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Article Dans Une Revue Computers in Human Behavior Année : 2022

How do online learning intentions lead to learning outcomes? The mediating effect of the autotelic dimension of flow in a MOOC

Rawad Chaker
Rémi Bachelet
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Résumé

We focus on the predictors of persistence and achievement in online learning by studying the students' learning intentions and their psychological states during learning activities. Flow/autotelic experience is a powerful predictor of engagement in MOOCs and online learning in general and relates to the deep involvement and sense of absorption during learning activities. Both theory and empirical evidence propose that predictors of flow in an educational setting include the need for belonging to a group of learners. Using path analyses and structural equation modeling, we verify the causal links between social intentions, autotelic experience and MOOC learning outcomes such as final grade and dropout. Using the Online Learning Enrollment Intentions (OLEI) scale, we find that in total six OLEI items predict MOOC success and dropout, with flow as a mediating effect. In two models, we verify "Autotelic experience" as a mediator between enrollment intentions and MOOC final grade and dropout. Our results highlight socially driven intentions as major factors to be considered in online learning environments. We draw theoretical and practical implications for MOOC design, considering explicit communication about the provided learning environment and tools towards a socially shared learning experience.
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Dates et versions

hal-03701540 , version 1 (22-06-2022)

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Rawad Chaker, François Bouchet, Rémi Bachelet. How do online learning intentions lead to learning outcomes? The mediating effect of the autotelic dimension of flow in a MOOC. Computers in Human Behavior, 2022, 134, pp.107306. ⟨10.1016/j.chb.2022.107306⟩. ⟨hal-03701540⟩
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