clarifying verbal information intellectual skill psychomotor skill attitude

Table 2 Clarifying and Method of Learning Method for learning Face-to-face, self- learning, cooperative learning, etc Face-to-face, cooperative learning, discussion learning, exploring learning, research learning, etc Operation exercise, case learning cooperative learning, Face-to-face

x

Self-study. In the design of the curriculum, we considered adequately that we should train them to study by themselves, to analyze and solve problems by themselves. x Cooperative Learning. We have put forward the plan to divide students to small groups. During the learning course, small-group learning was feasible and very effective. Firstly, we assessed the former situation of groups, allowing the adjustment of group numbers and outer-group numbers, in order to avoid the disadvantage of the method of group that teachers decided. The next task was to divide the work to each number in a group. After inner-group communications, the group leader could complete this task. Then a discussion was followed to decide everyone’s task. Secondly, we chose the corresponding learning task, and developed the group learning using three different methods: Inner-group Communication, Inner-group Cooperation and Communications among Groups. At last, a simple assessment concluding an assessment to the whole fruit for every group and each member in a group was done. During the learning course, we used the tool of the web of ‘Fundamentals of Computer for University’ to develop cooperative learning, providing a new means. 3.3 Performance support materials During the practical teaching course, to provide students with good service and support for study guarantees the completion of the learning task. Considering of the characteristic of blending learning, we should do this as follows. x Providing Comprehensive Resources. It should contain teaching materials and learning materials. Supported by the web of ‘Fundamentals of computer for university’, it also should contain some digital resources, for example, powerpoint lectures and other complementary resources, etc. x Tutoring and Answering. We arranged face-to-face tutoring once a week. The teacher and teaching fellow were responsible to answer the questions, correct students’ assigned work, tutor their experiment and psychology, train them use the platform to study, and tutor their learning style. They also deployed discussion areas online to encourage students to communicate and study. x Holding lectures. Holding lectures could not only increase students’ knowledge, but also arouse their interests.

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4. Evaluation A good mechanism of evaluation is a very useful support to teaching course. The evaluation of the mode of blending learning is classified into two categories: formative evaluation and summative evaluation, just as the following table shows. Table 3 Item, Content and Methods of Evaluation Content of evaluation Methods of evaluation Evaluation to students Usual behavior and the mastering Formative evaluation, degree of content and skills summative evaluation Evaluation to teachers The feasibility and effects of the Formative evaluation, instructional design summative evaluation Evaluation to learning The learning style and other Formative evaluation strategy elements during the learning course Item

Formative evaluation is the process of collecting data and information in order to improve the effectiveness instruction. It contains evaluation to the behavior and learning effects of students, the organization and service of teachers and various strategies that are adopted during the teaching activity. Its purpose is to find problems and solve them in time to guarantee the teaching effects. Summative evaluation is the process of collecting data and information in order to make decisions about the acquisition or continued use of some instruction. In the summative evaluation of this term, generally we adopted the form of final examination. Students’ usual behavior accounted for 60%, homework accounted for 20% and final exam accounted for 20%. Acknowledgments We thank the people, Wang Hong and Zhang Huiyu, who helped me a lot during the course.

References [1] Kekang He, 6ee the 1ew 'evelopment in the 7heory from %lending /earning. http://www.etc.edu.cn/academist/hkk/blending.htm. [2] Ronghuai Huang, Jinbao Zhang and Yan Dong,The Discussion about the Four Key Ingredients during the Course of Network Teaching. http://www.vschool.net.cn/elr/zhiliang/zl0015.htm. [3] McAteer, E., Tolmie, A., Du.y,C., Computer-mediated &ommunication as a /earning 5esource. Journal of Computer Associated Learning,(1997).


49

A Combined Method for Extracting Rules with Improved Quality ˄

1

Fuyan Liu ʳ Hangzhou Dianzi University, Hangzhou, P.R.China, 310008 [email protected]

Abstract: This paper presents an effective approach, which combines association rules and clustering algorithm for extracting rules on the web. The advantage of the method is that it can provide more precise and detailed rules compared with traditional algorithms. The paper analyzed some issues in previous approaches first. Then the proposed method is presented. Experiments are performed to evaluate the performance of the combined approach. Experimental results show that the proposed method is more effective and efficient. Keywords: Clustering, association, inference, user profile, confidence

Introduction Compared to traditional techniques, one distinguished capability of the web is the ability to perform continuous unobtrusive observations on user browsing behavior. A web server can collect the information on the decision process rather than only the decision result. Based on these data, we can capture a more comprehensive user profile, which can be used as foundation for serving users. Usually there are two types of user service: individual-based and group-based. Most previous studies focused on individual-based inference. However, to new users, the individual-based rules may not make sense, even to old users, may be still difficult to adapt to their changing preferences. Thus, it is better to use a group-based inference mechanism. In this paper some concepts such as user profile are introduced first. Then some issues in previous approaches are analyzed. In the following section an effective combined approach to infer rules with improved quality is proposed. In addition, experiments are performed to evaluate the performance of the proposed approach. The experimental results show that the combined method can provide more effective and efficient performance.

1. Some Concepts There are different views about the user profile, for example, paper [1] defined the user profile as a set of keywords, which describes the information the user is interested in; paper [2] defined the user profile as user’s likes and dislikes. We adopt a broader view of the user profile as a database, which maintains all information about users [3]. For information from inside the web site, one source is provided by customers on purpose, such as data from registration form, online questionnaire, online orders, and message board, etc. These data can be collected through CGI or Cookies. Another way to collect information from inside the web site is to observe users’ behavior without the users’ awareness, e.g. online browsing behavior can be collected by ASP, Cookies or from the log files of the server. Information from outside the web site might be collected by

50

F. Liu / A Combined Method for Extracting Rules with Improved Quality

search engine from virtual community such as bbs, newsgroups or personal homepage, etc. User data can be divided into raw data and refined data. Raw data is the original data, such as a user’s ID, age, sex, browsing behavior and purchasing records. Refined data refers to data such as usage status, habit, affinity group, and preference evaluation, etc. It is extracted, hybrid or inferred from raw data on purpose to have a better description of users. Refined data can consist of individual related data as well as group related data. It is better to classify inference foundations into two types: individual-based and group-based. The individual-based inference is based on individual data. The group-based inference is based on affinity group data. Both can be applied to infer a user profile as well as association rules. Our paper will focus on group-based inference.

2. Considerations of the Proposed Method Most of previous studies focused on individual-based inference. There are some unique problems that stem from the special characteristics when neural networks or induction learning techniques are used, e.g. a stochastic problem or large number of attributes, infinite number of states, low response rate, and difficulty in explaining results etc. may be faced. Therefore, in this paper we use association rules instead. An association rule is an expression: XY, where XI, YI, XY= I, and I={il, i2,..., im} is a set of items. Association rules can avoid the stochastic problem, since they calculate the proportion of the specific item sets in the database to identify rules rather than learn from each user [4]. Also association rules have no problems about large number of attributes and infinite number of states because the analysis unit is item sets such as transactions or browsing on the web not user’s attributes. Besides, the low response rate does not cause much trouble because browsing data can be collected from the web. In order to find association rules Apriori is the most essential algorithm. But the algorithm only explores the association between items. It is important to consider categories of different subjects in the data set. So we use Algorithm Basic, which takes account of categories to explore association rules [5]. Previous studies explore association rules based on all transactions, where they didn’t consider the concept of segmentation. Thus the extracted association rules are too general to reflect different user’s needs. But users of different affinity groups have different desires, geographical locations, preference and behavior, etc. It is better to cluster all users into different groups before exploring association rules. The main goal of cluster analysis is to identify clusters existing in the data. There are many clustering algorithms developed for different applications and k-medoid methods are more popular due to their robustness, efficiency and being independent of the order in which the objects are selected during operations. Among k-medoid methods, PAM is a simpler and well-accepted one [6]. However, PAM works quite well only for small data sets. Its computing complexity is O(k(n-k))2 for one iteration. When the number of objects n and the number of clusters k are quite large, then it is too costly. In this paper we adopt CLARA for clustering [7]. Unlike PAM which will deal with the entire data set, CLARA draws a sample of the data set first, then applies PAM on the sample. Its computing complexity is O(ks2 +k(n-k)), where s is the sample size and it is quite smaller than n.

