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Supporting Self-Regulation Learning Using a Bayesian Approach. Some Preliminary Insights

Fahima Djelil 1, 2 Jean-Marie Gilliot 1, 2 Serge Garlatti 1, 2 Philippe Leray 3 
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance : UMR6285
Abstract : Self-Regulated Learning (SRL) is actually challenging modern online environments (e-learning platforms, MOOCs, exercise-based platforms, ...). A large emergent literature points out the need for empirical studies on approaches that can help to build tools for measuring and scaffolding SRL. This paper is a state of the art aiming to identify a set of key avenues to conduct a future experimental research on this theme. More importantly, we present the approaches of Open Learner Models, and knowledge Tracing through Bayesian Networks that offer promising insights to model student knowledge, measure SRL levels and provide appropriate interventions to foster student SRL skills.
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Submitted on : Thursday, August 26, 2021 - 10:10:17 AM
Last modification on : Thursday, November 17, 2022 - 4:48:10 PM
Long-term archiving on: : Saturday, November 27, 2021 - 6:19:19 PM


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  • HAL Id : hal-03325733, version 1


Fahima Djelil, Jean-Marie Gilliot, Serge Garlatti, Philippe Leray. Supporting Self-Regulation Learning Using a Bayesian Approach. Some Preliminary Insights. International Joint Conference on Artificial Intelligence IJCAI-21, Workshop Artificial Intelligence for Education, Aug 2021, Montreal (virtual), Canada. ⟨hal-03325733⟩



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