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 13
... environment . We do not believe it will describe how learning takes place in an environment based on MATHPERT . In this environment , we will " nip bugs in the bud " . ( Perhaps we should not mix metaphors : we will nip bugs in the ...
... environment . We do not believe it will describe how learning takes place in an environment based on MATHPERT . In this environment , we will " nip bugs in the bud " . ( Perhaps we should not mix metaphors : we will nip bugs in the ...
Page 186
... environment , supply the basis for the environment of PROBIT . The environment General facilities A " word problems " generator generates problems dealing with series of dependent trials . The student is required to assign values to ...
... environment , supply the basis for the environment of PROBIT . The environment General facilities A " word problems " generator generates problems dealing with series of dependent trials . The student is required to assign values to ...
Page 331
... environment . A computational goal may be satisfied by more than one plan , and the " best " plan for a goal will depend on what the user knows and the current context , such as the state of the environment and the existence of specific ...
... environment . A computational goal may be satisfied by more than one plan , and the " best " plan for a goal will depend on what the user knows and the current context , such as the state of the environment and the existence of specific ...
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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