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Book ChapterDOI

The knowledge engineer as student: metacognitive bases for asking good questions

W. J. Clancey
- pp 80-113
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TLDR
Different forms of metaknowledge are studied, a model of learning is presented that describes how the knowledge engineer detects problem-solving failures and tracks them back to gaps in domain knowledge, which are then reformulated as questions to ask a teacher.
Abstract
Knowledge engineers are efficient, active leamers. They systematically approach domains and acquire knowledge to solve routine, practical problems. By modeling their methods, we may develop a basis for teaching other students how to direct their own learning. In particular, a knowledge engineer is good at detecting gaps in a knowledge base and asking focused questions to improve an expert system''s performance. This ability stems from domain-general knowledge about: problem-solving procedures, the categorization of routine problem-solving knowledge, and domain and task differences. this paper studies these different forms of metaknowledge, and illustrates its incorporation in an intelligent tutoring system. A model of learning is presented that describes how the knowledge engineer detects problem-solving failures and tracks them back to gaps in domain knowledge, which are then reformulated as questions to ask a teacher. We describe how this model of active learning is being developed and tested in a knowledge acquisition program for an expert system.

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Citations
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Are Cognitive Skills Context-Bound?:

TL;DR: In this paper, a synthesis of general and specialized knowledge function in close partnership, and the authors explore the nature of this partnership and consider its implications for educational practice, and suggest a synthesis.
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Learning Theory and the Study of Instruction

TL;DR: In this paper, the authors examine the articulation of learning theory that is emerging from studies that take principled approaches to the design of instruction of complex forms of knowledge and skill, and suggest that studies of instruction can now address questions about the integration of the competences fostered separately by such programs and thereby contribute to the development of more comprehensive theories of the acquisition of knowledge.
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Field review: Metacognition in computation: A selected research review

TL;DR: This work examines metacognition with respect to both problem solving and comprehension processes of cognition, and attempts to place them in the context of computational theories.
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Model construction operators

TL;DR: This paper reviews different ways of describing expert system reasoning, emphasizing the use of simple logic, set, and graph notations for making dimensional analyses of modeling languages and inference methods.
Journal ArticleDOI

Viewing knowledge bases as qualitative models

TL;DR: A viewpoint on expert systems is developed that considers the qualitative nature of knowledge encoded in such programs, and shows how AI's concern with adaptiveness and the rationality of the autonomous agent emphasizes the role of models as what a problem-solver knows.
References
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Journal ArticleDOI

A Sign That Education is Maturing: Taxonomy of Educational Objectives, the Classification of Educational Goals, Handbook I: Cognitive Domain

TL;DR: Using Bloom's Taxonomy to Write Effective Learning Objectives: The Abcds of Writing Learning ObjectIVES: A Basic Guide.
Journal ArticleDOI

Actual Minds, Possible Worlds.

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Actual Minds, Possible Worlds

TL;DR: In this paper, the authors present a reader's retelling of "Clay" by James Joyce and compare it with a novel version of the same story written by Anne Frank.
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Understanding Computers and Cognition: A New Foundation for Design

TL;DR: It is argued that what AI needs is not so much a hermeneutic approach as a better appreciation of biology and psychology.