Artificial Intelligence and Education: Proceedings of the 4th International Conference on AI and Education, 24-26 May 1989, Amsterdam, Netherlands, Volume 4Dick Bierman, Joost Breuker, Jacobijn Sandberg IOS, 1989 - 339 pages |
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Page 94
... sequences . Each sequence is to be defined by four subsets of knowledge : 1. The " actions " , i.e. a list of functions that the tutor must call successively when it applies a tutoring sequence . These functions are defined in the ...
... sequences . Each sequence is to be defined by four subsets of knowledge : 1. The " actions " , i.e. a list of functions that the tutor must call successively when it applies a tutoring sequence . These functions are defined in the ...
Page 95
... sequence by comparing its " conditions " part to the current student model state . If , for some sequence , these conditions are satisfied , then the monitor's next task is to run this sequence . Since the sequences are composed of ...
... sequence by comparing its " conditions " part to the current student model state . If , for some sequence , these conditions are satisfied , then the monitor's next task is to run this sequence . Since the sequences are composed of ...
Page 96
... sequence Y AND I experienced that the diagnostic of the student Z was X AND I experienced that the sequence Y has been inefficient for a student Z THEN a ) I remove diagnostic X from the " conditions " part of sequence Y OR b ) c ) I ...
... sequence Y AND I experienced that the diagnostic of the student Z was X AND I experienced that the sequence Y has been inefficient for a student Z THEN a ) I remove diagnostic X from the " conditions " part of sequence Y OR b ) c ) I ...
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Abstract Abstract Interpretation actions algebra algorithm analysis Anderson applied approach architecture Artificial Intelligence behavior Blackboard COACH cognitive Cognitive Science components Computer Science concepts constraints construct correct described diagnosis diagrams dialogue discourse discovery learning discussion level domain knowledge dynamic instructional educational environment equation error evaluation example expert module expert system expertise explanation explicit feedback Figure flag tutor forward chaining function fuzzy goal granularity graphical hierarchy hypothesis Igoals implemented inference input instantiations instructional plan instructional planner Intelligent Tutoring Systems interaction interface interpretation knowledge base knowledge representation language learner lesson LISP programming manipulation mathematical misconceptions monitor node novice objects operators output performance problem solving procedures reasoning recursion represented rules schema selected semantic sequence simulation skills solution specific step structure student model subgoals subjects task teacher teaching troubleshooting tutorial strategy types understanding user model word problem XTRA-TE