3. Framework of the Method We propose a combined method to infer association rules, which is shown in Fig.1. Clustering is implemented first, which is based on user’s demographic, geographic, and


51

behavioral data stored in the raw data, while using CLARA to identify the affinity group. The k-medoids and the individual user clusters are recorded in the refined data set of the user profile for the purpose of identifying the affinity groups that users belong to. Then for each cluster, Algorithm Basic is used to Inference engine learn the associations of relevant subjects Association Clustering such as browsing patterns and transactions rule algorithm algorithm to find association rules, which are valid for (A-BASIC) (CLARA) a particular cluster only. These rules retrieve transactions or browsing patterns from the raw data set. For the description of Output Row Site site contents and advertisements, it retrieves Refined rule base related data from the site database. The data set data set database User profile results of group-based association rules are stored in output rule base. Fig.1. Diagram of the combined method 4. Experiments and Conclusions In order to evaluate the proposed method, experiments were performed. In the experiments, a user profile was built first by the method and then the performance of the method was evaluated. The data used are collected from experiments of online assignment in the course of the Management Information Systems (MIS), which involves five categories: Fundamental Techniques (FT), Strategic Planning and Development Approaches (SP&DA), System Analysis (SA), System Design (SD) and System Implementation (SI). There are 198 students of five classes participating in the experiments over a period of two weeks in our computer lab. The experiments began with a registration form that required students to fill in their personal data and then submit to the lab server. We also used log files in the lab server to collect browsing behaviors of students during the specified period. In the experiments, students are firstly classified by CLARA of a k-medoid method before exploring the association rules. The collected students’ demographic and behavioral data are used for clustering. The registration form is used to collect students’ sex, age, class, daily hours of browsing, daily hours of online doing assignment, date of finishing assignment and total hours spent on assignment. It is assumed that the students may fall into one of the three groups according to our experience and observation: Ɣ Group 1: They visit the web site regularly, almost every day, and spend more time on the assignment during the first week of the specified period and they finish the assignment in advance. Ɣ Group 2: They visit the web site randomly and spend less time on the assignment during the first week of the specified period. But they spend much more time during the second week and finish the assignment on time. Ɣ Group 3: They visit the web site randomly but almost do nothing on the assignment each day. And they rush into the assignment on the last one or two days during the specified period and can not finish assignment on time. As stated earlier, in this paper we use CLARA algorithm to perform clustering. For accuracy, the quality of the clustering is measured by the average dissimilarity of all objects in the entire data set, although CLARA draws a sample of the data set. Then we apply PAM on the sample. Here we set the number of samples to 3 and set the size of each sample to 60. The predefined 3 clusters are classified as shown in Table 1. It can be seen from Table 1 that cluster 1 has more female students and above 89.7% students of cluster 1 visited the web regularly every day and finished the assignment in

52


advance. Above a half of students spent less than 40 hours for finishing the assignment. The other two clusters in Table 1 can be analyzed similarly. After clustering, the association rules for browsing behavior were extracted with Algorithm Basic based on all students with and without clustering. As a result, the numbers of association rules are 55 and 13 respectively and the group-based approach can provide more detailed rules. Table 1. Result of clustering Cluster attributes Sex Age Total hours spent on assignment Date of finishing assignment Daily online hours

Male Female Average 30 hours 30~40 hours >40 hours In advance On the last day Delay Regularly Randomly

Cluster 1 10.6% 13.6% 21.4 5.1% 7.6% 11.6% 24.2% 0.0% 0.0% 21.7% 2.5%

Cluster 2 35.9% 24.7% 22.6 0.0% 0.0% 60.6% 35.4% 25.3% 0.00% 25.3% 35.4%

Cluster 3 9.1% 6.1% 20.9 14.1% 1.1% 0.0% 0.0% 0.0% 15.2% 0.0% 15.2%

Now we compare the performance using the association rules and two measures: support and confidence for the non-clustering entire data set and clusters separately. Table 2 shows the comparison of clusters with the non-clustering entire data set for each measure and each association rule. All rules in cluster 1, cluster 2 and cluster 3 have higher support and higher confidence than that of the non-clustering data set. Table 2. Measure comparison on browsing NonClustering Clustering Cluster 1 Cluster 2 Cluster 3 Association rules S C S C S C S C (C1,C2) 0.13 0.35 0.33 0.49 0.28 0.45 0.15 0.38 (C2,C3) 0.15 0.38 0.40 0.52 0.34 0.48 0.17 0.40 (C3,C4) 0.15 0.40 0.43 0.54 0.30 0.56 0.16 0.43 (A,C4) 0.12 0.26 0.18 0.31 0.20 0.32 0.12 0.28 (A,C5) 0.11 0.22 0.15 0.28 0.16 0.28 0.12 0.22 Note: (1) the value of support is set to 0.1; S, C represent degrees of support and confidence respectively. (2) A and C1, C2, C3, C4, C5 represent the assignment and five categories: FT, SP&DA, SA, SD, SI.

In this paper a combined approach is proposed to infer rules of user browsing on the web, which combines clustering and association rules to avoid the troubles such as the stochastic problem, the interpretation difficulty, too many attributes and a low response rate etc. Experimental results show that the combined approach increases the degree of both support and confidence of association rules and it confirms that the proposed approach can produce more detailed and precise rules.

References [1] John Davies, Richard Weeks, Mike Revett. Jasper: Communicating information agents for WWW. http:// www.w3.org/Conferences/WWW4/Papers/180/. [2] George Leaks, George M. Giggles (2004) A Lifestyle-based approach for delivering personalized advertisements in digital interactive television. Journal of Computer-Mediated Communication, 9, 2, 23-45. [3] Lai H., and Yang, T. C. (1998) A system architecture of intelligent-guided browsing on the Web. Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences, 4, 423-432. [4] Hsiangchu Lai and Tzyy-Ching Yang (2000) A group-based inference approach to customized marketing on the Web-integrating clustering and association rules techniques. Proceedings of the 33rd Hawaii International Conference on System Sciences, 6054-6063. [5] Shi Zhong-zhi (2002) Knowledge Discovery. Tsinghua University Press, Beijing. [6] Carey V. J, Kollekolle. http://www.biostat.ku. dk/~pd/bioC-2003/vclec4.pdf. [7] Shu-Chuan Chu, John F. Roddick, Tsong-Yi Chen and Jeng-Shyang Pan (2002) Efficient search approaches for K-medoids-based algorithms. Proceedings of TENCON '02, 712-715.

