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High-Level Student Modeling with Machine Learning

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Intelligent Tutoring Systems (ITS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1839))

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Abstract

We have constructed a learning agent that models student behavior at a high level of granularity for a mathematics tutor. Rather than focusing on whether the student knows a particular piece of knowledge, the learning agent determines how likely the student is to answer a problem correctly and how long he will take to generate this response. To construct this model, we used traces from previous users of the tutor to train the machine learning agent. This agent used information about the student, the current topic, the problem, and the student’s efforts to solve this problem to make its predictions. This model was very accurate at predicting the time students required to generate a response, and was somewhat accurate at predicting the likelihood the student’s response was correct. We present two methods for integrating such an agent into an intelligent tutor.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Massachusetts, Amherst, MA, 01003, USA

    Joseph E. Beck & Beverly Park Woolf

Authors
  1. Joseph E. Beck
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  2. Beverly Park Woolf
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Editor information

Editors and Affiliations

  1. Département d’informatique, Université du Québec á Montréal, C.P. 8888, succ. Centre-Ville, Montréal, QC, Canada, H3C 3P8

    Gilles Gauthier

  2. Département d’informatique et de recherche opérationnelle, Université de Montréal, C.P. 6128, succ. Centre-Ville, Montréal, QC, Canada, H3C 3J7

    Claude Frasson

  3. Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Kurt VanLehn

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© 2000 Springer-Verlag Berlin Heidelberg

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Beck, J.E., Woolf, B.P. (2000). High-Level Student Modeling with Machine Learning. In: Gauthier, G., Frasson, C., VanLehn, K. (eds) Intelligent Tutoring Systems. ITS 2000. Lecture Notes in Computer Science, vol 1839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45108-0_62

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  • DOI: https://doi.org/10.1007/3-540-45108-0_62

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67655-3

  • Online ISBN: 978-3-540-45108-2

  • eBook Packages: Springer Book Archive

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