Detecting Flow via a Machine Learning Model in a MOOC Context - HAL-SHS - Sciences de l'Homme et de la Société
Chapitre D'ouvrage Année : 2024

Detecting Flow via a Machine Learning Model in a MOOC Context

Nour El Mawas
Rémi Bachelet
Jean Heutte

Résumé

Flow is a human psychological state positively correlated to self-efficacy, motivation, engagement, and academic achievement, all of which positively affect learning. However, automatic, real-time flow detection is extremely difficult, a challenge particularly exacerbated in a Massively Online Open Course (MOOC) context, where the distant, and asynchronous components rejoin the educational and online context. We approach this issue by training a Machine Learning (ML) model to detect flow transparent and automatically in a MOOC. We pair the results of the EduFlow2 and Flow-Q questionnaires (n = 1 553, two years data collection), and their MOOC log data (French MOOC “Gestion de Projet” [Project Management]) to a ML pipeline to create a ML model that detects flow (ROC = 0.68 and PRC = 0.87) in a MOOC context. This ML model detects flow (0.85) with a greater Precision than its absence (0.34).
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Dates et versions

halshs-04677184 , version 1 (25-08-2024)

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Copyright (Tous droits réservés)

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Citer

Sergio Iván Ramírez Luelmo, Nour El Mawas, Rémi Bachelet, Jean Heutte. Detecting Flow via a Machine Learning Model in a MOOC Context. In Fred, A., Hadjali, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2024. Communications in Computer and Information Science, 2171, Springer Nature Switzerland, pp.123-142, 2024, ⟨10.1007/978-3-031-66694-0_8⟩. ⟨halshs-04677184⟩
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