Towards a Machine Learning Flow-predicting Model in a MOOC Context

Sergio Iván Ramírez Luelmo, Nour El Mawas, Rémi Bachelet, Jean Heutte

2022

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 (0.74).

Paper Citation

in Harvard Style

Ramírez Luelmo S., El Mawas N., Bachelet R. and Heutte J. (2022). Towards a Machine Learning Flow-predicting Model in a MOOC Context. In Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-562-3, pages 124-134. DOI: 10.5220/0011070300003182

in Bibtex Style

@conference{csedu22,
author={Sergio Ramírez Luelmo and Nour El Mawas and Rémi Bachelet and Jean Heutte},
title={Towards a Machine Learning Flow-predicting Model in a MOOC Context},
booktitle={Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2022},
pages={124-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011070300003182},
isbn={978-989-758-562-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Towards a Machine Learning Flow-predicting Model in a MOOC Context
SN - 978-989-758-562-3
AU - Ramírez Luelmo S.
AU - El Mawas N.
AU - Bachelet R.
AU - Heutte J.
PY - 2022
SP - 124
EP - 134
DO - 10.5220/0011070300003182