Modelling Student Learning and Forgetting for Optimally Scheduling Skill Review.

ERCIM NEWS(2020)

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摘要
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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