Modeling Skill Acquisition Over Time with Sequence and Topic Modeling


José González-Brenes ;
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, PMLR 38:296-305, 2015.


Online education provides data from students solving problems at different levels of proficiency over time. Unfortunately, methods that use these data for inferring student knowledge rely on costly domain expertise. We propose three novel data-driven methods that bridge sequence modeling with topic models to infer students’ time varying knowledge. These methods differ in complexity, interpretability, accuracy and human supervision. For example, our most interpretable method has similar classification accuracy to the models created by domain experts, but requires much less effort. On the other hand, the most accurate method is completely data-driven and improves predictions by up to 15% in AUC, an evaluation metric for classifiers.

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