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Towards a Machine Learning flow-predicting model in a MOOC context

Sergio Ramirez 1 Nour El Mawas 1 Rémi Bachelet 2 Jean Heutte 1 
1 Trigone-CIREL
CIREL - Centre Interuniversitaire de Recherche en Education de Lille - ULR 4354 : EA1038
Abstract : 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 prediction is quite difficult, particularly in a Massively Online Open Course context, even more so because of its online, distant, asynchronous, and educational components. In such context, flow prediction allows for personalization of activities, content, and learning-paths. By pairing the results of the EduFlow2 and Flow-Q questionnaires (n = 1589, two years data collection) from the French MOOC “Gestion de Projet” (Project Management) to Machine Learning techniques (Logistic Regression), we create a Machine Learning model that successfully predicts flow (combined Accuracy & Precision ~ 0.8, AUC = 0.85) in an automatic, asynchronous fashion, in a MOOC context. The resulting Machine Learning model predicts the presence of flow (0.82) with a greater Precision than it predicts its absence (0.74).
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https://halshs.archives-ouvertes.fr/halshs-03606527
Contributor : Jean Heutte Connect in order to contact the contributor
Submitted on : Friday, March 11, 2022 - 6:30:55 PM
Last modification on : Tuesday, May 17, 2022 - 3:06:16 PM

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  • HAL Id : halshs-03606527, version 1

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Sergio Ramirez, Nour El Mawas, Rémi Bachelet, Jean Heutte. Towards a Machine Learning flow-predicting model in a MOOC context. 14th International Conference on Computer Supported Education (CSEDU 2022), Apr 2022, Online Streaming, United Kingdom. ⟨halshs-03606527⟩

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