出版社：The Japanese Society for Artificial Intelligence
摘要：In simulation-based learning environments, 'unexpected' phenomena often work as counterexamples which promote a learner to reconsider the problem. It is important that counterexamples contain sufficient information which leads a learner to correct understanding. This paper proposes a method for creating such counterexamples. Error-Based Simulation (EBS) is used for this purpose, which simulates the erroneous motion in mechanics based on a learner's erroneous equation. Our framework is as follows: (1) to identify the cause of errors by comparing a learner's answer with the problem-solver's correct one, (2) to visualize the cause of errors by the unnatural motions in EBS. To perform (1), misconceptions are classified based on problem-solving model, and related to their appearance on a learner's answers (error-identification rules). To perform (2), objects' motions in EBS are classified and related to their suggesting misconceptions (error-visualization rules). A prototype system is implemented and evaluated through a preliminary test, to confirm the usefulness of the framework.
