A Novel Application of Theory Refinement to Student Modeling

Paul T. Baffes, Raymond J. Mooney

Theory refinement systems developed in machine learning automatically modify a knowledge base to render it consistent with a set of classified training examples. We illustrate a novel application of these techniques to the problem of constructing a student model for an intelligent tutoring system (ITS). Our approach is implemented in an ITS authoring system called ASSERT which uses theory refinement to introduce errors into an initially correct knowledge base so that it models incorrect student behavior. The efficacy of the approach has been demonstrated by evaluating a tutor developed with ASSERT with 75 students tested on a classification task covering concepts from an introductory course on the C++ programming language. The system produced reasonably accurate models and students who received feedback based on these models performed significantly better on a post test than students who received simple reteaching.

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