A new student modeling system called ASSERT is described which uses domain independent learning algorithms to model unique student errors and to automatically construct bug libraries. ASSERT consists of two learning phases. The first is an application of theory refinement techniques for constructing student models from a correct theory of the domain being tutored. The second learning cycle automatically constructs the bug library by extracting common refinements from multiple student models which are then used to bias future modeling efforts. Initial experimental data will be presented which suggests that ASSERT is a more effective modeling system than other induction techniques previously explored for student modeling, and that the automatic bug library construction significantly enhances subsequent modeling efforts.

PDF, PS

unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.

@unpublished{baffes:proposal93, title={Learning to Model Students: Using Theory Refinement to Detect Misconceptions}, author={Paul T. Baffes}, month={June}, pages={34 pages}, note={Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin}, url="http://www.cs.utexas.edu/users/ai-lab?baffes:proposal93", year={1993} }