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

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Artificial Intelligence and Tutoring Systems, the first comprehensive reference text in this dynamic area, surveys research since the early 1970s and assesses the state of the art. Adopting the perspective of the communication of knowledge, the author addresses practical issues involved in designing instructional systems as well as theoretical questions raised by investigating computational methods of knowledge communication.

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More About This Book

Overview

Artificial Intelligence and Tutoring Systems, the first comprehensive reference text in this dynamic area, surveys research since the early 1970s and assesses the state of the art. Adopting the perspective of the communication of knowledge, the author addresses practical issues involved in designing instructional systems as well as theoretical questions raised by investigating computational methods of knowledge communication.

Weaving together the goals, contributions, and fascinating challenges of intelligent tutoring system development, this timely book is useful as a text in courses on intelligent tutoring systems or computer-aided instruction, an introduction for newcomers to the field, or as a reference for researchers and practitioners.

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Product Details

  • ISBN-13: 9780934613262
  • Publisher: Morgan Kaufmann Publishers
  • Publication date: 1/28/1987
  • Edition number: 1
  • Pages: 486
  • Product dimensions: 6.34 (w) x 9.31 (h) x 1.15 (d)

Table of Contents

Artificial Intelligence and Tutoring Systems:
Computational and Cognitive Approaches to the Communication of Knowledge
by Etienne Wenger
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
Bibliographic 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
Bibliographic 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
3.2.1 Natural reasoning: plausible inferences
3.2.2 An inquiry into the nature of tutorial dialogues
3.3 WHY: the Socratic method
3.4 From local strategies to multiple mental models
3.4.1 Considering global goals
3.4.2 Domain knowledge: multiple viewpoints
3.4.3 From multiple viewpoints to multiple mental models
Summary and Conclusion
Bibliographic Notes

Chapter 4 SOPHIE: from quantitative to qualitative simulation
4.1 Simulation: Dialogues and learning environments
4.1.1 The METEOROLOGY tutor: question answering
4.1.2 A reactive learning environment for troubleshooting
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
Bibliographic Notes

Chapter 5 Interactive simulations: communicating mental models
5.1 STEAMER: simulation and abstraction
5.1.1 Manipulable simulation of a steam plant
5.1.2 Tutorial capabilities: the use of abstractions
5.1.3 Inspectable simulations: conceptual fidelity
5.1.4 From graphic abstractions to qualitative processes
5.2 QUEST: progressions of qualitative models
5.2.1 Mental models: a developmental approach
5.2.2 A learning environment for zero-order qualitative models
Summary and Conclusion
Bibliographic Notes

Chapter 6 Existing CAI traditions: other early contributions
6.1 Early attempts to tailor problem-solving experiences
6.1.1 EXCHECK: understanding natural proof procedures
6.1.2 A probabilistic model for judgmental knowledge
6.1.3 BIP: curriculum information networks
6.2 Pedagogical experiments: teaching expertise
6.2.1 Self-improving tutors: tuning strategies empirically
6.2.2 Concept teaching: a formal analytical approach
Summary and Conclusion
Bibliographic 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
7.4.1 WUSOR-I: an expert-based coach
7.4.2 Overlays: a rule-based paradigm for student modeling
7.4.3 The overlay theory in WUSOR-II
7.4.4 WUSOR-III: the genetic graph
7.5 Architectures organized around curricula
7.5.1 The bite-sized architecture
7.5.2 Curriculum organization and steering testing
Summary and Conclusion
Bibliographic Notes

Chapter 8 Bugs in procedural skills: the 'buggy repair step' story
8.1 BUGGY: an enumerative theory of bugs
8.1.1 A modular representation of subskills
8.1.2 The nature of bugs in procedural skills
8.2 DEBUGGY: a diagnostic system
8.2.1 Off-line diagnosis: heuristic search strategies
8.2.2 Coercions: a model-driven analysis of noisy data
8.2.3 IDEBUGGY: an on-line version
8.2.4 Diagnostic tests and the notion of subskill
8.3 REPAIR theory: a generative theory of bugs
8.3.1 Explanatory power and observational coverage
8.4 STEP theory: a learning model of bug generation
8.4.1 Planning nets: teleological reasoning
8.4.2 The nature of learning in procedural skills
8.4.3 SIERRA: a model of the generation of core procedures
8.4.4 Coverage and validation: competitive argumentation
Summary and conclusion
Bibliographic 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
9.2.1 Representing procedural skills with production systems
9.2.2 Containing the combinatorics of the modeling task
9.2.3 Automated text generation
9.3 PIXIE: generating mal-rules
9.3.1 Diagnosis by reconstruction
9.3.2 Student interviews: toward a generative theory of bugs
9.4 UMFE: a generic modeling subsystem

