A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs

MOOCs have great potential to innovate education, but lack of personalization. In this paper, we show how FUMA, a data-driven framework for student modeling and adaptation, can help understand how to provide personalized support to MOOCs students, specifically targeting video watching behaviors. We...

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Detalles Bibliográficos
Autores principales: Lallé, Sébastien, Conati, Cristina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334175/
http://dx.doi.org/10.1007/978-3-030-52237-7_23
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author Lallé, Sébastien
Conati, Cristina
author_facet Lallé, Sébastien
Conati, Cristina
author_sort Lallé, Sébastien
collection PubMed
description MOOCs have great potential to innovate education, but lack of personalization. In this paper, we show how FUMA, a data-driven framework for student modeling and adaptation, can help understand how to provide personalized support to MOOCs students, specifically targeting video watching behaviors. We apply FUMA across several MOOCs to show how to: (i) discover video watching behaviors that can be detrimental for or conductive to learning; (ii) use these behaviors to detect ineffective learners at different weeks of MOOCs usage. We discuss how these behaviors can be used to define personalized support to effective MOOC video usage regardless of the target course.
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spelling pubmed-73341752020-07-06 A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs Lallé, Sébastien Conati, Cristina Artificial Intelligence in Education Article MOOCs have great potential to innovate education, but lack of personalization. In this paper, we show how FUMA, a data-driven framework for student modeling and adaptation, can help understand how to provide personalized support to MOOCs students, specifically targeting video watching behaviors. We apply FUMA across several MOOCs to show how to: (i) discover video watching behaviors that can be detrimental for or conductive to learning; (ii) use these behaviors to detect ineffective learners at different weeks of MOOCs usage. We discuss how these behaviors can be used to define personalized support to effective MOOC video usage regardless of the target course. 2020-06-09 /pmc/articles/PMC7334175/ http://dx.doi.org/10.1007/978-3-030-52237-7_23 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Lallé, Sébastien
Conati, Cristina
A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs
title A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs
title_full A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs
title_fullStr A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs
title_full_unstemmed A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs
title_short A Data-Driven Student Model to Provide Adaptive Support During Video Watching Across MOOCs
title_sort data-driven student model to provide adaptive support during video watching across moocs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334175/
http://dx.doi.org/10.1007/978-3-030-52237-7_23
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