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A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems

  • Kai-min Chang
  • Joseph Beck
  • Jack Mostow
  • Albert Corbett
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)

Abstract

This paper describes an effort to model a student’s changing knowledge state during skill acquisition. Dynamic Bayes Nets (DBNs) provide a powerful way to represent and reason about uncertainty in time series data, and are therefore well-suited to model student knowledge. Many general-purpose Bayes net packages have been implemented and distributed; however, constructing DBNs often involves complicated coding effort. To address this problem, we introduce a tool called BNT-SM. BNT-SM inputs a data set and a compact XML specification of a Bayes net model hypothesized by a researcher to describe causal relationships among student knowledge and observed behavior. BNT-SM generates and executes the code to train and test the model using the Bayes Net Toolbox [1]. Compared to the BNT code it outputs, BNT-SM reduces the number of lines of code required to use a DBN by a factor of 5. In addition to supporting more flexible models, we illustrate how to use BNT-SM to simulate Knowledge Tracing (KT) [2], an established technique for student modeling. The trained DBN does a better job of modeling and predicting student performance than the original KT code (Area Under Curve = 0.610 > 0.568), due to differences in how it estimates parameters.

Keywords

Intelligent Tutor System Student Knowledge Student Modeling Junction Tree Automatic Speech Recognizer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kai-min Chang
    • 1
  • Joseph Beck
    • 1
  • Jack Mostow
    • 1
  • Albert Corbett
    • 1
  1. 1.Project LISTEN, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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