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Artificial Intelligence and Learning Environments

Cover Image Copyright Year: 1990
Author(s): Clancey, W.; Soloway, E.
Publisher: MIT Press
Content Type : Books & eBooks
Topics: Computing & Processing (Hardware/Software)
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Abstract

New perspectives and techniques are shaping the field of computer-aided instruction. These essays explore cognitively oriented empirical trials that use AI programming as a modeling methodology and that can provide valuable insight into a variety of learning problems. Drawing on work in cognitive theory, plan-based program recognition, qualitative reasoning, and cognitive models of learning and teaching, this exciting research covers a wide range of alternatives to tutoring dialogues.William J. Clancey is Senior Research Scientist at the Institute for Research on Learning, Palo Alto. Elliot Soloway is Associate Professor at the University of Michigan.Contents: Artificial Intelligence and Learning Environments, William J. Clancey, Elliot Soloway. Cognitive Modeling and Intelligence Tutoring, John R. Anderson, C. Franklin Boyle, Albert T. Corbett, Matthew W. Lewis. Understanding and Debugging Novice Programs, W. Lewis Johnson. Causal Model Progressions as a Foundation for Intelligent Learning Environments, Barbara Y. White and John R. Frederiksen.

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      Front Matter

      Clancey, W. ; Soloway, E.
      Artificial Intelligence and Learning Environments

      Page(s): i - v
      Copyright Year: 1990

      MIT Press eBook Chapters

      This chapter contains sections titled: Half-Title, Special Issues of Artificial Intelligence: An International Journal, Title, Copyright, Contents View full abstract»

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      Artificial Intelligence and Learning Environments: Preface

      Clancey, W. ; Soloway, E.
      Artificial Intelligence and Learning Environments

      Page(s): 1 - 6
      Copyright Year: 1990

      MIT Press eBook Chapters

      This chapter contains sections titled: Rationale for the Collection, Research Standards, Overview of the Articles, Research Trends of the 1980s, New Perspectives, References View full abstract»

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      Cognitive Modeling and Intelligent Tutoring

      Clancey, W. ; Soloway, E.
      Artificial Intelligence and Learning Environments

      Page(s): 7 - 49
      Copyright Year: 1990

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, The Cognitive Theory, Converting Theory to Tutoring: Model Tracing, Implementing the Model-Tracing Methodology, Conclusions, Appendix A, References View full abstract»

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      Understanding and Debugging Novice Programs

      Clancey, W. ; Soloway, E.
      Artificial Intelligence and Learning Environments

      Page(s): 51 - 97
      Copyright Year: 1990

      MIT Press eBook Chapters

      Accurate identification and explication of program bugs requires an understanding of the programmer's intentions. This paper describes a system called PROUST which performs intention-based diagnosis of errors in novice PASCAL programs. The technique used involves generating possible goal decompositions for the program, matching them against the program, and then proposing bugs and misconceptions to explain the mismatches, Empirical studies of PROUST's performance show that it achieves high performance in finding bugs in nontrivial student programs View full abstract»

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      Causal Model Progressions as a Foundation for Intelligent Learning Environments

      Clancey, W. ; Soloway, E.
      Artificial Intelligence and Learning Environments

      Page(s): 99 - 157
      Copyright Year: 1990

      MIT Press eBook Chapters

      AI research in qualitative modeling makes possible new approaches to teaching people about science and technology. We are exploring the implications of this work for the design of intelligent learning environments. The domain of application is electrical circuits, but the approach can be generalized to other subjects. Our prototype instructional system is based upon a progression of qualitative models of electrical circuit behavior. These models enable the system to simulate circuit behavior and to generate causal explanations. They also serve as target mental models for the learner. The model progression is used lo create problem sets that motivate successive refinements to the students' mental models. Acquisition of these models allows students, at all stages of learning, to solve interesting problems, such as circuit design and troubleshooting problems. The system enables students to employ different learning strategies and to manage their own learning. For instance, they can create and experiment with circuits, can attempt problems posed by the system, and can ask for feedback and coaching from the models. In pilot trials, the learning environment successfully taught novices to troubleshoot and to mentally simulate circuit behavior View full abstract»

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      Index

      Clancey, W. ; Soloway, E.
      Artificial Intelligence and Learning Environments

      Page(s): 159 - 162
      Copyright Year: 1990

      MIT Press eBook Chapters

      New perspectives and techniques are shaping the field of computer-aided instruction. These essays explore cognitively oriented empirical trials that use AI programming as a modeling methodology and that can provide valuable insight into a variety of learning problems. Drawing on work in cognitive theory, plan-based program recognition, qualitative reasoning, and cognitive models of learning and teaching, this exciting research covers a wide range of alternatives to tutoring dialogues.William J. Clancey is Senior Research Scientist at the Institute for Research on Learning, Palo Alto. Elliot Soloway is Associate Professor at the University of Michigan.Contents: Artificial Intelligence and Learning Environments, William J. Clancey, Elliot Soloway. Cognitive Modeling and Intelligence Tutoring, John R. Anderson, C. Franklin Boyle, Albert T. Corbett, Matthew W. Lewis. Understanding and Debugging Novice Programs, W. Lewis Johnson. Causal Model Progressions as a Foundation for Intelligent Learning Environments, Barbara Y. White and John R. Frederiksen. View full abstract»




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