Programming



55

The Impact of CABLE on Teaching Computer Programming Ioana CHAN MOW, Wing K. AU & Gregory C. R. YATES Dean of Science, National University of Samoa Program Director & Senior Lecturer, University of South Australia Senior Lecturer, University of South Australia [email protected] Abstract: This paper reports the second of two studies on the impact of a Cognitive Apprenticeship-Based Learning Environment (CABLE) in the teaching of computer programming. The pedagogical model used in this study employs a combination of instructional strategies including directive support, responsive cognitive apprenticeship, collaborative learning, stimulating metacognition, using technologies via the use of teleapprenticeship and online discussion. In an earlier study, students who participated within the CABLE project scored more highly on test scores, relative to comparable students who did not participate within CABLE, but these effects were found to be restricted to highability students. In the present study, students who participated within CABLE scored more highly than those participating within the non-CABLE group. However, with an enhanced CABLE environment, the benefits of CABLE were now evident in both ability groups, with the effects being more prominent within the low-ability group. Keywords: Cognitive metacognition

apprenticeship,

collaborative

learning,

tele-apprenticeship,

1 Introduction Computer programming is a difficult and challenging subject area which places a heavy cognitive load on students [1, 2]. After two years of learning programming, most novice programmers are still struggling to be proficient [3, 2]. From an examination of current research in this field, it can be postulated, that one reason computer programming instruction seldom results in the successful transfer of problem solving skills, lies in a lack of understanding about good instructional approaches in this direction [4, 5]. This paper reports the second of two studies on the impact of a Cognitive Apprenticeship-Based Learning Environment (CABLE) in the teaching of computer programming that would promote problem-solving skills of university students in Samoa. The main instructional approach used in this study is described as cognitive apprenticeship. The notion of apprenticeship stems from Vygotskian psychology, but we based our approach after the work of cognitive psychologists such as Collins, Brown and Newman [6] and others. Cognitive apprenticeship places emphasis upon reflective thinking as a metacognitive experience. The approach encourages metacognitive thinking, the use of directed teacher instruction to guide problem solving, and the use of scaffolding. The pedagogical model researched in the two studies represents our attempt to devise a viable instructional model based around the construct of apprenticeship. The aim was to achieve this goal through using a combination of instructional strategies working in concert to

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I. Chan Mow et al. / The Impact of CABLE on Teaching Computer Programming

provide the learners with a highly demanding but responsive instructional experience that differed from those derived within more traditional learning environments. Cognitive apprenticeship is a model of instruction that involves the effective communication of domain knowledge in such a way that the students become aware of the thought processes involved in knowledge construction within that domain. As an instructional approach it is directed at teaching processes that experts use to handle complex tasks, characterized by a number of teaching methods [6]. The first of these, is modelling where the teacher models his or her thought processes in solving problems within a domain. The second of these methods is guided practice or coaching where the student attempts to solve the problems for themselves with the support of the teacher to answer specific queries. A third method is scaffolding, where the teacher assists students to manage complex task performance and then gradually withdraws support from the student (fading). Other key components of this approach include articulation, where the student attempts to articulate their problem solving strategies; reflection, where the students are encouraged to reflect on how they approached tasks and solved problems, possibly by discussion with other students and, finally, exploration which is intended to encourage learner autonomy and problem formulation by students. Another instructional strategy that is gaining prominence as an effective teaching method and is integrated into the learning environment being trialled is collaborative learning. There are many approaches to collaborative learning but all have the following characteristics in common [7]. It is a learning activity suitable for group work; it is small group based (usually 2-5); it has tasks which encourage cooperative behaviour; it is characterized by student interdependence; individual student accountability and responsibility for task completion. Our starting assumption is that the computer can be used as a tool assisting in both cognitive apprenticeship and collaborative learning. An important element of the CABLE learning environment is its use of computer-mediated communication techniques for implementing aspects of cognitive apprenticeship such as scaffolding, coaching, feedback and modelling. Computer-mediated communication refers to communication using a computer. Examples of computer-mediated communication include email, computer conferencing and electronic bulletin boards. How these techniques are utilized to support the learning process is referred to as electronic apprenticeship or tele-apprenticeship. Teleapprenticeship or online apprenticeship refers to the use of computer mediated communication techniques, for the implementation of cognitive apprenticeship [8]. Computer mediated communication techniques used in implementing tele-apprenticeship in the current study include email, online notes, interactive tests and a bulletin board. As mentioned earlier, the term used to refer to this pedagogical model, is the cognitive apprenticeship-based learning environment or CABLE, because although it is based on the cognitive apprenticeship approach, the approach used in this study, is further enhanced by the incorporation of elements of metacognition and collaborative learning, instructional elements, which studies have proven to be conducive to effective learning. Furthermore some elements of this cognitive apprenticeship based approach will be implemented electronically by means of email, bulletin board, online notes and interactive quizzes. This is referred to as tele-apprenticeship and a subsidiary aim of this research was to investigate the impact of computer-mediated communication in enhancing student learning. CABLE is a hybrid learning environment and it is implemented both in face-toface and online mode. This paper reports the findings of our second study on the impact of CABLE. In the initial study, results showed that students who participated in CABLE scored more highly on a post test measure of mastery of Java programming, relative to comparable


57

students who did not participate within CABLE. However, these effects were found to be restricted to high-ability students. These results were disappointing in that the hope was to develop instructional procedures that would provide a richly motivating and responsive educational context that would appeal to low-ability students. One goal of this program lies within reducing variance within student achievement levels. From these findings our team re-evaluated our procedures and modified CABLE to include more scaffolding and more online resources. The effectiveness of this enhanced CABLE model is evaluated in the present study.

2 Methodology 2.1 Research design

Students participating in University Foundation Computer Studies class at the National University of Samoa were allocated to either the CABLE treatment or were taught in accordance with the traditional university model of teaching (i.e. Non-CABLE methods). This division was achieved through students being enrolled within different class times. Although the initial total enrolment was 80 students, complete data were able to be collected from 39 students within the CABLE group, and 33 students from the traditional group. Participants were students within their first year of studies from the Foundation (64%) and non-Foundation (36%) programs. The Foundation program was for students who entered at a higher GPA and were preparing for studies in overseas universities whereas the non-Foundation program was for those who entered at a lower GPA and intending to pursue study locally. The approximate average age of the participants was 19 years. Student records were available from previous computing courses in the form of test scores. The design of the present study was very similar to the first study. After six weeks of exposure to the treatments, all students completed the final test paper, and also completed a questionnaire intended to tap into their evaluations of their course experience. The post-test consisted of six questions which evaluated their knowledge of Java commands and also practical questions which tested the application of these Java skills and knowledge to solving a problem. Students were given program codes to explain, to troubleshoot and to predict program output. Furthermore, the assumption made here was that computer programming was a form of problem solving and hence these scores represented the problem-solving ability of these students. The questionnaire consisted of Likert items which gauged students’ attitudes towards the learning environment, effectiveness of feedback, effectiveness of collaborative learning, the effects on self-confidence, and students’ love of learning. A diary of students’ interviews was kept in order to provide some qualitative, narrative and descriptive data on the study. It is important to point out that there was considerable overlap in the instructional approaches in the two treatments. Students in both the cognitive apprenticeship and traditional groups were given the same set of notes and exercises on JAVA programming. Both groups were exposed to elements of the cognitive apprenticeship based approach such as feedback and coaching. In both groups, the teacher modelled computer programming theory and JAVA programming concepts using worked examples and reallife examples. A main difference between the two groups was in the provision of feedback to the students. In the control group, as was characteristic of didactic instruction, feedback was

