Contextual slip and prediction of student performance after use of an intelligent tutor

  • Authors:

  • Ryan S. J. . Baker;Albert T. Corbett;Sujith M. Gowda;Angela Z. Wagner;Benjamin A. MacLaren;Linda R. Kauffman;Aaron P. Mitchell;Stephen Giguere

  • Affiliations:

  • Department of Social Science and Policy Studies, Worcester Polytechnic Institute, Worcester, MA;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;Department of Social Science and Policy Studies, Worcester Polytechnic Institute, Worcester, MA;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA;Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA;Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA;Department of Social Science and Policy Studies, Worcester Polytechnic Institute, Worcester, MA

  • Venue:
  • UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
  • Year:
  • 2010

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Abstract

Intelligent tutoring systems that utilize Bayesian Knowledge Tracing have achieved the ability to accurately predict student performance not only within the intelligent tutoring system, but on paper post-tests outside of the system Recent work has suggested that contextual estimation of student guessing and slipping leads to better prediction within the tutoring software (Baker, Corbett, & Aleven, 2008a, 2008b) However, it is not yet clear whether this new variant on knowledge tracing is effective at predicting the latent student knowledge that leads to successful post-test performance In this paper, we compare the Contextual-Guess-and-Slip variant on Bayesian Knowledge Tracing to classical four-parameter Bayesian Knowledge Tracing and the Individual Difference Weights variant of Bayesian Knowledge Tracing (Corbett & Anderson, 1995), investigating how well each model variant predicts post-test performance We also test other ways to utilize contextual estimation of slipping within the tutor in post-test prediction, and discuss hypotheses for why slipping during tutor use is a significant predictor of post-test performance, even after Bayesian Knowledge Tracing estimates are controlled for.