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While work in the 2010 KDD Cup data set showed the benefits of ensemble methods, work in the Genetics Tutor failed to show similar benefits. We hypothesize that the key factor has been data set size. We explore the potential for improving student performance prediction with ensemble methods in a data set drawn from a different tutoring system, the ASSISTments Platform, which contains 15 times the number of responses of the Genetics Tutor data set. We evaluated the predictive performance of eight student models and eight methods of ensembling predictions. 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