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

By William J. Clancey, Elliot Soloway

New views and strategies are shaping the sphere of computer-aided guide. those essays discover cognitively orientated empirical trials that use AI programming as a modeling technique and that could offer worthy perception right into a number of studying difficulties. Drawing on paintings in cognitive thought, plan-based application attractiveness, qualitative reasoning, and cognitive versions of studying and educating, this interesting learn covers a variety of choices to tutoring dialogues.William J. Clancey is Senior examine Scientist on the Institute for study on studying, Palo Alto. Elliot Soloway is affiliate Professor on the collage 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. realizing and Debugging beginner courses, W. Lewis Johnson. Causal version Progressions as a starting place for clever studying Environments, Barbara Y. White and John R. Frederiksen.

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The substeps of cleanup. The screen maintains two levels of completed sub­ steps . Thus , the student can see that cleanup was solved by a "distribute" followed by a "collect . " Having finished the cleanup substep the student has turned to the next major step in solving the problem which is to move the variable terms to one side . The student has chosen to try to answer this directly rather than pursue it in substeps . Unfortunately, he has made the classic sign error and entered 15 - 4x. The tutor recognizes this error and enters a remedial message on the blackboard.

Sensitivity to student history By and large the only student model we use is our generic model which is a composite of all correct and incorrect moves that a student can make. At each point in time we are prepared to process all the production rules that we have seen any student use , correct or buggy. If students make an error we give the same feedback independe nt of their history The only place we show sensitivity to student history is in presenting remedial problems to students who are having difficulties.

3. An d erson , J . R . , Acquisition of proof skills in geometry, in: J . G . Carbonell , R. Michalski and T. ), Machine Learning: An Artificial Intelligence Approach (Tioga, Palo Alto, CA, 1983). 4. Anderson, J . R . , The Architecture of Cognition (Harvard University Press, Cambridge, MA , 1 983 ) . 5. A n d erson , J . R . , Production systems. learning , an d tut o ring , in: D. Kl ah r , P. Langley and R. ), Production System Models of Learning and Development ( MIT Press, Cam­ bridge , MA , 1987) 437-458.

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