Towards a Machine Learning flow-predicting ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Towards a Machine Learning flow-predicting model in a MOOC context
Auteur(s) :
Ramirez Luelmo, Sergio Ivan [Auteur]
Trigone-CIREL
El Mawas, Nour [Auteur]
Trigone-CIREL
Bachelet, Rémi [Auteur]
Centrale Lille
Heutte, Jean [Auteur]
Trigone-CIREL
Trigone-CIREL
El Mawas, Nour [Auteur]
Trigone-CIREL
Bachelet, Rémi [Auteur]
Centrale Lille
Heutte, Jean [Auteur]
Trigone-CIREL
Titre de la manifestation scientifique :
14th International Conference on Computer Supported Education (CSEDU 2022)
Ville :
Online Streaming
Pays :
Royaume-Uni
Date de début de la manifestation scientifique :
2022-04-22
Mot(s)-clé(s) en anglais :
MOOC
Flow
Autotelic experience
Machine Learning
Logistic Regression
Flow
Autotelic experience
Machine Learning
Logistic Regression
Discipline(s) HAL :
Sciences de l'Homme et Société/Psychologie
Sciences de l'Homme et Société/Education
Sciences de l'Homme et Société/Education
Résumé en anglais : [en]
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 ...
Lire la suite >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).Lire moins >
Lire la suite >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).Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Source :