[Studies in Fuzziness and Soft Computing] Innovative Teaching and Learning Volume 36 ||

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  • Innovative Teaching and Learning

  • Studies in Fuzziness and Soft Computing

    Editor-in-chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warsaw, Poland E-mail: kacprzyk@ibspan.waw.pl

    Vol. 3. A. Geyer-Schulz Fuzzy Rule-Based Expert Systems and Genetic Machine Learning, 2nd ed. 1996 ISBN 3-7908-0964-0

    Vol. 4. T. Onisawa and 1. Kacprzyk (Eds.) Reliability and Safety Analyses under Fuzziness, 1995 ISBN 3-7908-0837-7

    Vol. 5. P. Bosc and 1. Kacprzyk (Eds.) Fuzziness in Database Management Systems, 1995 ISBN 3-7908-0858-X

    Vol. 6. E. S. Lee and Q. Zhu Fuzzy and Evidence Reasoning, 1995 ISBN 3-7908-0880-6

    Vol. 7. B. A. Juliano and W. Bandler Tracing Chains-(if-Thought, 1996 ISBN 3-7908-0922-5

    Vol. 8. F. Herrera and 1. L. Verdegay (Eds.) Genetic Algorithms and Soft Computing, 1996 ISBN 3-7908-0956-X

    Vol. 9. M. Sato et al. Fuzzy Clustering Models and Applications, 1997, ISBN 3-7908-1026-6

    Vol. 10. L. C. Jain (Ed.) Soft Computing Techniques in Knowledge-based Intelligent Engineering Systems, 1997 ISBN 3-7908-1035-5

    Vol. II. W. Mielczarski (Ed.) Fuzzy Logic Techniques in Power Systems, 1998, ISBN 3-7908-1044-4

    Vol. 12. B. Bouchon-Meunier (Ed.) Aggregation and Fusion of Imperfect Information, 1998 ISBN 3-7908-1048-7

    Vol. 13. E. Orlowska (Ed.) Incomplete l'1formation: Rough Set Analysis, 1998 ISBN 3-7908-1049-5

    Vol. 14. E. Hisdal Logical Structures for Representation of Knowledge and Uncertainty, 1998 ISBN 3-7908-1056-8

    Vol. 15. G.1. Klir and M.1. Wierman Uncertainty-Based Information, 2nd ed. 1999 ISBN 3-7908-1242-0

    Vol. 16. D. Driankov and R. Palm (Eds.) Advances in Fuzzy Control, 1998 ISBN 3-7908-1090-8

    Vol. 17. L. Reznik, V. Dimitrov and J. Kacprzyk (Eds.) Fuzzy Systems Design, 1998 ISBN 3-7908-1118-1

    Vol. 18. L. Polkowski and A. Skowron (Eds.) Rough Sets in Knowledge Discovery I, 1998 ISBN 3-7908-1119-X

    Vol. 19. L. Polkowski and A. Skowron (Eds.) Rough Sets in Knowledge Discovery 2, 1998 ISBN 3-7908-1120-3

    Vol. 20. 1. N. Mordeson and P. S. Nair Fuzzy Mathematics, 1998 ISBN 3-7908-1121-1

    Vol. 21. L. C. Jain and T. Fukuda (Eds.) Soft Computing for Intelligent Robotic Systems, 1998 ISBN 3-7908-1147-5

    Vol. 22. J. Cardoso and H. Camargo (Eds.) Fuzziness in Petri Nets, 1999 ISBN 3-7908-1158-0

    Vol. 23. P. S. Szczepaniak (Ed.) Computational Intelligence and Applications, 1999 ISBN 3-7908-1161-0

    Vol. 24. E. Orlowska (Ed.) Logic at Work, 1999 ISBN 3-7908-1164-5

    continued on page 335

  • Lakhmi C. J ain (Editor)

    Innovative Teaching and Learning Knowledge-Based Paradigms

    With 121 Figures and 18 Tables

    Springer-Verlag Berlin Heidelberg GmbH

  • Professor Lakhmi C. Jain Director, KES Centre University of South Australia Adelaide Mawson Lakes South Australia 5095

    Cataloging-in-Publication Data applied for Die Deutsche Bibliothek - CIP-Einheitsaufnahme Innovative teaching and learning: knowledged-based paradigms / Lakhmi C. Jain.

    (Studies in Fuzziness and Soft Computing; Vol. 36)

    This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplica-tion of this publication or parts thereof is permitted only under the provisions of the German Copy-right Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer -Verlag Berlin Heidelberg GmbH . Violations a re liable for prosecution under the German Copyright Law.

