Artificial Intelligence and Tutoring Systems
Computational and Cognitive Approaches to the Communication of Knowledge
Published on 12. May 2014
510 pages
978-1-4832-2111-3 (ISBN)
Description
Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge focuses on the cognitive approaches, methodologies, principles, and concepts involved in the communication of knowledge. The publication first elaborates on knowledge communication systems, basic issues, and tutorial dialogues. Concerns cover natural reasoning and tutorial dialogues, shift from local strategies to multiple mental models, domain knowledge, pedagogical knowledge, implicit versus explicit encoding of knowledge, knowledge communication, and practical and theoretical implications. The text then examines interactive simulations, existing CAI traditions, and learning environments. The manuscript elaborates on knowledge communication, didactics, and diagnosis. Topics include knowledge presentation and communication, pedagogical contexts, target levels of didactic operations, behavioral and epistemic diagnosis, and aspects of diagnostic experience. The publication is a dependable reference for researchers interested in the computational and cognitive approaches to the communication of knowledge.
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Etienne Wenger
Artificial Intelligence and Tutoring Systems
Computational and Cognitive Approaches to the Communication of Knowledge
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06/1987
Morgan Kaufmann
€63.27
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Content
Part I A First Glance: Introducing the Field Chapter 1 Knowledge Communication Systems 1.1 Implicit Versus Explicit Encoding Of Knowledge 1.2 Knowledge Communication 1.3 Practical and Theoretical Implications 1.4 An Interdisciplinary Enterprise Summary and Conclusion Bibliographical Notes Chapter 2 Basic Issues 2.1 Domain Knowledge: The Object of Communication 2.2 Student Model: The Recipient of Communication 2.3 Pedagogical Knowledge: The Skill of Communication 2.4 Interface: The Form of Communication Summary and Conclusion Bibliographical NotesPart II A Panorama: People, Ideas, and Systems Chapter 3 Tutorial Dialogues: From Semantic Nets to Mental Models 3.1 SCHOLAR: Launching a New Paradigm 3.2 Natural Reasoning and Tutorial Dialogues 3.3 WHY: The Socratic Method 3.4 From Local Strategies to Multiple Mental Models Summary and Conclusion Bibliographical Notes Chapter 4 SOPHIE: From Quantitative to Qualitative Simulation 4.1 Simulation: Dialogues and Learning Environments 4.2 Natural-Language Interface: Semantic Grammars 4.3 SOPHIE-I: Simulation-Based Inferences 4.4 SOPHIE-II: An Articulate Expert 4.5 SOPHIE-III: Humanlike Reasoning 4.6 Mental Models: Qualitative Reasoning Summary and Conclusion Bibliographical Notes Chapter 5 Interactive Simulations: Communicating Mental Models 5.1 STEAMER: Simulation and Abstraction 5.2 QUEST: Progressions of Qualitative Models Summary and Conclusion Bibliographical Notes Chapter 6 Existing CAI Traditions: Other Early Contributions 6.1 Early Attempts to Tailor Problem-Solving Experiences 6.2 Pedagogical Experiments: Teaching Expertise Summary and Conclusion Bibliographical Notes Chapter 7 Learning Environments: Coaching Ongoing Activities 7.1 LOGO: Knowledge Communication as Learning 7.2 WEST: Relevance and Memorability of Interventions 7.3 The Design of Learning Environments 7.4 WUSOR: Toward Learner-Oriented Models of Expertise 7.5 Architectures Organized Around Curricula Summary and Conclusion Bibliographical Notes Chapter 8 Bugs in Procedural Skills: The "Buggy Repair Step' Story 8.1 BUGGY: An Enumerative Theory of Bugs 8.2 DEBUGGY: A Diagnostic System 8.3 REPAIR Theory: A Generative Theory of Bugs 8.4 STEP Theory: A Learning Model of Bug Generation Summary and Conclusion Bibliographical Notes Chapter 9 More on Student Modeling: Toward Domainindependent Mechanisms 9.1 PSM/ACE: Interactive Diagnosis 9.2 LMS: Inferential Diagnosis with Rules and Mal-Rules 9.3 PIXIE: Generating Mal-Rules 9.4 UMFE: A Generic Modeling Subsystem Summary and Conclusion Bibliographical Notes Chapter 10 Bug Reconstruction: Beyond Libraries of Bugs 10.1 Extending Past Knowledge with General Operators 10.2 Syntactic Manipulations on Production Systems 10.3 ACM: Machine Learning Techniques for Diagnosis 10.4 Primitive Operators Versus Bugs Summary and Conclusion Bibliographical Notes Chapter 11 Problem Solving and Design: Diagnostic Plan Analysis 11.1 The FLOW Tutor: Structured Memory Organization 11.2 SPADE: Toward a Tutor Based on a Theory of Design 11.3 The MACSYMA ADVISOR: Plans and Beliefs 11.4 MENO: Debugging and Tutoring Summary and Conclusion Bibliographical Notes Chapter 12 GUIDON: The Epistemology of an Expert System 12.1 GUIDON: A Tutor on Top of MYCIN 12.2 NEOMYCIN: Reconfiguring the Expert Knowledge 12.
