Estimating Individual Differences for Student Modeling in Intelligent Tutors from Reading and Pretest Data
Past studies have shown that Bayesian Knowledge Tracing (BKT) can predict student performance and implement Cognitive Mastery successfully. Standard BKT individualizes parameter estimates for skills, also referred to as knowledge components (KCs), but not for students. Studies deriving individual student parameters from the data logs of student tutor performance have shown improvements to the standard BKT model fits, and result in different practice recommendations for students. This study investigates whether individual student parameters, specifically individual difference weights (IDWs) , can be derived from student activities prior to tutor use. We find that student performance measures in reading instructional text and in a conceptual knowledge pretest can be employed to predict IDWs. Further, we find that a model incorporating these predicted IDWs performs well, in terms of model fit and learning efficiency, when compared to a standard BKT model and a model with best-fitting IDWs derived from tutor performance.
KeywordsBKT Genetics Machine learning Student modeling
This research was supported by the National Science Foundation under the grant “Knowing What Students Know: Using Education Data Mining to Predict Robust STEM Learning”, award number DRL1420609.
- 2.Mayo, M., Mitrovic, A.: Optimising ITS behaviour with Bayesian networks and decision theory. Int. J. Artif. Intell. Educ. 12, 124–153 (2001)Google Scholar
- 9.Lee, J., Brunskill, E.: The impact of individualizing student models on necessary practice opportunities. In: Yacef, K., Zaiane, O., Hershkovitz, A., Yudelson, M., Stamper, J. (eds.) EDM 2012 Proceedings of the 5th International Conference on International Educational Data Mining Society, pp. 118–125 (2012)Google Scholar
- 16.Zwann, R., Singer, M.: Text comprehension. In: Graesser, A., Gernsbacher, M., Goldman, S. (eds.) Handbook of Discourse Processes, pp. 83–121. Erlbaum, Mahwah (2003)Google Scholar
- 17.Arroyo, I., Beck, J.E., Park Woolf, B., Beal, C.R., Schultz, K.: Macroadapting animalwatch to gender and cognitive differences with respect to hint interactivity and symbolism. In: Gauthier, G., VanLehn, K., Frasson, C. (eds.) ITS 2000. LNCS, vol. 1839, pp. 574–583. Springer, Heidelberg (2000)CrossRefGoogle Scholar
- 18.Baker, R.S., Corbett, A.T., Aleven, V.: More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 406–415. Springer, Heidelberg (2008)CrossRefGoogle Scholar