Modelling Student Learning and Forgetting for Optimally Scheduling Skill Review

Benoît Choffin (1, 2) , Fabrice Popineau (1, 2) , Yolaine Bourda (1, 2)

1 LaHDAK - LRI - Données et Connaissances Massives et Hétérogènes (LRI)
2 LRI - Laboratoire de Recherche en Informatique

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.

Keywords: learning analytics; artificial intelligence in education

Domains: Computer Science [cs]; Artificial Intelligence [cs.AI]; Statistics [stat]; Applications [stat.AP]; Statistics [stat]; Machine Learning [stat.ML]; Statistics [stat]; Other Statistics [stat.ML]; Computer Science [cs]; Technology for Human Learning

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⟩