Systematic Review of Self-Regulated Learning with Blended Learning in Digital Space

• Published in: IEEE access Vol. 12; p. 1
• Main Authors: Faathima Fayaza, M.S., Ahangama, Supunmali
• Format: Journal Article
• Language: English
• Published: IEEE 01.01.2024
• Subjects: Adaptation models; Analytical models; Blended learning; Digital Space; Education; Electronic learning; Faces; Hybrid learning; Instruments; Online services; Reflection; Regulation; Self-Regulated Learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3470114

Technology enhancements introduce novel learning modes by replacing traditional face-to-face learning. Blended learning (BL) has emerged as the new normal, combining face-to-face instructions and online elements. Successful BL requires a high self-regulation capability among the learners. Technology can enhance these Self-Regulated Learning (SRL) capabilities. This paper reviews 66 papers published in the Scopus, IEEE Xplore, Google Scholar, and ScienceDirect databases between 2016 and 2024 (till March) using the PRISMA model. As per the review, self-reported data using a questionnaire is the most used mechanism to collect data on users and most of the studies used university undergraduates as a learner group. Very few researchers work collaboratively, though enhancement of the collaborative work will bring better outcomes. In general, SRL positively impacts learning outcomes in the BL context. Cognitive, metacognitive, motivational, and resource management strategies enhance learning, and emotional engagement is enhanced by the user interface of learning platforms. However, some strategies do not have an impact on the BL environment. This study suggests the importance of understanding the long-term impact of SRL and how different strategies impact the learning outcomes and their subsequent performance. Also, it is necessary to apply the different SRL strategies to different contexts to generalize the findings.