The history of computers in education can be characterized by a continuing effort to construct intelligent tutorial programs which can adapt to the individual needs of a student in a one-on-one setting. A critical component of these intelligent tutorials is a mechanism for modeling the conceptual state of the student so that the system is able to tailor its feedback to suit individual strengths and weaknesses. The primary contribution of this research is 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 into bug libraries. 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 knowledge 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 using 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 reteaching.

PDF, PS

PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.

@PhdThesis{baffes:phd94, title={Automatic Student Modeling and Bug Library Construction using Theory Refinement}, author={Paul T. Baffes}, school={Department of Computer Sciences, The University of Texas at Austin}, address={Austin, TX}, url="http://www.cs.utexas.edu/users/ai-lab?baffes:phd94", year={1994} }