2010 | OriginalPaper | Buchkapitel
Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor
verfasst von : Ryan S. J. d. Baker, Albert T. Corbett, Sujith M. Gowda, Angela Z. Wagner, Benjamin A. MacLaren, Linda R. Kauffman, Aaron P. Mitchell, Stephen Giguere
Erschienen in: User Modeling, Adaptation, and Personalization
Verlag: Springer Berlin Heidelberg
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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.