A critical component of model-based intelligent tutoring sytems is a mechanism for capturing the conceptual state of the student, which enables the system to tailor its feedback to suit individual strengths and weaknesses. To be useful such a modeling technique must be practical, in the sense that models are easy to construct, and effective, in the sense that using the model actually impacts student learning. This research presents a new student modeling technique which can automatically capture novel student errors using only correct domain knowledge, and can automatically compile trends across multiple student models. This approach has been implemented as a computer program, ASSERT, using a machine learning technique called theory refinement, which is a method for automatically revising a knowledge base to be consistent with a set of examples. Using a knowledge base that correctly defines a domain and examples of a student's behavior in that domain, ASSERT models student errors by collecting any refinements to the correct knowledege base which are necessary to account for the student's behavior. The efficacy of the approach has been demonstrated by evaluating ASSERT using 100 students tested on a classification task covering concepts from an introductory course on the C++ programming language. Students who received feedback based on the models automatically generated by ASSERT performed significantly better on a post test than students who received simple teaching.

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Journal of Artificial Intelligence in Education, Vol. 7, 1 (1996), pp. 75-116.

@Article{baffes:jaied96, title={Refinement-Based Student Modeling and Automated Bug Library Construction}, author={Paul Baffes and Raymond Mooney}, volume={7}, journal={Journal of Artificial Intelligence in Education}, number={1}, key={THREV ASSERT}, pages={75-116}, url="http://www.cs.utexas.edu/users/ai-lab?baffes:jaied96", year={1996} }