Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Ensembling predictions of student knowledge within intelligent tutoring systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
KT-IDEM: introducing item difficulty to the knowledge tracing model
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Clustering students to generate an ensemble to improve standard test score predictions
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Modeling individualization in a bayesian networks implementation of knowledge tracing
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
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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.