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DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills

Abstract : Spaced repetition consists in temporally distributing exposure to an information so as to improve long-term memorization for a human learner. However, most adaptive spacing algorithms rely on expert-defined heuristics and are limited to pure memorization, e.g. foreign language learning. In this article, we propose a new student model for human skill learning and forgetting, called DAS3H. We provide empirical evidence on three real-world educational datasets that DAS3H, which incorporates both exercise-skill relationships and forgetting effect, outperforms other state-of-the-art student models that consider one or the other.
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https://hal.archives-ouvertes.fr/hal-03427048
Contributor : Benoît Choffin Connect in order to contact the contributor
Submitted on : Friday, November 12, 2021 - 6:59:05 PM
Last modification on : Wednesday, December 1, 2021 - 4:41:29 PM

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  • HAL Id : hal-03427048, version 1

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Benoît Choffin, Fabrice Popineau, Yolaine Bourda, Jill-Jênn Vie. DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills. JDSE 2019 - Paris-Saclay Junior Conference on Data Science and Engineering, Sep 2019, Gif-sur-Yvette, France. ⟨hal-03427048⟩

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