Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor
| Autor: | Aaron P. Mitchell, Linda R. Kauffman, Albert T. Corbett, Benjamin A. MacLaren, Angela Z. Wagner, Stephen Giguere, Sujith M. Gowda, Ryan S. Baker |
|---|---|
| Rok vydání: | 2010 |
| Předmět: | |
| Zdroj: | User Modeling, Adaptation, and Personalization ISBN: 9783642134692 UMAP |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|