Résumés
Abstract
Online higher education provides exceptional flexibility in learning but demands high self-regulated learning skills. The deficiency of self-regulated learning skills in many students highlights the need for support. This study introduces a confidence-based adaptive practicing system as an intelligent assessment and tutoring solution to enhance self-regulated learning in STEM disciplines. Unlike conventional intelligent tutoring systems that depend entirely on machine control, confidence-based adaptive practicing integrates learner confidence and control options into the AI-based adaptive mechanism to improve learning autonomy and model efficiency, establishing an AI-learner shared control approach. Based on Vygotsky’s zone of proximal development (ZPD) concept, an innovative knowledge-tracing framework and model called ZPD-KT was designed and implemented in the confidence-based adaptive practicing system. To evaluate the effectiveness of the ZPD-KT model, a simulation of confidence-based adaptive practicing was conducted. Findings showed that ZPD-KT significantly improves the accuracy of knowledge tracing compared to the traditional Bayesian knowledge-tracing model. Also, interviews with experts in the field underlined the potential of the confidence-based adaptive practicing system in facilitating self-regulated learning and the interpretability of the ZPD-KT model. This study also sheds light on a new way of keeping humans apprised of adaptive learning implementation.
Keywords:
- adaptive practicing,
- confidence-based assessment,
- knowledge tracing,
- question sequencing,
- self-regulated learning,
- wheel-spinning
Résumé
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 oeuvre de l’apprentissage adaptatif.
Mots-clés :
- Apprentissage autorégulé,
- Évaluation basée sur la confiance,
- pratique adaptative,
- rouet,
- séquence de questions,
- traçage des connaissances
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Parties annexes
Biographical notes
Hongxin Yan is a Learning Designer at Athabasca University in Alberta, Canada and a Doctoral Student at the University of Eastern Finland (UEF). His research interests include adaptive and personalized learning, artificial intelligence (AI) in education, learning analytics, and related fields. Email: hongya@student.uef.fi ORCID: 0000-0002-3729-0844
Fuhua Lin is a Professor in the Faculty of Science and Technology at Athabasca University in Alberta, Canada. His research focuses on adaptive learning systems, artificial intelligence in education, and virtual reality applications for training. He has led multiple NSERC/CFI/Alberta Innovates-funded projects to advance personalized learning technologies. Email: oscarl@athabascau.ca
Kinshuk is a full Professor and the Dean of the College of Information at the University of North Texas, USA. His research interests include learning analytics, mobile learning, ubiquitous learning, personalized learning, and adaptivity. Email: kinshuk@ieee.org
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