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Journal Articles ERCIM News Year : 2020

Modelling Student Learning and Forgetting for Optimally Scheduling Skill Review

Abstract

Current adaptive and personalised spacing algorithms can help improve students’ long-term memory retention for simple pieces of knowledge, such as vocabulary in a foreign language. In real-world educational settings, however, students often need to apply a set of underlying and abstract skills for a long period. At the French Laboratoire de Recherche en Informatique (LRI), we developed a new student learning and forgetting statistical model to build an adaptive and personalised skill practice scheduler for human learners.
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Dates and versions

hal-02552100 , version 1 (23-04-2020)

Identifiers

  • HAL Id : hal-02552100 , version 1

Cite

Benoît Choffin, Fabrice Popineau, Yolaine Bourda. Modelling Student Learning and Forgetting for Optimally Scheduling Skill Review. ERCIM News, 2020, Educational Technology, 2020 (120), pp.12-13. ⟨hal-02552100⟩
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