Intelligent Tutoring SystemsD. Sleeman, J. S. Brown The first volume to appear on this topic and now a classic in the field, "Intelligent Tutoring Systems" provides the reader with descriptions of the major systems implemented before 1981. The introduction seeks to emphasise the principal contributions made in the field, to outline continuing research issues, and to relate these to research activities in artificial intelligence and cognitive science. Subject areas discussed are as varied as arithmetic, algebra, electronics, and medicine, together with some informal gaming environments. |
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Page 90
... Examples provide concrete instances of these abstract concepts . Providing both the description of the generic Issue ( a concept ) as well as a concrete example of it increases the chance that the student will integrate this piece of ...
... Examples provide concrete instances of these abstract concepts . Providing both the description of the generic Issue ( a concept ) as well as a concrete example of it increases the chance that the student will integrate this piece of ...
Page 313
... EXAMPLE THE RIGHT ANSWERS ARE IN FACT 3 AND 16 FIG . 1. Extract from protocol of quadratic tutor in use . features . These features are weighted as to how much they help or hinder the acquisition of the various subgoals . The example ...
... EXAMPLE THE RIGHT ANSWERS ARE IN FACT 3 AND 16 FIG . 1. Extract from protocol of quadratic tutor in use . features . These features are weighted as to how much they help or hinder the acquisition of the various subgoals . The example ...
Page 315
... example types , and co - ordinate the other components of the program , namely the task adminis- trator , task ... example . ( b ) ( HYP RULE ) -test the hypothesis that the student has mastered the rule in question ( see section on ...
... example types , and co - ordinate the other components of the program , namely the task adminis- trator , task ... example . ( b ) ( HYP RULE ) -test the hypothesis that the student has mastered the rule in question ( see section on ...
Contents
Intelligent tutoring systems | 1 |
A Friendly interfaces | 4 |
Special purpose deduction techniques | 7 |
Copyright | |
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AICAI air mass algebra algorithm analogy answer approach ARGUMENT arithmetic Artificial Intelligence assertions assumptions behavior Beranek and Newman Borrow Brown Burton caves circuit coach Cognitive Cognitive Science column complex component concept correct d-rule debugging deduction describe diagnostic dialogue diode discussed domain equations errors evaluation evidence example expert explanation explore extrapolation techniques fault FIRES-RESULT genetic graph goal Goldstein GROUP IS C H GUIDON heuristic Homunculus hypothesis implemented incorrect inference intelligent tutoring systems interaction interface Issues John Seely Brown learning MACSYMA mal-rules methods misconceptions mode module move multiple MYCIN nodes operator particular PEAK possible primitive bugs problem problem-solving procedure production rules propagation rainfall representation result rules sequence simulation skills Sleeman solution solving SOPHIE Spade-0 specific steps strategy structure student model subgoals subskills subtraction task teaching theory troubleshooting tutor underlying voltage Wumpus zero