Individualized Bayesian Knowledge Tracing Models

  • Michael V. Yudelson
  • Kenneth R. Koedinger
  • Geoffrey J. Gordon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7926)

Abstract

Bayesian Knowledge Tracing (BKT)[1] is a user modeling method extensively used in the area of Intelligent Tutoring Systems. In the standard BKT implementation, there are only skill-specific parameters. However, a large body of research strongly suggests that student-specific variability in the data, when accounted for, could enhance model accuracy [5,6,8]. In this work, we revisit the problem of introducing student-specific parameters into BKT on a larger scale. We show that student-specific parameters lead to a tangible improvement when predicting the data of unseen students, and that parameterizing students’ speed of learning is more beneficial than parameterizing a priori knowledge.

Keywords

Bayesian knowledge tracing model fitting model selection student-specific model parameters 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael V. Yudelson
    • 1
  • Kenneth R. Koedinger
    • 1
  • Geoffrey J. Gordon
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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