Wenger Artificial Intelligence and Tutoring Systems

Computational and Cognitive Approaches to the Communication of Knowledge
1. Auflage 2014
ISBN: 978-1-4832-2111-3
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 510 Seiten, Web PDF

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.

Autoren/Hrsg.

Wenger, Etienne

Inhaltsverzeichnis

1;Front Cover;1
2;Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge;4
3;Copyright Page;5
4;Table of Contents;14
5;Foreword;8
6;Acknowledgments;12
7;Part I: A first glance:
introducing the field;28
7.1;Chapter 1. Knowledge communication systems;30
7.1.1;1.1 Implicit versus explicit encoding of knowledge;31
7.1.2;1.2 Knowledge communication;33
7.1.3;1.3 Practical and theoretical implications;34
7.1.4;1.4 An interdisciplinary enterprise;36
7.1.5;Summary and conclusion;38
7.1.6;Bibliographical notes;38
7.2;Chapter 2. Basic issues;40
7.2.1;2.1 Domain knowledge: the object of communication;41
7.2.2;2.2 Student model: the recipient of communication;43
7.2.3;2.3 Pedagogical knowledge: the skill of communication;46
7.2.4;2.4 Interface: the form of communication;48
7.2.5;Summary and conclusion;50
7.2.6;Bibliographical notes;50
8;Part II: A panorama:
people, ideas, and systems;54
8.1;Chapter 3. Tutorial dialogues: from semantic nets
to mental models;56
8.1.1;3.1 SCHOLAR: launching a new paradigm;57
8.1.2;3.2 Natural reasoning and tutorial dialogues;62
8.1.3;3.3 WHY: the Socratic method;66
8.1.4;3.4 From local strategies to multiple mental models;68
8.1.5;Summary and conclusion;75
8.1.6;Bibliographical notes;76
8.2;Chapter 4. SOPHIE:
from quantitativeto qualitative to qualitative simulation;78
8.2.1;4.1 Simulation: dialogues and learning environments;79
8.2.2;4.2 Natural-language interface: semantic grammars;82
8.2.3;4.3 SOPHIE-I: simulation-based inferences;86
8.2.4;4.4 SOPHIE-II: an articulate expert;89
8.2.5;4.5 SOPHIE-III: humanlike reasoning;91
8.2.6;4.6 Mental models: qualitative reasoning;97
8.2.7;Summary and conclusion;103
8.2.8;Bibliographical notes;104
8.3;Chapter 5. Interactive simulations: communicating
mental models;106
8.3.1;5.1 STEAMER: simulation and abstraction;107
8.3.2;5.2 QUEST: progressions of qualitative models;115
8.3.3;Summary and conclusion;124
8.3.4;Bibliographical notes;126
8.4;Chapter 6. Existing CAI traditions:
other early contributions;128
8.4.1;6.1 Early attempts to tailor problem-solving experiences;129
8.4.2;6.2 Pedagogical experiments: teaching expertise;138
8.4.3;Summary and conclusion;147
8.4.4;Bibliographical notes;148
8.5;Chapter 7. Learning environments:
coaching ongoing activities;150
8.5.1;7.1 LOGO: knowledge communication as learning;151
8.5.2;7.2 WEST: relevance and memorability of interventions;153
8.5.3;7.3 The design of learning environments;160
8.5.4;7.4 WUSOR: toward learner-oriented models of expertise;162
8.5.5;7.5 Architectures organized around curricula;171
8.5.6;Summary and conclusion;176
8.5.7;Bibliographical notes;178
8.6;Chapter 8. Bugs in procedural skills:
the 'buggy repair step' story;180
8.6.1;8.1 BUGGY: an enumerative theory of bugs;181
8.6.2;8.2 DEBUGGY: a diagnostic system;186
8.6.3;8.3 REPAIR theory: a generative theory of bugs;192
8.6.4;8.4 STEP theory: a learning model of bug generation;198
8.6.5;Summary and conclusion;209
8.6.6;Bibliographical notes;210
8.7;Chapter 9. More on student modeling: toward domain-independent
mechanisms;212
8.7.1;9.1 PSM/ACE: interactive diagnosis;213
8.7.2;9.2 LMS: inferential diagnosis with rules and mal-rules;217
