Student modeling has been identified as an important component to the long term development of Intelligent Computer-Aided Instruction (ICAI) systems. Two basic approaches have evolved to model student misconceptions. One uses a static, predefined library of user bugs which contains the misconceptions modeled by the system. The other uses induction to learn student misconceptions from scratch. Here, we present a third approach that uses a machine learning technique called theory revision. Using theory revision allows the system to automatically construct a bug library for use in modeling while retaining the flexibility to address novel errors.

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In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, pp. 617-622, Bloomington, IN 1992.

@InProceedings{baffes:cogsci92, title={Using Theory Revision to Model Students and Acquire Stereotypical Errors}, author={Paul T. Baffes and Raymond J. Mooney}, booktitle={Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society}, address={Bloomington, IN}, pages={617-622}, url="http://www.cs.utexas.edu/users/ai-lab?baffes:cogsci92", year={1992} }