    Springer-Verlag Berlin Heidelberg 2000 Originally published by Physic a-Verlag Heidelberg New York in 2000 Softcover reprint of the hardcover 1st edition 2000

    The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the rele-vant protecti ve laws and regulations and therefore free for general use.

    Hardcover Design: Erich Kirchner, Heidelberg

    ISBN 978-3-7908-2465-0 ISBN 978-3-7908-1868-0 (eBook)DOI 10.1007/978-3-7908-1868-0

  • Dedication

    This book is dedicated to all my students L.c. Jain

  • Preface

    The engineers and scientists of tomorrow require a valid image of science and its interactions with technology and society to enable them to take an active informed role in society. Today's educational institutions are presented with the challenge of exposing students to ever-widening domains. Not only do mathematical techniques need to be addressed, but also computing techniques, and environmental and management aspects.

    In the engineering field in particular, the rate of obsolescence is so high that curricula must be revised and updated much more frequently than ever before.

    Traditional teaching methods cannot cope with this challenge, and hence there is a need to develop more effective teaching and learning strategies.

    This book presents innovative teaching and learning techniques for the teaching of knowledge-based paradigms. The main knowledge-based intelligent paradigms are expert systems, artificial neural networks, fuzzy systems and evolutionary computing. Expert systems are designed to mimic the performance of biological systems. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimization applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge.

    The first chapter, by Tedman and Jain, presents an introduction to innovative teaching and learning. A valid image of the nature of the interaction between science, technology and society is presented.

    Chapter 2, by Lee and Liu, is on teaching and learning the AI modeling. Authors have presented their study into teaching tools to help learning and understanding the concepts of neural networks, fuzzy systems and genetic algorithms.

  • viii Preface

    Chapter 3, by Karr, Sunal and Smith, describes an innovative course developed and taught at The University of Alabama, U.S.A. for students attending the College of Education. This course presents an overview of artificial intelligence (AI) techniques including expert systems, fuzzy systems, neural networks, and genetic algorithms. Its goal is to provide future educators with enough information about the science of the twenty-first century to effectively educate and motivate, their future students.

    Chapter 4, by Vega-Riveros, presents the architecture of an intelligent tutoring system for a neural networks course. A new intelligent tutoring system architecture using collaborating agents is proposed.

    Chapter 5, by Devedzi6, focuses on teaching knowledge modeling. It presents a survey of knowledge modeling techniques that are taught at the School of Business Administration and the School of Electrical Engineering University of Belgrade, Yugoslavia. Theoretical and architectural concepts, design approaches, and research issues of various knowledge modeling techniques used in the class room are discussed.

    Chapter 6, by Devedzi6, Radovi6 and Jerini6, is devoted to innovative modeling techniques for intelligent tutoring systems. The inclusion of three modeling techniques in teaching environment are included.

    Chapter 7, by Fulcher, is concerned with a teaching course on artificial neural networks. A key component of this course is the use of artificial neural networks simulator to undertake laboratory assignments. The visualization of key neural network parameters via the simulator has been found to significantly aid the students' learning process.

    Chapter 8, by Hiyama, introduces an innovative education for fuzzy logic stabilization of electric power systems. Matlab/Simulink based transient stability simulation programs for multi-machine power systems are introduced. The programs are used to teach fuzzy logic stabilization of electric power systems as well in the development of generator controllers using fuzzy logic and neural networks.

  • Preface ix

    Chapter 9, by Goh and Amarasinghe, describes a neural network workbench for teaching and learning. The workbench permits to create, train and test various neural network algorithms. One unique feature of this workbench is the use of real time displays for tracking progress when training a neural network.

    The final chapter, by Higgins and Mansouri, outlines a coursework system for the automatic assessment of AI programs. The system usefully assesses students' work, improve learning, and allows the marking and assessment of students' progress while learning a particular programming language.

    This book will be useful to professors, researchers, scientists, practicing engineers and students who wish to develop successful learning and teaching tools for the teaching of knowledge-based paradigms.

    I wish to express my thanks to Berend Jan van der Zwaag and Ashlesha Jain, for their assistance in the preparation of the manuscript. I am grateful to the authors for their contributions. I also thank Professor Janusz Kacprzyk for the opportunity to publish this book, and the Springer-Verlag Company for their excellent editorial assistance.