58


student initiated. With the experimental group, feedback was more structured and was given on a weekly basis. Feedback was provided by an online system where the lecturer provided individualised feedback via individualised emails sent to and received from each student. On a weekly basis, the students were expected to send an email to the lecturer, answering several questions. The first question required them to describe what activities and topics they had done during the week, and to indicate how they felt about their progress. The second and third question required them to describe any areas they are having problems with. The last question required the student to reflect upon what they have learnt and how useful they thought what they had learnt would be to them. The lecturer would then respond to each student via email by way of feedback and encouragement. From the individualised feedback, the lecturer could gauge areas most students were having problems with and then use their information to post some frequently asked questions (FAQs) and solutions on the class web-site, providing further feedback and guidance to students in the class. It was assumed that this feedback system would provide students with the opportunity to articulate their thoughts and ideas and also to reflect upon their work and their progress in class. A second differentiating factor between the two groups was metacognition. A key component of the cognitive apprenticeship based approach was the provision of a rich metacognitive experience to the learner, thus facilitating them to learn. This was facilitated within the cognitive group by encouraging students to reflect on their progress, problems encountered, what they had learnt, the usefulness of what they had learnt and also by the articulation of their thinking processes in the form of “think-alouds”. The third differentiating factor between the two groups was the incorporation of elements of collaborative learning. Within the cognitive apprenticeship based group, coaching and mediation would also be provided by a more capable peer as the students were be paired, with the more capable student collaborating with the weaker student in carrying out their programming tasks in class. 3 Results and Discussion 3.1 Achievement Test Scores The two treatment groups were found to be similar on non-Java prior test scores from earlier completed course units but diverged on the Java post-test scores. Results showed that there was an effect for CABLE treatment on post-test scores. That is, people who participated in CABLE scored more highly on the Java post-test than those who were in the traditional treatment. Statistical ANOVA procedures were used and showed a significant overall effect for treatment, F (1,53) = 8.48, p = .005. For further investigation, the two treatment groups were then split into two groups on the basis of the prior test scores. The prior test scores were taken as a measure of ability level. The median value of 50 was used, to generate a classification of high-ability and low-ability students. From an inspection of the data from the two ability levels separately, it was apparent that although the CABLE treatment had benefited both ability groups, the effect was more prominent in the less able group. The treatment effect was stronger in the less able group, with the F ratio increasing to 9.13 (p =.006). Inspection of effect-sizes (Cohen’s procedure) yielded d = 1.22 for low-ability and d = 0.66 for the more able group [The coefficient d is expressed in standard deviation units]. In other words, although the CABLE treatment benefited both ability groups, the effect was stronger for the less able group, as indexed upon through their prior achievement scores. This interaction effect is shown in Figure 1


59

18

16

Java Posttest Means

14

12

TREAT

10

Trad 8

CABLE Low Ability

High Ability

ᴾ

Figure 1. Mean scores on post-test for students of low and high ability

3.2 Questionnaire responses A measure of general course affect (or ‘liking’) was generated through summing the responses to 11 items on the questionnaire. It was found that on this measure, the two treatment groups exhibited similar levels. Out of a possible score of 44, the actual mean was 34.1 (SD of 3.96), which indicated a very high level of course approval (Note: the natural midpoint on the scale was 27.5). In short, students from both treatment groups evidenced very high levels of liking for their course experience. The levels of course affect was found to be independent of either treatment mode or ability status. ᴾ

3.2.1 Responses to the Unstructured questions Responses to Question xviii: Probe: Problems encountered (if any) during treatment

For the traditional treatment, 6 out of 24 students reported having no problems in using this learning mode. The main problem students identified was the difficulty in understanding Java programming (12 out of 24 students). Other problems reported included (a) not enough time to do work, (b) difficulty in accessing the lecturer, and (c) difficulty in specific Java concepts such as declaring types, running the program. In the CABLE treatment, 4 out of 24 students reported having no problems with the learning environment. As in the traditional group the main problems students reported were (a) difficulty in understanding Java, and (b) too many Java terms to learn. Responses to Question xix Probe: List reasons why you think this is an effective or ineffective form of learning environment

Fifteen out of 24 students in the traditional treatment agreed it was an effective learning mode. The following reasons were given: (a) it kept students alert, occupied and provided experience of working under pressure, (b) it prepared students for programming independent of the lecturer, (c) improved student understanding of Java, (d) the provision

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of a balance of both practical and theory improved understanding of Java, (e) provided encouragement to learn, and (f) provided more computing knowledge and skills. Five students found the learning environment ineffective. One claimed it was because it was the first time they had studied computers, while another resented disruptions from other students. For the CABLE treatment, 15 of 24 students reported the learning mode as effective. The main reasons given were (a) the use of both practicals and lectures facilitated a better understanding of Java, (b) increased motivation and improved confidence in learning Java, (c) improved understanding of Java and, (d) the use of email. Two students found the learning environment ineffective, one of them giving the reason as insufficient coverage time for topics. 3.2.2 Effectiveness of Online learning To gauge the effectiveness of online learning, three data sources were used, (a) the responses to the questionnaire questions 12 – 17 administered only during the cognitive apprenticeship based treatment, (b) responses from personal student interviews, conducted in weeks 6 and weeks 12 of the study, and (c) an analysis of products, processes and perceptions based on the Triple Framework approach for evaluating online environments [9]. Questionnaire questions and interviews with students focused on four main issues: (a) The effectiveness of email for feedback (b) The effectiveness of online notes on class website (c) The usefulness of posting sample test solutions online (d) Whether students like working in pairs (a) The effectiveness of email for feedback The majority of the students interviewed agreed on the usefulness and effectiveness of email as a means of feedback. The main source of frustration was technical problems preventing effective access to email. Most of the students regarded email as very helpful as it gave useful and immediate feedback. (b) The effectiveness of online notes on class website All of the students interviewed liked online notes, giving the main reason for liking, as the ease of access and also that they could access the online notes at any time. Students also found the interactive self tests useful as a means of reviewing before the Java test. Again, the main complaints were technical. (c) The usefulness of posting sample test solutions online All of the students interviewed liked the idea of sample solutions online as they said they could i) access them any time, ii) useful for revision and iii) were useful for doing test corrections. (d) Whether students liked working in pairs All of the interviewees liked working in groups as they could help each other sharing ideas, especially when there were some things that the other group members knew, that they had no knowledge of. Another important source of data was the log of student email. According to Salomon as cited in [9], there are 5 stages in the development of an online learning


61

community. The first stage is when community members develop the motivation to access and use the web environment proficiently. In the second stage, members are able to establish online identities and take the initiative to socialise with others online. Stage three is characterised by participants initiating the process of assisting and providing mutual support in information exchange. The process develops to stage four when course related group discussions eventuate and there is increased collaboration amongst members of the online community as they devise various means of collaborating in online work. Finally, the last stage, stage five, is characterised by members of the online community showing how the online learning has facilitated the achievement of personal goals and an ability to reflect on the learning process. Inspection of the log of student emails indicated that from the perceptions, processes and products of online learning using the Triple P Framework as developed by Ryba, Selby and Mentis [9], showed that the online community in Project 2, had progressed to stage three of the five stage model where students were involved in information exchange using email and the discussion forum. In terms of processes, the students were not only proficient in using the online environment, but were also using the online environment for receiving coaching, feedback and scaffolding. Students were also using the email facility for online discussion forums and for a few of them, the ability to use it for reflection on their work. In terms of products, student participation in email included: x Technical messages relating to the website or managing of the helpdesk. x Questions related to course work Lecturer participation was in the form of providing encouragement to the students, providing feedback on student queries and bringing to the notice of students valuable features of the online environment. Hence the effectiveness of the online learning environment is suggested from student interview responses, an examination of the perceptions of students, an analysis of the content of student email messages and the processes students engaged in.

