Modelling Student Learning and Forgetting for Optimally Scheduling Skill Review

Autor: Choffin, Benoît, Popineau, Fabrice, Bourda, Yolaine
Přispěvatelé: Données et Connaissances Massives et Hétérogènes (LRI) (LaHDAK - LRI), Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Caisse des Dépôts et Consignations, e-Fran program, Choffin, Benoît
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: ERCIM News
ERCIM News, ERCIM, 2020, Educational Technology, 2020 (120), pp.12-13
ISSN: 0926-4981
Popis: International audience; 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.
Databáze: OpenAIRE