Download PDF by William J. Clancey, Elliot Soloway: Artificial Intelligence and Learning Environments

By William J. Clancey, Elliot Soloway

ISBN-10: 0262530902

ISBN-13: 9780262530903

New views and methods are shaping the sphere of computer-aided guideline. those essays discover cognitively orientated empirical trials that use AI programming as a modeling technique and which could supply precious perception right into a number of studying difficulties. Drawing on paintings in cognitive thought, plan-based software popularity, qualitative reasoning, and cognitive types of studying and educating, this interesting study covers a variety of choices to tutoring dialogues.William J. Clancey is Senior study Scientist on the Institute for learn on studying, Palo Alto. Elliot Soloway is affiliate Professor on the collage of Michigan.Contents: man made Intelligence and studying Environments, William J. Clancey, Elliot Soloway. Cognitive Modeling and Intelligence Tutoring, John R. Anderson, C. Franklin Boyle, Albert T. Corbett, Matthew W. Lewis. figuring out and Debugging beginner courses, W. Lewis Johnson. Causal version Progressions as a origin for clever studying Environments, Barbara Y. White and John R. Frederiksen.

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While such a mastery policy for problem sequence may seem reasonable and there is evidence in the educational literature for its effectiveness [16], it is interesting to inquire as to its underlying psychological rationalization . Why not go onto new problems while the student is weak on current knowledge and teach both the new knowledge and the old weak knowledge in the context of the new problems? Fundamentally, the mastery policy rests on a belief in an optimal learning load-that if we overload a student with too many things to learn, he will learn none of them well.

R . ANDERSON ET AL. what these responses mean; and (3) what tutorial dialogue templates are attached to the productions, not what these dialogues mean . We are in fact working on a new pUPs-based tutor [12] which is a limited realization of this idea. It is concerned with tutoring three programming languages-LIsP, PASCAL, and PROLOG. We hope to build student models for different programming domains independent of tutoring strategy and to build different tutors to implement variations on tutoring strategy independent of domain.

The obvious methodology for doing this is to run o u r nondeterministic student model forward and see what paths prod uce matching behavior. Whil e there are co m ple x itie s and efficiencies that have been added to this basic insight this is the core idea . The rest of the discussion of the student model is concerned with iss ue s raised in trying to i m plem ent this core idea. 3. 1 . 1 . Nondeterminacy Nonde te rmin a cy i n th e prod u ction sequence is a major source of problems in implementi ng the model-tracing methodology.

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Artificial Intelligence and Learning Environments by William J. Clancey, Elliot Soloway

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