Abstract
This paper describes the student modeling component of Andes, an Intelligent Tutoring System for Newtonian physics. Andes’ student model uses a Bayesian network to do long-term knowledge assessment, plan recognition and prediction of students’ actions during problem solving. The network is updated in real time, using an approximate anytime algorithm based on stochastic sampling, as a student solves problems with Andes. The information in the student model is used by Andes’ Help system to tailor its support when the student reaches impasses in the problem solving process. In this paper, we describe the knowledge structures represented in the student model and discuss the implementation of the Bayesian network assessor. We also present a preliminary evaluation of the time performance of stochastic sampling algorithms to update the network.
This research is supported by AFOSR under grant number F49620-96-1-0180, by ONR’s Cognitive Science Division under grant N00014-96-1-0260 and by DARPA’s Computer Aided Education and Training Initiative under grant N66001-95-C-8367. In addition, Dr. Druzdzel was supported by the National Science Foundation under Faculty Early Career Development (CAREER) Program, grant IRI-9624629. We would like to thank Zhendong Niu and Yan Lin for programming support.
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Conati, C., Gertner, A.S., VanLehn, K., Druzdzel, M.J. (1997). On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks. In: Jameson, A., Paris, C., Tasso, C. (eds) User Modeling. International Centre for Mechanical Sciences, vol 383. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2670-7_24
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DOI: https://doi.org/10.1007/978-3-7091-2670-7_24
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