Using Theory Revision to Model Students and Acquire Stereotypical Errors (1992)
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.
In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, pp. 617-622, Bloomington, IN 1992.

Paul Baffes Ph.D. Alumni baffes [at] intellilearn com
Raymond J. Mooney Faculty mooney [at] cs utexas edu