4 Conclusion In essence it was found that: x CABLE, as a viable model of conceiving and delivering a high quality instructional aid system, receives a measure of strong positive support from the present results. The results showed that on the overall, students were advantaged through their participation within this program. However, at the group level, significant achievement effects for the CABLE treatment were more prominent in the case of students in the less able group as indexed on prior achievement scores. x In terms of positive attitudes towards the learning environment, results of the study indicate that, all the participants showed strong positive feelings towards their allocated treatment. x There is positive evidence for the effectiveness of online learning in all of the student interview and questionnaire responses and also from the analysis of email content and the processes students were engaged in. x The results indicate the effectiveness of CABLE as a viable instructional model for teaching Computer programming. However the results of the two studies differed in terms of which groups had benefited. In our initial study the high-ability students were clearly advantaged by CABLE. However in the current study, the low-ability students appeared to benefit slightly more than the high-ability students, although both ability groups showed clear advantages in the CABLE. Why might two similar studies yield

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slightly different findings? The solution we favour is that our CABLE model continues undergoing development firstly in terms of improvement and familiarity with procedures. Also in the second study the lecturing team placed relatively greater emphasis upon individualised feedback. It can be noted that the two studies differed slightly in the composition of the participant groups. In the first study, all the participants were from the Foundation program. These were generally the more able students, entering with high GPA’s. In the second study, 64% of the participants were from the Foundation program. However there were also 36% from the non-Foundation program, generally entering with low GPA’s. Despite the difference in ability levels of these two groups, there was no interaction between program and treatment, as CABLE had a positive effect on achievement levels of participants from both programs. Hence, the results of the present study have boosted the confidence of our team in the viability of CABLE. It has also confirmed that the provision of excellent instructional procedures in a richly motivating and responsive educational context can have appeal and positive effects on students who may not otherwise perform to a high level. In this respect CABLE can enable us to achieve the ultimate goal of reducing variance within student achievement levels. This is encouraging, especially within the context of teaching computer programming as the subject area is very challenging and cognitively demanding. Hence an effective instructional model would certainly improve the quality of instruction of such an inherently demanding subject and ultimately result in improved achievement levels of Computer programming students within the university.

References [1] Astrachan, O., Selby, T., & Unger, J. (1996). An object-oriented, apprenticeship approach to data structures using simulation: Proceedings of FIE '96, Frontiers in Education (pp. 130 – 134). Retrieved June 25th 2004 from [http://www.cs.duke.edu/~ola/papers/fie96.html]. [2] Garner, S. (2000). Cognitive load reduction in problem solving domains, Perth, Australia: Edith Cowan University. [3] AECT. (2001). The handbook of research for educational communications and technology. Retrieved June 12, 2003 from http://www.aect.org/intranet/publications/edtech/24/24-05.html [4] Au, W. K. (1992). Logo programming: Instructional methods and problem-solving. Unpublished doctoral dissertation. Palmerston North, New Zealand: Massey University. [5] Blair, A., & Hume, T. (1994). An exploration of the application of constructive learning techniques to software development using object orientation as a Vehicle. Paper presented at CTI Annual Conference, 1994. Retrieved March 12, 2003, from http://www.ulst.ac.uk/cticomp/therhume.html [6] Collins, A. (1989). Cognitive apprenticeship and instructional technology (Technical Report No. 474). BBN Laboratories, Cambridge, MA: Centre for the Study of Reading, University of Illinois. [7] Cooper, J., Prescott. S, Cook, L, Smith., L, Mueck, R., & Cuseo, J. (1990). Cooperative learning and college instruction: Effective use of student learning teams, California State University Foundation, Long Beach. CA. [8] Levin, J., & Waugh, M. (1998). Teaching tele-apprenticeships: Electronic network-based educational frameworks for improving teacher education. Journal of Interactive Learning Environments, 6(1-2), 39-58. [9] Ryba, K., Selby, L., & Mentis, M. (2001). Analysing the effectiveness of on-line learning found in communities. [Electronic version]. Retrieved September 25, 2003, from http:// www.ecu.edu.au/conferences/herdsa/main/papers/nonref/pdf/KenRyba.pdf.


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Problem Solving Process Oriented Diagnosis in Logic Programming Nguyen-Thinh Le, Wolfgang Menzel Department of Informatics, University of Hamburg, Germany {le,menzel}@informatik.uni-hamburg.de Abstract. In this paper, we present the evaluation result of our constraint-based tutoring system for logic programming from which we derive the conclusion that students need diagnostic information and remedial hints corresponding to the stage of the problem solving process where they are stuck. For this reason, we propose a three steps diagnosis approach which consists of: diagnosis at the task analysis stage, diagnosis at the solution design stage and diagnosis at the implementation stage. Our diagnosis approach should not only help students learn logic programming, but also master the skills of task analysis and solution design. Keywords: Constraint-based modeling, cognitive diagnosis, tutoring systems, logic programming, problem-solving process, instructional process.

Introduction Error diagnosis plays an important role in an Intelligent Tutoring System (ITS) because diagnostic information is essential for modelling the state of student’s knowledge and for initiating appropriate instructional actions. Currently, several programming tutoring systems apply a rather simple diagnostic approach by presenting a problem to the student and providing the possibility to submit a solution by choosing from several options or filling in a template. Diagnostic approaches supported by this type of solution submission might be used in tutoring systems aiming at helping students to become familiar with basic concepts of a programming language. In problem solving, however, students often make errors because they have difficulties with task analysis or solution design. Thus, at that point, diagnostic information about semantic or syntactic errors is not relevant. Rather it is important to consider the stage of the problem solving process, where the student becomes stuck: task analysis, solution design or implementation. We have developed a web-based tutoring system for logic programming applying the constraint-based modeling (CBM) approach. In this paper, first, we introduce the constraint-based approach briefly. In the second section, we present the result of the preliminary evaluation of our system. From the analysis of the evaluation result, we claim that diagnostic information is only useful for students if it matches the stage of the problem solving process where the student has difficulties. In the third section we outline related work. Our diagnosis approach is introduced in the fourth section. The current state and further directions of our research are summarized in the last section. 1. Constraint-based Modeling The CBM approach proposed in [1] can be applied to model general principles of a domain as a set of constraints. A constraint is represented as an ordered pair consisting of

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N.-T. Le and W. Menzel / Problem Solving Process Oriented Diagnosis in Logic Programming

a relevance part and a satisfaction part: Constraint C =

The relevance part represents circumstances under which the constraint applies, and the satisfaction part represents a condition that needs to be fulfilled for the constraint to be satisfied. Constraints can be used to describe facts, principles or conditions which must hold for every solution contributed by the student. In addition, constraints can also be used to specify requirements of a task. Using the relevance part, constraints can be tailored according to an ideal solution, which represents the requirements of the given task. Requirements, which have to be satisfied in that specific situation, can be specified in the satisfaction part. More about the application of the CBM approach to model problem solving in logic programming can be found in [3, 4]. A constraint is evaluated by matching its relevance part to the solution. If the matching is successful then the solution should also fulfill the satisfaction part. Otherwise, the solution is considered to be incorrect with respect to the constraint that has been evaluated. If a constraint is violated, it indicates that the student solution does not obey principles of the domain or does not meet the requirements of the given task. We developed a tutoring system (INCOM) [3] for logic programming applying the CBM technique. The diagnosis approach of our current system consists of two steps. It starts by hypothesising the Prolog pattern the student solution is based on. A Prolog pattern represents a solution strategy for a programming problem [4]. For a given programming problem, there are usually several appropriate patterns which can be applied to solve it. The pattern selection is carried out heuristically. The second step of the diagnosis examines whether the student solution satisfies the task requirements. In the evaluated version, we use constraints to model task requirements. If a constraint is violated, a programming technique has been applied incorrectly or a task requirement is not fulfilled. 2. Evaluation and the Problem of Remedial Hints 2.1 Evaluation results We have conducted a preliminary evaluation for INCOM during the winter term 2004/05 at the University of Hamburg. We provided students with four exercise assignments: 1. Define a predicate which specifies the relationship between a list and its prefix. 2. Write a function to convert Peano numbers to integer numbers. 3. Write a predicate which defines an even Peano number. 4. Write a function to compute the sum of compound interest for a given amount, an interest rate and a duration in years. Students have been requested to consult our system via a web interface when experiencing difficulties in solving those four exercise assignments. On our server machine, we registered 261 log files created by 99 users. Table 1 number of false and correct trials for each task trials for a correct solution task solved