8.7.3;9.3 PIXIE: generating mal-rules;221
8.7.4;9.4 UMFE: a generic modeling subsystem;225
8.7.5;Summary and conclusion;228
8.7.6;Bibliographical notes;229
8.8;Chapter 10. Bug reconstruction:
beyond libraries of bugs;232
8.8.1;10.1 Extending past knowledge with general operators;233
8.8.2;10.2 Syntactic manipulations on production systems;234
8.8.3;10,3 ACM: machine learning techniques for diagnosis;237
8.8.4;10.4 Primitive operators versus bugs;245
8.8.5;Summary and conclusion;247
8.8.6;Bibliographical notes;249
8.9;Chapter 11. Problem solving and design:
diagnostic plan analysis;250
8.9.1;11.1 The FLOW tutor: structured memory organization;251
8.9.2;11.2 SPADE: toward a tutor based on a theory of design;254
8.9.3;11.3 The MACSYMA ADVISOR: plans and beliefs;259
8.9.4;11.4 MENO: debugging and tutoring;263
8.9.5;Summary and conclusion;283
8.9.6;Bibliographical notes;284
8.10;Chapter 12. GUIDON: the epistemology
of an expert system;288
8.10.1;12.1 GUIDON: a tutor on top of MYCIN;289
8.10.2;12.2 NEOMYCIN: reconfiguring the expert knowledge;298
8.10.3;12.3 GUIDON2: tutoring systems for classification tasks;305
8.10.4;Summary and conclusion;313
8.10.5;Bibliographical notes;314
8.11;Chapter 13. ACTP: a marriage with
cognitive psychology;316
8.11.1;13.1 Cognitive theories and pedagogy;317
8.11.2;13.2 Tutoring systems based on theoretical principles;319
8.11.3;13.3 Field evaluation of ACTP's tutors;326
8.11.4;13.4 Skill acquisition and expertise;329
8.11.5;Summary and conclusion;331
8.11.6;Bibliographical notes;333
9;Part III: A synopsis: the state of the
art;334
9.1;Chapter 14. The computer;338
9.1.1;14.1 Representational mappings: epistemic fidelity;339
9.1.2;14.2 Internal representation: the power of process models;341
9.1.3;14.3 External representation: the power of interfaces;343
9.1.4;14.4 Process model and interface: reification;344
9.1.5;14.5 Representational commitments;348
9.1.6;Bibliographical notes;349
9.2;Chapter 15. The domain;352
9.2.1;15.1 Communicability in epistemic terms;353
9.2.2;15.2 Compiled knowledge;355
9.2.3;15.3 Conceptual articulation: contents;357
9.2.4;15.4 Epistemological articulation: organization;362
9.2.5;15.5 The paradox of articulation;367
9.2.6;15.6 Articulation versus compilation;368
9.3;Chapter 16. The student;372
9.3.1;16.1 Scope of expertise;373
9.3.2;16.2 Incorrect knowledge;374
9.3.3;16.3 Viewpoints;382
9.3.4;16.4 Knowledge states;388
9.4;Chapter 17. Diagnosis;394
9.4.1;17.1 Behavioral diagnosis;398
9.4.2;17.2 Epistemic diagnosis;403
9.4.3;17.3 Noise: sources and solutions;410
9.4.4;17.4 Sources of evidence: diagnostic data;415
9.4.5;17.5 Aspects of diagnostic expertise;420
9.5;Chapter 18. Didactics;422
9.5.1;18.1 Didactic operations: plans of action;423
9.5.2;18.2 Pedagogical contexts;425
9.5.3;18.3 Decision base: constraints and resources;427
9.5.4;18.4 Target levels of didactic operations;435
9.5.5;18.5 Aspects of didactic expertise;440
9.5.6;Bibliographical notes;442
9.6;Chapter 19. Knowledge
communication;444
9.6.1;19.1 The components of knowledge communication;445
9.6.2;19.2 Knowledge presentation;450
9.6.3;19.3 Knowledge communication;452
9.7;Chapter 20. Epilogue;454
9.7.1;20,1 Direct outcome: useful systems;455
9.7.2;20.2 Indirect outcome: articulation of expertise;455
9.7.3;20.3 Related research: motivation and validation;457
9.7.4;20.4 Research topic: knowledge and communication;457
10;Bibliography;460
11;Figure Credits;488
12;Author Index;490
13;Subject Index;494