Authors:
Sergio Iván Iván Ramírez Luelmo
1
;
Nour El Mawas
1
;
Rémi Bachelet
2
and
Jean Heutte
1
Affiliations:
1
CIREL - Centre Interuniversitaire de Recherche en Éducation de Lille, Université de Lille, Campus Cité Scientifique, Bâtiments B5 – B6, Villeneuve d’Ascq, France
;
2
Centrale Lille, Université Lille Nord de France, Cité Scientifique, Villeneuve d’Ascq, France
Keyword(s):
MOOC, Flow, Autotelic Experience, Machine Learning, Logistic Regression.
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, because of its online, distant, asynchronous, and educational components. In such context, flow prediction would allow 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 (0.7
4).
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