Task

trials/user

task not solved

1

6.07

4.33

11

7

2

6.21

6.54

22

23

3

5.72

6.83

27

17

4

6.21

74.5

1

24

The first goal of our evaluation was to find out which task is challenging for students. Table 1 provides the results of problem solving for each task. The 2nd and 3rd


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columns show, how many trials in average a user carried out, and how many trials he/she needed to reach a correct solution. The last two columns tell us, how many log files contained correct solutions and how many did not. From the last two columns we can identify that most students could solve Task 1 and 3. For Task 2, the numbers of successful and unsuccessful attempts are almost the same. We also noticed that most users could not solve task 4. Analyzing the log files, we found two main reasons for these frequent failures: 1. Task 4 is more complicated than the other tasks. It includes the concept of recursion, arithmetic expressions, and arithmetic computation; 2. Many users were not able to derive a correct formula for the computation of a compound interest. The second goal of our evaluation was to identify the problems of students and where they are usually stuck. By investigating the log files, we recognized that most errors have been detected by the first step of the diagnosis - the pattern identification process. Most users had one of the following problems: • Users were not familiar with the data structure of Peano numbers. Some of them simply input “Peano” as arguments and expected that to be a Peano number. • The arithmetic evaluation mechanism in Prolog poses considerable problems for many users. Some of them placed an arithmetic expression at an argument position and expected a functional evaluation. Others used “=” instead of “is” for arithmetic evaluation, as it is common in mathematical notations. • Users called auxiliary predicates without defining them in the hope that they are builtin predicates. Or, they used arbitrary material at an argument or subgoal position and expected that the system is able to provide helpful hints. Through errors detected during the second step of the diagnosis - the constraint evaluation process, we noticed that users had the following problems: • Many users applied arithmetic expressions without making sure that the arguments are sufficiently instantiated. Sometimes, they transposed the positions of operands and result arguments or used operands not correctly. • Instead of decomposing an input argument, many novice programmers composed it in recursive subgoals. Or, they decomposed an input argument and processed it, but then, they did not know how to return the result of the processed input value. This indicates that Prolog novices are not familiar with composition and decomposition. The third goal of our evaluation was to determine the efficacy of the system. Our system provides three levels of feedback. If the system detects an error in the user’s solution, first, it notifies that the solution is incorrect; second, upon request, it shows the problem location, gives an explanation and third, it provides suggestions to remove the error. We evaluate the efficacy of the system by determining if errors disappeared after users have seen the error location or a remedial hint. A remedial hint includes an error explanation and a correction proposal. In 75.6 % of a total of 632 false trials, students were interested in system feedback. In 60.5% of 478 feedback requests, after seeing the error location, they requested more detailed error explanation and remedial hints. That indicates that most students are interested in receiving feedback from the system in order to improve their solutions. In 68.3% of cases after seeing the error location without requesting remedial hint, users were able to remove the error. In 75.8% of cases after requesting remedial hints, the error was eliminated. As expected, the efficacy of remedial hints is higher than that of error location because remedial hints give more information. In general the system is helpful for students. However, the efficacy of our current system does not satisfy our ambitions. We investigated the log files to trace back how students have corrected their solutions after having read the feedback. We noticed that students could not correct their solutions according to some remedial hints provided by our system. This can be attributed 1) to the

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incoherence of the remedial hints which are specified for isolated constraints [5, 6] and 2) to a mismatch between the feedback and the stage of the problem solving process where the student’s difficulty occurred. In this paper, we mainly address the second problem. 2.2 The problem of solution remediation We illustrate the problem of providing students with appropriate feedback with the following example. The third exercise assignment requests students to define a predicate which specifies the relationship between a list and its prefix. Our system expects a correct solution like IP1 or IP2 which uses an auxiliary predicate append. Task: define a predicate to examine the relationship between a list and its prefix Solution IP1: prefix([], _). prefix([H|R], [H|T]):-prefix(R,T).

Solution IP2: IP2: prefix(L1, L2):-append(L1, Rest, L2).

A student submitted the following solution SP1 for the task above. Our system hypothesizes that the student decided to apply the strategy IP1, it then evaluates the relevant constraints and returns the corresponding diagnostic information. Student solution SP1:

prefix(List, List). prefix([], List).

Remedial hints: Error1: a base case in your solution is superfluous. Error2: a recursive case in your solution is missing.

The solution indicates that the student is in a position to specify a base case “prefix([], List).” but not able to specify a recursive case. Perhaps the student wanted to specify the type restriction for the argument positions by giving the clause “prefix(List, List)”. More likely, however, it is that the student does not know how to specify a list data structure which is required for both predicate arguments. That means he/she is not able to fully analyze the task and to specify the arguments correctly. Therefore, remedial hints concerning solution design are not helpful for the author of the solution above. In this case, we need to help the student analyze the task requirements. The task analysis includes questions like: Which information should be represented as an argument? What kind of data structures should be specified for predicate arguments? Which mode should an argument have? The following student’s solution SP2 indicates that he/she has succeeded with the task analysis, but is now struggling with designing a solution for the given task. Student solution SP2:

prefix([X], [X]). prefix(L, [X|Rest]):- append(H, Rest, [X|Rest]),prefix(H, Rest).

Remedial hints: Error1: the subgoal prefix(H, Rest) is superfluous. Error2: the clause prefix([X], [X]) is superfluous. Error3: L should be unified with H. Error4: the argument [X|Rest] in the head of 2nd clause should be represented as a variable.

The system hypothesizes that the student was following strategy IP2 and evaluates the corresponding constraints. While the first two feedback messages concern the solution design corresponding to the strategy IP2, the last two consider the erroneous implementation of arguments. This might have caused the student to be confused because she/he is currently having problems with designing the solution, not with the


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implementation. At this stage, the student has to deal with the questions: what kind of clauses and subgoals are required to construct a solution according to the intended design strategy, and how these clauses and subgoals have to be arranged? Table 2 tells us the proportion of false attempts due to errors in task analysis and in solution design. The second and the third columns indicate the absolute number of students’ attempts to solve the tasks. We notice that students made most errors (70%) at the stage of analyzing Task 3. That means, they were not able to specify a Peano number correctly. We also see that students had difficulties with designing solutions for Task 4. 42% of their attempts for Task 4 were not successful at finding appropriate clauses or subgoals. Table 2 Proportion of false attempts due to errors in task analysis and in solution design. Task Total attempts False attempts Errors in task analysis Errors in solution design 1