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
10.3.1 ACM's modeling process
10.4 Primitive operators versus bugs
Summary and Conclusion
Bibliographic 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
11.2.1 Ideas for a theory of design
11.2.2 SPADE-0: a plan-based editor
11.3 The MACSYMA ADVISOR: plans and beliefs
11.4 MENO: debugging and tutoring
11.4.1 MENO-II: error-oriented program analysis
11.4.2 The knowledge of novice programmers
11.4.3 PROUST: intention-based diagnosis
11.4.4 MENO-TUTOR: strategies for tutorial discourses
Summary and Conclusion
Bibliographic Notes

Chapter 12 GUIDON: the epistemology of an expert system
12.1 GUIDON: a tutor on top of MYCIN
12.1.1 Tutorial context: the case method
12.1.2 Modularity: domain and pedagogical expertise
12.1.3 GUIDON and MYCIN: rule-based representations
12.2 NEOMYCIN: reconfiguring the expert knowledge
12.2.1 NEOMYCIN: a model of diagnostic thinking
12.2.2 The significance of NEOMYCIN's reconfiguration
12.3 GUIDON2: tutoring systems for classification tasks
12.3.1 HERACLES: a heuristic classification system
12.3.2 Diagnosing heuristic classification
12.3.3 Teaching heuristic classification
Summary and Conclusion
Bibliographic Notes

Chapter 13 ACTP: a marriage with cognitive psychology
13.1 Cognitive theories and pedagogy
13.2 Tutoring systems based on theoretical principles
13.3 Field evaluation of ACTP's tutors
13.4 Skill acquisition and expertise
Summary and Conclusion
Bibliographic Notes
Part III A Synopsis: the state of the art
Chapter 14 The computer
14.1 Representational mappings: epistemic fidelity
14.2 Internal representation: the power of process models
14.3 External representation: the power of interfaces
14.4 Process model and interface: reification
14.5 Representational commitments
Bibliographic Notes

Chapter 15 The domain
15.1 Communicability in epistemic terms
15.2 Compiled knowledge
15.3 Conceptual articulation: contents
15.3.1 Articulation by justifications: toward first principles
15.3.2 Articulation by integration
15.3.3 Conceptual contents: concluding remarks
15.4 Epistemological articulation: organization
15.4.1 Articulation by decomposition
15.4.2 Articulation by configuration
15.5 The paradox of articulation
15.6 Articulation versus compilation

Chapter 16 The student
16.1 Scope of expertise
16.2 Incorrect knowledge
16.2.1 Different types of theories of bugs
16.2.2 Trade-offs between types of theories of bugs
16.2.3 Bugs and misconceptions: compilation and articulation
16.3 Viewpoints
16.3.1 What are viewpoints?
16.3.2 Viewpoints in knowledge communication systems
16.4 Knowledge states
16.4.1 Knowledge states: structural and genetic constraints
16.4.2 Representing variations: classification versus emergence
16.4.3 Theories of knowledge states: knowledge communication

Chapter 17 Diagnosis
17.1 Behavioral diagnosis
17.1.1 Noninferential evaluation
17.1.2 Inference of unobservable behavior
17.2 Epistemic Diagnosis
17.2.1 Direct assignment of credit blame
17.2.2 Structural consistency
17.2.3 Longitudinal consistency
17.3 Noise: sources and solutions
17.3.1 Noise in the data: variations in behavior over time
17.3.2 Noise in the diagnostic process: ambiguities
17.3.3 Noise in the model of communicable knowledge
17.4 Sources of evidence: diagnostic data
17.4.1 Diagnostic observation
17.4.2 Overt diagnostic actions
17.5 Aspects of diagnostic expertise

Chapter 18 Didactics
18.1 Didactic operations: plans of action
18.2 Pedagogical contexts
18.3 Decision base: constraints and resources
18.3.1 Didactic
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