The Intellectual Structure of Learning Analytics Research: A Bibliometric and Science Mapping Analysis
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This study explores the intellectual structure of learning analytics (LA) research through a bibliometric and science mapping analysis. Over the past decade, LA has gained significant importance due to its role in analyzing learning behaviors through big data, artificial intelligence, and online learning systems, especially during the COVID-19 pandemic. Using data from Scopus and Web of Science, this study examines trends in publication volume, research themes, international collaborations, and influential authors and institutions within the field from 2020 to 2025. The results reveal key research domains such as educational data mining, MOOCs, self-regulated learning, and AI-integrated adaptive learning systems. Additionally, the analysis uncovers evolving research trends, particularly a shift from MOOCs to learning management systems and AI applications in post-pandemic education. By applying co-citation and co-word analysis, the study identifies emerging themes and their relationships, offering a comprehensive visualization of the field’s intellectual network. The findings also highlight the need for future research on AI-driven learning analytics and ethical considerations. This study contributes to a better understanding of LA's conceptual evolution and offers insights to policymakers, educational institutions, and researchers, helping guide future directions for digital education and data-driven educational policies. The results align with global objectives such as Sustainable Development Goal 4 (quality education for all), promoting the integration of learning analytics to enhance educational quality and inclusivity worldwide.
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