Supprimer les obstacles à l'apprentissage dans l'enseignement asynchrone en ligne des STIM

Auteurs-es

DOI :

https://doi.org/10.21432/cjlt28264

Mots-clés :

barrière d'apprentissage, pratique adaptative, traçage des connaissances, séquencement des exercices, apprentissage par renforcement, apprentissage en ligne asynchrone (à son rythme)

Résumé

            L'apprentissage en ligne asynchrone (à son propre rythme) offre une grande souplesse d'apprentissage, mais il comporte des obstacles à l'apprentissage inhérents à la nature de ce paradigme éducatif. Cet article de revue suggère quelques stratégies pertinentes permettant de répondre à ces obstacles afin de créer un environnement d'apprentissage en ligne asynchrone plus favorable. Ces stratégies comprennent a) l'augmentation de la conscience de l'apprentissage chez les étudiants, b) l'identification des étudiants en difficulté et c) la facilitation de la pédagogie de la maîtrise. En se concentrant sur la dispensation de l'apprentissage en ligne asynchrone dans les disciplines des sciences, de la technologie, de l'ingénierie et des mathématiques (STIM), cet article examine le rôle de l'évaluation formative pour l'apprentissage. Il est proposé que la conception et l'intégration systématiques de pratiques adaptatives dans les cours STIM constituerait une solution efficace de conception de l'apprentissage pour mettre en œuvre ces stratégies. En examinant les objectifs et le contexte de la pratique adaptative demandés dans cette étude, les exigences en matière de fonctionnalités sont décrites pour un tel modèle de pratique adaptative. Les modèles et les techniques qui peuvent être utilisés pour l'évaluation adaptative ont ensuite été examinés. Sur la base des résultats de cette revue, cet article soutient qu'un modèle de pratique adaptative basé sur l'apprentissage par renforcement serait la meilleure option pour répondre à ces exigences. Enfin, nous soulignons les insuffisances de la recherche dans ce domaine et suggérons une direction de recherche future pour nous-mêmes et pour d'autres chercheurs.

Bibliographies de l'auteur-e

Hongxin Yan, University of Eastern Finland

Hongxin Yan is a doctoral student at the School of Computing, University of Eastern Finland, Finland and works as a Learning Designer in the Faculty of Science and Technology, Athabasca University, Canada. His research interests include personalized and adaptive learning in online education.

Fuhua Lin, Athabasca University

Dr. Fuhua Lin is Professor and former Chair for the School of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Canada. He is the Coordinator of Intelligent Systems and Machine Learning research cluster of Athabasca University and a senior member of IEEE and ACM. 

Kinshuk, University of North Texas

Dr. Kinshuk is the Dean of the College of Information at the University of North Texas. Prior to that, he held the NSERC/CNRL/Xerox/McGraw Hill Research Chair for Adaptivity and Personalization in Informatics, funded by the Federal government of Canada, Provincial government of Alberta, and by national and international industries.

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Publié-e

2022-11-29

Numéro

Vol. 48 No. 4 (2022): Special Issue

Rubrique

Articles

Licence

© Hongxin Yan, Fuhua Lin, Kinshuk 2022

Licence Creative Commons

Cette œuvre est sous licence Creative Commons Attribution - Pas d'Utilisation Commerciale 4.0 International.

Droits d’auteur

Les auteurs conservent le droit d'auteur et accordent le droit de la première publication de la revue avec le travail simultanément sous une licence Creative Commons Attribution - Pas d’Utilisation Commerciale 4.0 International (CC-BY-NC 4.0) qui permet aux autres de partager le travail avec une reconnaissance de la paternité de l'œuvre et la publication initiale dans ce journal.