91

70

7%

20%

2

242

205

25%

18%

3

246

210

70%

17%

4

149

147

9%

42%

From the evaluation result and the analysis of student’s programs above, we can derive the need for a diagnosis approach which is able to provide diagnostic information corresponding to the stage of the problem solving process. 3. Related works Available tutoring systems are able to detect semantic or syntactic errors in a program. However, this kind of diagnostic information is not useful for students who already have difficulties in the early phases of problem solving. The problem is to determine which level of understanding the student has and how to guide him/her to correct his/her solution in a way he/she is supposed to do. Various attempts have been developed in this direction, but none of them is really able to provide diagnostic information tailored to the stage where the difficulties occurred. The Pascal tutoring system [7] is able to infer the student’s intention and to diagnose errors by mapping a student program to programming plans. This system focuses the diagnosis mainly on the solution design applying programming plans and misses the diagnosis at the task analysis stage. A model-tracing tutor [8, 9] follows the student’s intention by forcing the student to act as an expert would do. Hence, a model-tracing tutor always pretends to know the student’s intention. However, model tracing does not guarantee that student errors can always be corrected. When a student performs an act, which is neither on a correct path nor on a anticipated incorrect one, model tracing has nothing to say other than that is probably incorrect [10]. ELM-PE provides a syntax-based structure editor, which guides the student filling in appropriate insertions into predefined LISP statement slots, such that only valid LISP expressions may be constructed [11]. The diagnostic approaches mentioned restrict students’ creativity and do not support them to improve the problem solving skill. Some other approaches introduce different abstraction levels of errors made by students. The approach in [12] represents student’s actions and errors in terms of knowledge applied in a learning context. Two levels of knowledge are differentiated. The micro-level contains elements describing problems, operators, and control structures and the macro-level describes conceptions. The micro-level represents the way a conception may be revealed by a student, whereas the macro-level represents conceptions in terms of knowledge. The diagnosis approach is driven by taking into account student’s actions related to a particular task and the system provides explanations on the student’s reasoning

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by recognizing sub-jacent knowledge. According to [12], an environment that intends to provide personalized feedback must be able to interpret student’s actions in terms of knowledge. The approach in [13] distinguishes the surface level student model from a deeper level student model. The former one represents the scheduled problem solving plans and applied procedural knowledge. The authors of [13] argue that just diagnosing problem solving knowledge applied by the student is not sufficient, because the sequence of the procedures the student has used may reflect his or her belief in the domain axioms. Therefore, it is necessary to build a deeper level student model which consists of diagnostic hypotheses explaining the procedural operations of a student in terms of the domain axioms. Both approaches [12, 13] introduce different levels of knowledge which can be inferred from the student’s input. However, they do not provide diagnostic information along the process of problem solving. We propose a diagnostic approach which not only enables students to input a solution for a given task in free form. It also supports the students at all three stages of the problem solving process1: task analysis, solution design and implementation. 4. Three Steps Diagnosis in Logic Programming 4.1 Diagnosis at the task analysis stage To create a logic program, first, it is necessary to know how many arguments are required to solve the given task. Normally the number of required arguments can be inferred from the task specification. The student should be able to understand the functionality of every argument which is used to define a predicate. If an argument does not have any function, it is considered to be superfluous. If information from the given task has not been modeled as an argument in the predicate definition, then the student has missed a necessary argument to solve the given task. The second step is to determine the argument modes. In logic programming, an argument can have input mode, output mode or both. Students are requested to specify a mode for each argument of the predicate to be defined according to the given task. The last step of task analysis is to define appropriate data structures for the argument positions. A data structure for an argument in logic programming can be an atom, a number, a list or a special kind of term (i.e. Peano number). We request students to input the information for predicate declaration, i.e. argument list, argument mode and data structure, before they submit a complete solution. The system examines their inputs of the task analysis as the following example shows: Task description: please define a predicate which specifies the relationship between a list and its prefix. You can use the built-in predicate append if necessary. System: please, input the predicate name and the list of arguments with the appropriate modes. Student: prefix(?List1, -List2) System: Error location: 2nd argument; Explanation: wrong mode; Suggestion: check the task specification and choose the appropriate mode. Student: prefix(?List1, ?List2) System: The declaration for the predicate is correct.

1

Our term “problem solving process” differs from the definition in [13] where a sequence of operations applied by the student in solving a given problem is called a problem solving process.


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4.2 Diagnosis at the solution design stage Now, the student has analyzed the task correctly. The system allows the student to put in a solution. A template prefix(List1, List2) according to the predicate declaration is given to the student. After the student has submitted her/his solution, the system hypothesizes the design strategy of the student [4] and returns appropriate feedback. System: please, input your solution for the task above. Student: prefix([X],[X]).

prefix(L,[X|Rest]):-append(H,Rest,[X|Rest]),prefix(H,Rest).

System: we hypothesize that you apply the built-in predicate append to concatenate the first list with an arbitrary second list to get the third list. Please, consider the following feedback: Error1: Location: clause prefix([X],[X]) Explanation: this clause is superfluous. It is not necessary for this design strategy. Remedial hint: please remove it. Error2: Location: prefix(H,Rest) Explanation: this subgoal is superfluous. It is not required for this design strategy. Suggestion: please remove it. Student: prefix(L,[X|Rest]):-append(H,Rest,[X|Rest]).

4.3 Diagnosis at the solution implementation stage Once, the student’s solution conforms with the design strategy the system hypothesized, the system moves the diagnosis to the implementation level. This step deals with the questions: How is a necessary subgoal implemented? How are the arguments in the student’s solution implemented? Our system replies to the last student’s input in the example above as follows: System: please, consider the following feedback Error 1: Location: L, H Explanation: We need to process L. Therefore L and H have to be co-referenced. Remedial hint: write a subgoal L=H, or replace H by L, or replace L by H. Error 2:Location: [X|Rest] in the head of clause 1 Explanation: This argument position needs a variable which represents a list. Suggestion: choose a variable name. Error 3:Location: [X|Rest] in subgoal 1, clause 1 Explanation: This argument needs to represent the concatenation of two lists. Suggestion: choose a variable name instead using a list representation

