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

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

By William J. Clancey, Elliot Soloway

New views and methods are shaping the sector of computer-aided guide. those essays discover cognitively orientated empirical trials that use AI programming as a modeling method and that could supply important perception right into a number of studying difficulties. Drawing on paintings in cognitive thought, plan-based software popularity, qualitative reasoning, and cognitive versions of studying and instructing, this intriguing examine covers a variety of choices to tutoring dialogues.William J. Clancey is Senior examine Scientist on the Institute for examine on studying, Palo Alto. Elliot Soloway is affiliate Professor on the college of Michigan.Contents: synthetic 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. knowing and Debugging amateur 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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Appendix A Define the function fact. It takes a number argument, n and computes the factorial of n, which is the result of multiplying n x n - 1 x n 2 x x 1 . For instance , (fact 3) 3 x 2 x 1 6. By definition, the factorial of 0 is 1 . - = . . = ( 1 ) (defun fact (n) (cond «equal) ( ACTION » ( RECURSWE-CASE ) » I assume that you are trying to test if the number argument is equal to the number zero. There is a special function for testing equivalence to zero. Try to use that function instead of EQUAL.

COGNITIVE MODELING AND INTELLIGENT TUTORING 37 tion of the student's behavior on the student, rather than waiting until the student generates enough of the solution to eliminate the ambiguity. 2. Compiling the model tracing If one looks at all possible sequences of productions that can be generated in any of our models, one finds that it defines a problem space of finite cardinality That cardinality can be quite large, but often simply because we are looking at different permutations of independent or nearly i ndependent steps in a problem solution.

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.

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