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Article Dans Une Revue SN Computer Science Année : 2024

Detection and Asynchronous Flow Prediction in a MOOC

Nour El Mawas
Rémi Bachelet
Jean Heutte

Résumé

Flow is a human psychological state positively correlated to self-efficacy, motivation, engagement, and academic achievement. In a MOOC, flow detection and prediction would potentially allow for learners’ content personalization, fostering engagement and increasing already-low completion rates. In this study, we propose a Machine Learning flow-predicting model by pairing the results of the EduFlow-2 and Flow-Q measure instruments issued to participants of a MOOC (n = 1589, 2-year data collection). The resulting flow-predicting-model detects flow in an automatic, asynchronous fashion by applying only the EduFlow-2 measurement instrument. Our model proposal predicts flow presence with greater precision than it detects flow absence.
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Dates et versions

halshs-04611847 , version 1 (14-06-2024)

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Sergio Iván Ramírez Luelmo, Nour El Mawas, Rémi Bachelet, Jean Heutte. Detection and Asynchronous Flow Prediction in a MOOC. SN Computer Science, 2024, 5, pp.[En ligne]. ⟨10.1007/s42979-024-02838-w⟩. ⟨halshs-04611847⟩
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