Towards a Machine Learning flow-predicting model in a MOOC context
| Autor: | Sergio Ramírez Luelmo, Nour El Mawas, Rémi Bachelet, Jean Heutte |
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| Přispěvatelé: | Trigone-CIREL, Centre Interuniversitaire de Recherche en Education de Lille - ULR 4354 (CIREL), Université de Lille-Université de Lille, Centrale Lille |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 14th International Conference on Computer Supported Education (CSEDU 2022) 14th International Conference on Computer Supported Education (CSEDU 2022), Apr 2022, Online Streaming, United Kingdom |
| Popis: | International audience; 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). |
| Databáze: | OpenAIRE |
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