Abstract
In this paper, we propose a general approach to improve student modeling predictive accuracy. The approach was designed based on the assumption that student performance is sampled from multiple, rather than only one, distribution and thus should be modeled by multiple classification models. We applied k-means to identify student performances sampled from those multiple distributions, using no additional features beyond binary correctness of student responses. We trained a separate classification model for each distribution and applied the learned models to unseen students to evaluate our approach. The results showed that compared to the base classifier, our proposed approach is able to improve predictive accuracy: 4.3% absolute improvement in R2 and 0.03 absolute improvement in AUC, which are not trivial improvements considering the current state of the art in student modeling.
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Gong, Y., Beck, J.E., Ruiz, C. (2012). Modeling Multiple Distributions of Student Performances to Improve Predictive Accuracy. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds) User Modeling, Adaptation, and Personalization. UMAP 2012. Lecture Notes in Computer Science, vol 7379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31454-4_9
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DOI: https://doi.org/10.1007/978-3-642-31454-4_9
Publisher Name: Springer, Berlin, Heidelberg
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