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 56
... constraints are applied at each of the four levels . Constraints in Force Constraints Relaxed Syntactic , Semantic None Level 1 Level 2 Syntactic Level 3 Semantic Level 4 None Semantic Syntactic Syntactic , Semantic Figure 1 ...
... constraints are applied at each of the four levels . Constraints in Force Constraints Relaxed Syntactic , Semantic None Level 1 Level 2 Syntactic Level 3 Semantic Level 4 None Semantic Syntactic Syntactic , Semantic Figure 1 ...
Page 269
... constraints will be useful . Step 4 : Combine constraints . A very important component of word problem solving that has been largely overlooked is search . Singley ( 1986 ) found that , in the domain of calculus related rates word ...
... constraints will be useful . Step 4 : Combine constraints . A very important component of word problem solving that has been largely overlooked is search . Singley ( 1986 ) found that , in the domain of calculus related rates word ...
Page 273
... constraints . The tutor allows students to write primitive ( or composed ) constraints at any time during the solution of the problem . There are two ways to write constraints : either by writing a full - fledged equation ( often in ...
... constraints . The tutor allows students to write primitive ( or composed ) constraints at any time during the solution of the problem . There are two ways to write constraints : either by writing a full - fledged equation ( often in ...
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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