Analyzing Fine-Grained Skill Models Using Bayesian and Mixed Effects Methods

Authors:
Zachary A. Pardos;Mingyu Feng;Neil T. Heffernan;Cristina Linquist-Heffernan
Affiliations:
Worcester Polytechnic Institute, {zpardos, mfeng, nth}@wpi.edu;Worcester Polytechnic Institute, {zpardos, mfeng, nth}@wpi.edu;Worcester Polytechnic Institute, {zpardos, mfeng, nth}@wpi.edu;Worcester Polytechnic Institute, {zpardos, mfeng, nth}@wpi.edu
Venue:
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Year:
2007

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Abstract

Two modelling methods were used to answer the research question of how accurate various grained 1, 5, 39 and 106 skill models are at assessing student knowledge in the ASSISTment online tutoring system and predicting student performance on a state math test. One method is mixed-effects statistical modelling. The other uses a Bayesian networks machine learning approach. We compare the prediction 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 which support the use of fine grained skill models.