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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 standard 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.  \nKeywords: adaptive practicing, confidence-based assessment, knowledge tracing, question sequencing, self-regulated learning, wheel-spinning  \nRésumé  \nL’enseignement supérieur en ligne offre une flexibilité exceptionnelle dans l’apprentissage, maisil exige des compétences élevées en termes d’apprentissage autorégulé . Le manque de compétences  \nCJLT/RCAT Vol. 50 (3)  \nd’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 dedéveloppement 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.  \nMots-clés : apprentissage autorégulé, évaluation basée sur la confiance, pratique adaptative, rouet, séquenc","cbCaiuIOjtN6Q1ra","https://ap.wps.com/l/cbCaiuIOjtN6Q1ra","pdf",647066,22,"English","# Introduction\n## Background on online self-regulated learning\n## Need for support in self-paced STEM learning\n## Rationale for adaptive practicing and confidence-based design","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study targets the lack of self-regulated learning skills among many students in online higher education, especially in self-paced STEM contexts.\"},{\"question\":\"How does the proposed system differ from conventional intelligent tutoring?\",\"answer\":\"It uses confidence-based adaptive practicing that integrates learner confidence and control options into an AI adaptive mechanism, forming an AI-learner shared control approach.\"},{\"question\":\"What is ZPD-KT and what evidence supports its effectiveness?\",\"answer\":\"ZPD-KT is a knowledge tracing framework designed based on Vygotsky’s zone of proximal development concept. Simulation results show improved knowledge tracing accuracy over standard Bayesian Knowledge Tracing, supported by expert interviews.\"}]","Adaptive Practicing Design to Facilitate Self-Regulated Learning - Volume 50 - Fall / Automne 2024 | PDF",1792086994,55]