Searching concordance between two measurement ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Searching concordance between two measurement tools (EduFlow-2 and FlowQ): Proposal for Flow State Method Detection in Educational and Training Contexts
Author(s) :
Heutte, Jean [Auteur]
Trigone-CIREL
Ramirez Luelmo, Sergio Ivan [Auteur]
Trigone-CIREL
Bachelet, Rémi [Auteur]
Centrale Lille
El Mawas, Nour [Auteur]
Centre de Recherche sur les Médiations [Crem]
Martin-Krumm, Charles [Auteur]
École de Psychologues Praticiens [EPP]
Maladies chroniques, santé perçue, et processus d'adaptation [APEMAC]
Institut de Recherche Biomédicale des Armées [Brétigny-sur-Orge] [IRBA]
Cognitions Humaine et ARTificielle [CHART]
Fenouillet, Fabien [Auteur]
Laboratoire Interdisciplinaire en Neurosciences, Physiologie et psychologie [LINP2]
Trigone-CIREL
Ramirez Luelmo, Sergio Ivan [Auteur]
Trigone-CIREL
Bachelet, Rémi [Auteur]
Centrale Lille
El Mawas, Nour [Auteur]
Centre de Recherche sur les Médiations [Crem]
Martin-Krumm, Charles [Auteur]
École de Psychologues Praticiens [EPP]
Maladies chroniques, santé perçue, et processus d'adaptation [APEMAC]
Institut de Recherche Biomédicale des Armées [Brétigny-sur-Orge] [IRBA]
Cognitions Humaine et ARTificielle [CHART]
Fenouillet, Fabien [Auteur]
Laboratoire Interdisciplinaire en Neurosciences, Physiologie et psychologie [LINP2]
Conference title :
11th European Conference on Positive Psychology (ECPP 2024)
City :
Innsbruck
Country :
Autriche
Start date of the conference :
2024-07-10
English keyword(s) :
Autotelism-flow
Machine learning
Assesment
Machine learning
Assesment
HAL domain(s) :
Sciences de l'Homme et Société/Education
Informatique [cs]/Environnements Informatiques pour l'Apprentissage Humain
Sciences cognitives/Psychologie
Informatique [cs]/Environnements Informatiques pour l'Apprentissage Humain
Sciences cognitives/Psychologie
English abstract : [en]
BackgroundMost Flow measurement tools have mainly demonstrated their usefulness for the scientific study of variations in optimal experience. However, with the exception of the Flow Questionnaire (FlowQ, Csikszentmihalyi, ...
Show more >BackgroundMost Flow measurement tools have mainly demonstrated their usefulness for the scientific study of variations in optimal experience. However, with the exception of the Flow Questionnaire (FlowQ, Csikszentmihalyi, 1975, 1982), very few of them are really able to reveal flow state as a cut-off point.AimsIdentify a method closest to concordance with FlowQ, specifically adapted for training contexts.Method-Data collection (n = 1729) using the flow in education scale (EduFlow-2, Heutte et al., 2021), including 4 sub-dimensions (Cognitive control (D1); Immersion & Time Transformation (D2); Loss of self-consciousness (D3); Autotelic experience (D4)), and the 3 items of FlowQ. -3 Structural equations modelling (SEM1, SEM2, SEM3) with a 2nd order factor based on the EduFlow-2 data. -Establishment of 3 formulas (F1, F2, F3) taking into account the weight of the first-order factors on the 2nd-order factor (Flow state (FS)) to establish 3 scores (SF1, SF2, SF3) per individual. -F1: based on SEM1 factors = 4 sub-dimensions of EduFlow-2 connected on FS -F2: based on Kawabata and Mallett’s (2011) model, which distinguishes flow conditions from flow state, and SEM2 factors = only D2, D3 and D4 connected on FS. -F3: based on SEM3 factors = 4 sub-dimensions of EduFlow-2, but only D2, D3 and D4 connected on FS.ResultsThese 3 methods allow flow state detection in concordance with FlowQ, as follows: 68,5% (F1), 71,9% (F2), 71,7% (F3) DiscussionMethodological investigations need to be continued, but these first results open up new research prospects, particularly in the field of lifelong learning.Show less >
Show more >BackgroundMost Flow measurement tools have mainly demonstrated their usefulness for the scientific study of variations in optimal experience. However, with the exception of the Flow Questionnaire (FlowQ, Csikszentmihalyi, 1975, 1982), very few of them are really able to reveal flow state as a cut-off point.AimsIdentify a method closest to concordance with FlowQ, specifically adapted for training contexts.Method-Data collection (n = 1729) using the flow in education scale (EduFlow-2, Heutte et al., 2021), including 4 sub-dimensions (Cognitive control (D1); Immersion & Time Transformation (D2); Loss of self-consciousness (D3); Autotelic experience (D4)), and the 3 items of FlowQ. -3 Structural equations modelling (SEM1, SEM2, SEM3) with a 2nd order factor based on the EduFlow-2 data. -Establishment of 3 formulas (F1, F2, F3) taking into account the weight of the first-order factors on the 2nd-order factor (Flow state (FS)) to establish 3 scores (SF1, SF2, SF3) per individual. -F1: based on SEM1 factors = 4 sub-dimensions of EduFlow-2 connected on FS -F2: based on Kawabata and Mallett’s (2011) model, which distinguishes flow conditions from flow state, and SEM2 factors = only D2, D3 and D4 connected on FS. -F3: based on SEM3 factors = 4 sub-dimensions of EduFlow-2, but only D2, D3 and D4 connected on FS.ResultsThese 3 methods allow flow state detection in concordance with FlowQ, as follows: 68,5% (F1), 71,9% (F2), 71,7% (F3) DiscussionMethodological investigations need to be continued, but these first results open up new research prospects, particularly in the field of lifelong learning.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Source :