Publications: Student Modeling for Intelligent Tutoring Systems

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

   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.

   ML ID: 65

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

   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.

   ML ID: 59

3. Automatic Student Modeling and Bug Library Construction using Theory Refinement
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   Paul T. Baffes
   PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1994.

   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.

   ML ID: 40

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

   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.

   ML ID: 24

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

   Student modeling has been identified as an important component to the long term development of Intelligent Computer-Aided Instruction (ICAI) systems. Two basic approaches have evolved to model student misconceptions. One uses a static, predefined library of user bugs which contains the misconceptions modeled by the system. The other uses induction to learn student misconceptions from scratch. Here, we present a third approach that uses a machine learning technique called theory revision. Using theory revision allows the system to automatically construct a bug library for use in modeling while retaining the flexibility to address novel errors.

   ML ID: 16