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Clustered Knowledge Tracing

Zachary A. Pardos, Shubhendu Trivedi, Neil T. Heffernan, Gábor N. Sárközy

By learning a more distributed representation of the input space, clustering can be a powerful source of information for boosting the performance of predictive models. While such semi-supervised methods based on clustering have been applied to increase the accuracy of predictions of external tests, they have not yet been applied to improve within-tutor prediction of student responses. We use a widely adopted model for student prediction called knowledge tracing as our predictor and demonstrate how clustering students can improve model accuracy. The intuition behind this application of clustering is that different groups of students can be better fit with separate models. High performing students, for example, might be better modeled with a higher knowledge tracing learning rate parameter than lower performing students. We use a bagging method that exploits clusterings at different values for K in order to capture a variety of different categorizations of students. The method then combines the predictions of each cluster in order to produce a more accurate result than without clustering.

The final publication is available at Springer via https://doi.org/10.1007/978-3-642-30950-2_52.