Analyzing Fine-Grained Skill Models Using Bayesian And Mixed Effect Methods


Pardos, Z., Feng, M., Heffernan, N. T., & Heffernan-Lindquist, C. (2007).Analyzing fine-grained skill models using bayesian and mixed effect methods. In Luckin & Koedinger (Eds.) Proceedings of the 13th Conference on Artificial Intelligence in Education (pp. 626-628). Amsterdam, Netherlands: IOS Press.


Two modeling methods were employed to answer the same research question of how accurate the various grained models with 1, 5, 39 and 106 skills are at assessing student knowledge in the ASSISTment online tutoring system and predicting their performance on the 2005 state MCAS test. One method, used by the second author, is mixed-effects statistical modeling. The first author evaluated the problem with a Bayesian networks machine learning approach. We compare the two results to identify benefits and drawbacks of either method and to find out if the two results agree. We report that both methods showed compelling similarity in results especially with regard to residuals on the test. Our analysis of these residuals and our online skills allows us to better understand our model and conclude with recommendations for improving the tutoring system, as well as implications for state testing programs.

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