Figure 1: The three steps of the diagnosis process

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A subgoal can be: unification, de/composition, calculation, an arithmetic test, binding, relation, recursion or user defined. An argument can be a variable, anonymous variable, list, atom (letter), number (float, int), Peano number, arithmetic expression or arbitrary term. For each subgoal, there are different implementation techniques. For example, to implement an arithmetic test subgoal “less then”, we have three possibilities: Y=Y or Y>X. The whole diagnosis process is illustrated by Figure 1. 5. Conclusion and Future Work We have presented the evaluation of our current tutoring system for logic programming. The diagnosis component of this system is developed applying the constraint-based modeling approach. The evaluation result indicated that students have not only difficulties on the on the implementation level, but also on the task analysis level (e.g. data structure for Peano numbers) and the solution design level (e.g. de/composition of lists). The evaluation result has shown that it is necessary to devise a diagnosis approach which is able to deliver diagnostic information corresponding to the stage of the problem solving process where the student is stuck. For this purpose, we have proposed a three step diagnosis approach: 1) diagnosis at the problem analysis stage, 2) diagnosis at the solution design stage and 3) diagnosis at the implementation stage. The three step diagnosis approach can serve several educational purposes. First, it helps students master analysis skills. Second, it supports students in solving programming problems by using design strategies and lastly, students become familiar with the semantics of logic programming. The diagnostic component of our system is under restructuring. We plan to launch and to evaluate a second version of our tutoring system during the winter term 2006/2007. References [1] Ohlsson, S. (1994). Constraint-based Student Modeling. In J. E. Greer, G.I. McCalla, Student Modelling: The Key to Indivi-dualized Knowledge-based Instruction, 167-189. Berlin. [2] Suraweera, P., Mitrovic, A., and Martin, B. (2005) A Knowledge Acquisition System for Constraintbased Intelligent Tutoring Systems. http://www.cosc.canterbury.ac.nz/tanja.mitrovic/SuraweeraAIED05.pdf [3] Le, N.T., and Menzel, W. (2005) Constraint-based Error Diagnosis in Logic Programming. In Proceedings of the 13th International Conference on Computers in Education. [4] Le, N.T. (2006) Using Prolog Design Patterns to Support Constraint-Based Error Diagnosis in Logic Programming. ITS workshop on Intelligent Tutoring Systems for Ill-Defined Domains. [5] Kodaganallur, V., Weitz, R.R., and Rosenthal, D. (2005) A Comparison of Model-Tracing and Constraintbased Intelligent Tutoring Paradigms. In International Journal of Artificial Intelligence in Education, Vol. 15, 117-144. [6] Menzel, W. (2006) Constraint-based Modeling and Ambiguity. In International Journal of Artificial Intelligence in Education, Vol. 16, Nr. 1. [7] Johnson, W. (1986) Intention-based Diagnosis of Novice Programming Errors. Morgan Kaufmann. [8] Anderson, J.R., and Reiser, B. (1985) The LISP Tutor. Byte, 10, 159-175. [9] Anderson, J.R., Conrad, F.G., and Corbett, A.T. (1989) Skill Acquisition and the LISP Tutor. Cognitive Science 13, 467-505. [10] Martin, B. (2001) Intelligent Tutoring Systems: The Practical Implementation of Constraint-based Modelling. PhD thesis, University of Canterbury. [11] Weber, G., and Möllenberg, A. (1995) ELM Programming Environment: a Tutoring System for LISP Beginners. In Cognition and Computer programming, 373-408. [12] Webber, C. (2004) From Errors to Conceptions – an Approach to Student Diagnosis. In Proceedings of the 7th International Conference on Intelligent Tutoring Systems. [13] Matsuda, N. and Okamoto, T. (1992) Student Model Diagnosis for Adaptive Instruction in ITS. In Proceedings of the 2nd International Conference on Intelligent Tutoring Systems., 467-474.


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Programming Teaching Support System Using Student Model KeunWoo Han, EunKyoung Lee, YoungJun Lee, Korea National University of Education, Korea [email protected] Abstract: This paper describes the CTi system that supports teachers in C programming courses. The CTi system provides teachers with student’s model, various learning information and inductive instruction tools. Teachers using the system can provide effective feedback to students. Bayesian Belief Networks are used to represent a student’s model and relationship between the structure of programming language and the student’s knowledge. An empirical study showed the CTi system had positive effects on students’ programming performance. Keywords: Programming education, student modeling, teaching support tool, inductive instructional method

1. Introduction Computer programming is a difficult skill for many students. New methods and techniques to help novices to learn programming are needed. Programming is difficult because of the abstract concepts. Students have problems in different issues related to program construction [6]. Therefore, it is necessary to support teaching and learning with various learning strategies such as project based learning [5], self-regulated learning [12], pair programming [2]. There were, however, few systems that support teachers to teach with various learning strategies. We developed a C programming teaching support system. It helps teachers to understand their students’ programming ability and support their teaching activities. We have used Bayesian Belief Networks to represent a student’s model and proposed a teaching support system using the inductive instructional method. Many teachers taught their class using the deductive instructional method. However, a programming language is similar to a secondary foreign language. It is better to provide students with various examples than to provide few examples. With various examples, students are not only able to experience with more the programming codes but also learn some new concepts. A good practice in computer programming teaching is to teach general concepts first and then progressively teaches more detailed grammars [9]. An inductive teaching is better for long-term retention and transference of concepts. The developed CTi system supports the inductive instructional strategy and provides various tools to support teachers. 2. The CTi System The CTi system is a C programming Teaching support system using the inductive instructional method. Students or teachers can connect to the system via a web browser. They can access many practice examples, tools and the student model on the web system. The system records all learning activities and is able to infer students’ knowledge about the

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K. Han et al. / Programming Teaching Support System Using Student Model

concepts of the programming language. 2.1 Teaching Support tools We need to consider the inductive learning strategy in programming teaching environment [7]. It is necessary to provide students with good example codes and references such as manual. It is, also, necessary to provide teachers with theirs students’ models. It helps them to give effective feedback to their students. In the following, we illustrate the tools that are used to support teaching in the inductive instruction environment. x Annotation: Annotation can provide learning information such as learning covered and assessment. The annotations are useful for effective navigation support [11]. A screenshot of the CTi system interface is shown Figure 1. Each subjects’ icons are presented the covered material in the class and the result of assessment. x Good examples: Good example codes are needed to learn a programming language [9]. Especially in the inductive learning environment, it is important to spend time in teaching students about how to program and code. The CTi system provides students with good examples to learn programming concepts (see in Figure 1). x Visualization: A teacher can present various codes in a programming course and can control the font size of an example code on the system. The CTi system offers the highlight function that enables teachers to emphasize some codes using colored characters. The various teaching support tools are shown in Figure 2. To show example codes and change the font size of the code, teachers can use Tool 1. x Dictionary: A dictionary tool can help students to organize their knowledge about the programming language. Students’ knowledge organization directly affects their programming learning [13]. Teachers or Students are able to search a grammar, keywords and other codes (see Tool 2 in Figure 2). The result of the search is shown in Figure 2 (see Window 1 in Figure 2). Tool 1 Tool 2 Annotation Window 1

Figure 1. The CTi interface

Figure 2. The support tools in the CTi

2.2 Bayesian Student Modeling In a programming course, students learn programming by a topic. Each topic requires some prerequisite concepts. This situation can be represented as a cause-effect relation [10]. This relationship shows prerequisite concepts and learning order. Students can gradually learn contents of each topic in the inductive instructional environment. We developed a dynamic Bayesian Network student model based on a Corbett and Anderson’ probability model [4] and Reye’s paper [10]. Each topic is represented as a node and each node has a probability value of a student’s knowledge about the topic.

K. Han et al. / Programming Teaching Support System Using Student Model

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We represented the relationship between a structure of programming language and students’ knowledge. Since the probability of a node affects the probability of other nodes, we used Dynamic Belief Networks. We implemented Bayesian Student Models for C programming course (BSMC), in which a student model is represented a Direct Acyclic Graph (DAG). As shown in Figure 3 and Figure 4, students can access their learning activities and models on the knowledge about the programming language. An internal reflective thinking is required in a programming course [1]. The student models provide students with opportunity to have internal reflective thinking.

Figure 3. BSMC DAG ScreenG

Figure 4. The Learning Information

3. The Study 3.1 Methodology To evaluate the effectiveness of the CTi system, we had done a study with 39 university students. The study was done in an introductory C programming course. The students are taught about condition statements using the inductive method. The study participants were divided into an experimental and a control group. In the experimental group, 19 students were taught a programming learning with the CTi system. In the control group, 20 students were taught without the CTi system. Before the study, students were given a pre-test. After two hour class, they took a post-test. Students in experimental group were asked to complete a short questionnaire about the CTi system. During the study, all students’ interaction with the system was logged.

3.2 Results An independent sample t-test was conducted to compare the pre-test scores of the two groups. The difference between the two scores was not statistically significant (t=-1.011, p=.319). This indicates that there were no significant differences between the two groups. To compare the learning gain scores of the two groups, an independent sample t-test was conducted. The t-test result revealed significant differences between the learning gain scores of an experimental group and that of a control group. As shown in Table 1, the experimental group had a statistically higher learning gain scores than the control group (p

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