Emerging Trends in Self-Regulated Learning: A Bibliometric Analysis of MOOCs and AI-Enhanced Online Learning (2014–2024)

Yanxing Xue, Fariza Binti Khalid, Aidah Binti Abdul Karim

Abstract


Low levels of student engagement remain a challenge in online learning. However, in the post-pandemic era, the rapid evolution of online education has positioned self-regulated learning (SRL) as a critical determinant of learner success, particularly within massive open online courses (MOOCs) and artificial intelligence (AI) enhanced platforms. The objectives of this study are to synthesize the existing literature, identify research gaps, and propose new directions for advancing SRL in online learning environments. This bibliometric analysis examines the current state of research on SRL in online learning environments, particularly focusing on MOOCs and AI-enhanced platforms from 2014 to 2024. Using bibliometric methods, this study analysed 42 relevant articles retrieved from major academic databases, including Web of Science, Scopus, and ScienceDirect. This review identifies a substantial increase in research interest in SRL following the COVID-19 pandemic, focusing on cognitive and metacognitive strategies, while emotional and motivational aspects remain underexplored. Research methodologies used to support SRL, such as questionnaires and observational studies, were evaluated with AI tools, demonstrating the potential to enhance goal setting, self-monitoring, and time management. However, the scalability and long-term effectiveness of these tools remain under-researched. This review also highlights key issues such as the early-stage integration of AI in SRL research. This emphasizes the need for scalable AI-driven tools and comprehensive evaluation systems to better understand and optimize the effectiveness of SRL interventions in online learning environments.

https://doi.org/10.26803/ijlter.24.1.21


Keywords


self-regulated learning; online learning; massive open online courses; artificial intelligence

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References


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