    L.C. Jain, Australia

  • Contents

    Preface vii

    Chapter 1 An Introduction to Innovative Teaching and 1 Learning D. Tedman and L.C. Jain, Australia

    Chapter 2 Teaching and Learning the AI Modeling 31 R.S.T. Lee and J.N.K. Liu, Hong Kong

    Chapter 3 Artificial Intelligence Techniques for an 87 Interdisciplinary Science Course c.L. Karr, C. Sunal, and C. Smith, U.S.A.

    Chapter 4 On the Architecture of Intelligent Tutoring 105 Systems and Its Application to a Neural Networks Course J.P. Vega-Riveros, Colombia

    ChapterS Teaching Knowledge Modeling at the Graduate 135 Level - a Case Study V. Devedzi6, Yugoslavia

    Chapter 6 Innovative Modeling Techniques for Intelligent 189 Tutoring Systems V. Devedzi6, D. Radovi6 and L. Jerini6, Yugoslavia

    Chapter 7 Teaching Course on Artificial Neural Networks 235 J. Fulcher, Australia

    Chapter 8 Innovative Education for Fuzzy Logic 261 Stabilization of Electric Power Systems in a MatlablSimulink Environment T. Hiyama, Japan

  • xii Contents

    Chapter 9 A Neural Network Workbench for Teaching and 289 Learning W.L. Goh and S.K. Amarasinghe, Singapore

    Chapter 10 PRAM: A Courseware System for the Automatic 311 Assessment of AI Programs c.A. Higgins and F.Z. Mansouri, u.K.

    Index 331

  • CHAPTERl

    AN IN'"fRODUCTION TO INNOVATIVE TEACHING AND LEARNING

    D. Tedman Flexible Learning Centre

    University of South Australia Adelaide, Underdale, S.A. 5032

    Australia

    L.C. Jain Knowledge-Based Intelligent Engineering Systems Centre

    University of South Australia Adelaide, Mawson Lakes, S.A. 5095

    Australia

    This chapter presents an introduction to innovative teaching and learning and knowledge-based intelligent paradigms. The intrinsic nature of knowledge-based intelligent techniques involves an accommodation with the pervasive imprecision of the real world, with the human mind as the role model [1]. Thus there are two important issues that should be considered in the design of effective teaching and learning strategies in this area. The first is the need for careful consideration of the discussions over the years by eminent researchers in regard to the epistemology and thinking processes involved in science and technology, as an appropriate starting point for the design of innovative teaching strategies for knowledge-based intelligent techniques. Secondly, since an aim of education in science and technology is to prepare students for their lives in societies which are increasingly dependent upon technology, reflection upon the nature of science and technology is of great benefit for the design of curricula and learning strategies in knowledge-based intelligent techniques.

  • 2 D. Tedman and L.C. Jain

    The main knowledge-based intelligent paradigms include expert systems, artificial neural networks, fuzzy systems and evolutionary computing. Expert systems are designed to mimic the performance of biological systems. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimization applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge.

    1 Introduction

    The knowledge-based intelligent paradigms are those that are inspired by an understanding of information processing in biological systems. When this is the case the process will include an element of adaptive or evolutionary behavior similar to biological systems, and like the biological model there will be a high level of interconnection between distributed processing elements [2]-[7]. We have at our disposal the necessary hardware and software for building knowledge-based systems. A number ~ of Universities in the world have established teaching and research programs in this field. It is also important that we invent and introduce innovative teaching and learning practice in this important area. Effective learning about knowledge-based intelligent techniques requires the development of a wide range of well-developed thinking techniques in students to enable them to develop an understanding of areas such as fuzzy logic, neural networks and evolutionary computing.

    By developing a strong and coherent understanding of issues resulting from the interactions between Science, Technology and Society (STS) students would be empowered to take an active role in decision-making in regard to STS issues resulting from the use of knowledge-based intelligent techniques and similar technologies. University graduates would then be committed to ethical and social responsibility as professionals and citizens [8].

  • An Introduction to Innovative Teaching and Learning 3

    1.1 The Nature of Work in Science and Technology

    There is a need to present a revised view of science and technology that emphasizes the interaction between STS to university students. The STS view of science has been accepted gradually by scientists and educators, and a world-wide shift or reorientation towards the inclusion of STS objectives in science and technology courses has evolved. The impetus for the changing perception of science and reorientation of science and technology courses and curricula has been due to the writings of many scholars, e.g., see [9]-[12]. Their publications and theories about the nature and philosophy of science have changed understandings of the nature of science. Several decades later, these ideas are finding their way into education. The work of these and other eminent scholars provides an introduction to modem views of the nature and epistemology of science. Consideration of both changes in philosophical and epistemological models of science as well as the educational implications of this changing picture of science is a necessary foundation for the development of...

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