Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge

✍ Scribed by Wenger, Etienne

• Publisher: Elsevier Science
• Year: 2015
• Tongue: English
• Leaves: 513
• Category: Library

✦ Synopsis

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 t.;Front Cover; Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge; Copyright Page; Table of Contents; Foreword; Acknowledgments; 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.

✦ Table of Contents

Front Cover
Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge
Copyright Page
Table of Contents
Foreword
Acknowledgments
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 communication2.3 Pedagogical knowledge: the skill of communication
2.4 Interface: the form of communication
Summary and conclusion
Bibliographical notes
Part 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 quantitativeto qualitative to qualitative simulation4.1 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 contributions6.1 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' story8.1 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 domain-independent 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.

✦ Subjects

Artificial intelligence--Data processing;Computer-assisted instruction;Knowledge, Theory of;Electronic books;Artificial intelligence -- Data processing