Conception d'une pratique adaptative pour faciliter l'apprentissage autorégulé
DOI :
https://doi.org/10.21432/cjlt28768Mots-clés :
Apprentissage autorégulé, Évaluation basée sur la confiance, pratique adaptative, rouet, séquence de questions, traçage des connaissancesRésumé
L’enseignement supérieur en ligne offre une flexibilité exceptionnelle dans l’apprentissage, mais il exige des compétences élevées en termes d’apprentissage autorégulé. Le manque de compétences d’apprentissage autorégulé chez de nombreuses personnes étudiantes met en évidence la nécessité du soutien. Cette étude présente un système de pratique adaptative basé sur la confiance en tant que solution intelligente d’évaluation et de tutorat pour améliorer l’apprentissage autorégulée dans les disciplines STIM. Contrairement aux systèmes de tutorat intelligents conventionnels qui dépendent entièrement du contrôle de la machine, la pratique adaptative basée sur la confiance intègre la confiance de la personne apprenante et les options de contrôle dans le mécanisme adaptatif basé sur l’intelligence artificielle (IA) pour améliorer l’autonomie d’apprentissage et l’efficacité du modèle, établissant ainsi une approche de contrôle partagé entre l’IA et la personne apprenante. Basés sur le concept de zone de développement proximal de Vygotsky (ZPD), un cadre et un modèle innovant de traçage des connaissances appelé ZPD-KT ont été conçus et mis en œuvre dans le système de pratique adaptative basé sur la confiance. Pour évaluer l’efficacité du modèle ZPD-KT, une simulation de pratique adaptative basée sur la confiance a été effectuée. Les résultats ont démontré que le modèle ZPD-KT a considérablement amélioré la précision de la traçabilité des connaissances par rapport au modèle traditionnel de traçage des connaissances bayésiennes. De plus, les entrevues avec des experts dans le domaine ont souligné le potentiel du système de pratique adaptative pour faciliter l’apprentissage autorégulé et l’interprétabilité du modèle ZPD-KT. Cette étude a également mis en lumière une nouvelle façon de tenir les humains informés de la mise en œuvre de l’apprentissage adaptatif.
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© Hongxin Yan, Fuhua Lin, Kinshuk 2025
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