UT ML Group: Student Modeling for Intelligent Tutoring

Intelligent tutoring studies the use of AI techniques in computer-aided instruction. An important aspect concerns building a model of the student's current understanding of the domain in order to direct the tutoring process. Our work in the area has focused on using theory refinement to automatically construct a student model from correct domain knowledge and sample student behavior.

Publications

  1. A Novel Application of Theory Refinement to Student Modeling [Abstract] [PDF]
    Paul Baffes and Raymond J. Mooney
    Best Paper Award
    Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 403-408, Portland, OR, August 1996.

  2. Refinement-Based Student Modeling and Automated Bug Library Construction [Abstract] [PDF]
    Paul Baffes and Raymond Mooney
    Journal of Artificial Intelligence in Education, 7, 1 (1996), pp. 75-116.

  3. Automatic Student Modeling and Bug Library Construction using Theory Refinement [Abstract] [PDF]
    Paul T. Baffes
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, December 1994.
    211 pages
    Also appears as Technical Report AI 94-215, Artificial Intelligence Laboratory, University of Texas at Austin, February 1994.

  4. Learning to Model Students: Using Theory Refinement to Detect Misconceptions [Abstract] [PDF]
    Paul T. Baffes
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, June 1993.
    34 pages

  5. Using Theory Revision to Model Students and Acquire Stereotypical Errors [Abstract] [PDF]
    Paul T. Baffes and Raymond J. Mooney
    Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, pp. 617-622, Bloomington, IN, July 1992.

mooney@cs.utexas.edu