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]
Cognitions Humaine et ARTificielle [CHART]
Institut de Recherche Biomédicale des Armées [Brétigny-sur-Orge] [IRBA]
Maladies chroniques, santé perçue, et processus d'adaptation [APEMAC]
École de Psychologues Praticiens [EPP]
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

HAL domain(s) :

Sciences de l'Homme et Société/Education
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 >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

ULNE

Collections :

• Centre Interuniversitaire de Recherche en Éducation de Lille (CIREL) - ULR 4354

Source :

Harvested from HAL