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.
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References
Baker, R.S.J.d., Corbett, A.T., Aleven, V.: More Accurate Student Modeling Through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing. In: Proceedings of the 9th International Conference on Intelligent Tutoring Systems, pp. 406–415 (2008)
Baker, R.S.J.d., Corbett, A.T., Aleven, V.: Improving Contextual Models of Guessing and Slipping with a Truncated Training Set. In: Proceedings of the 1st International Conference on Educational Data Mining, pp. 67–76 (2008)
Beck, J.E., Chang, K.-m.: Identifiability: A fundamental problem of student modeling. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 137–146. Springer, Heidelberg (2007)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
Corbett, A.: Cognitive computer tutors: Solving the two- sigma problem. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds.) UM 2001. LNCS (LNAI), vol. 2109, pp. 137–147. Springer, Heidelberg (2001)
Corbett, A.T., Anderson, J.R.: Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1995)
Corbett, A., Kauffman, L., Maclaren, B., Wagner, A., Jones, E.: A Cognitive Tutor for Genetics Problem Solving: Learning Gains and Student Modeling. Journal of Educational Computing Research 42, 219–239 (2010)
Fogarty, J., Baker, R., Hudson, S.: Case Studies in the use of ROC Curve Analysis for Sensor-Based Estimates in Human Computer Interaction. In: Proceedings of Graphics Interface, pp. 129–136 (2005)
Hawkins, D.M.: The Problem of Overfitting. Journal of Chemical Information and Computer Sciences 44(1), 1–12 (2004)
Koedinger, K.R., Corbett, A.T.: Cognitive tutors: Technology bringing learning sciences to the classroom. In: Sawyer, R.K. (ed.) The Cambridge handbook of the learning sciences, pp. 61–77. Cambridge University Press, New York (2006)
Martin, J., VanLehn, K.: Student Assessment Using Bayesian Nets. International Journal of Human-Computer Studies 42, 575–591 (1995)
Pavlik, P.I., Cen, H., Koedinger, J.R.: Performance Factors Analysis – A New Alternative to Knowledge Tracing. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 531–540 (2009)
Reye, J.: Student Modeling based on Belief Networks. International Journal of Artificial Intelligence in Education 14, 1–33 (2004)
Ritter, S., Harris, T., Nixon, T., Dickinson, D., Murray, R.C., Towle, B.: Reducing the Knowledge Tracing Space. In: Proceedings of the 2nd International Conference on Educational Data Mining, pp. 151–160 (2009)
Rosenthal, R., Rosnow, R.L.: Essentials of Behavioral Research: Methods and Data Analysis, 2nd edn. McGraw-Hill, Boston (1991)
Schmidt, R.A., Bjork, R.A.: New conceptualizations of practice: common principles in three paradigms suggest new concepts for training. Psychological Science 3(4), 207–217 (1992)
Schwartz, D.L., Martin, T.: Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction 22, 129–184 (2004)
Shute, V.J.: SMART: Student modeling approach for responsive tutoring. User Modeling and User-Adapted Interaction 5(1), 1–44 (1995)
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Baker, R.S.J.d. et al. (2010). Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor. In: De Bra, P., Kobsa, A., Chin, D. (eds) User Modeling, Adaptation, and Personalization. UMAP 2010. Lecture Notes in Computer Science, vol 6075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13470-8_7
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DOI: https://doi.org/10.1007/978-3-642-13470-8_7
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