Mapping the Knowledge Structure and Emerging Trends of Adaptive Learning and Learning Style Research
DOI:
https://doi.org/10.12928/jimp.v5i2.14534Keywords:
Adaptive learning, bibliometric analysis, co-occurrence analysis, Learning style, research trendsAbstract
This study aims to analyze publication trends, conceptual network structures, and key theme clusters in adaptive learning and learning style research in the Social Sciences field from 2016 to 2025 using a bibliometric approach. Data were obtained from the Scopus database using VOSviewer software and OpenRefine for visualization and analysis. The results show a significant upward trend in publications, particularly in 2020–2024, with a peak of 78 documents in 2024, triggered by the digital transformation of education due to the COVID-19 pandemic. The conceptual network structure reveals learning style as a central node connecting various research domains, with a temporal evolution from a descriptive-theoretical to an implementative-technological paradigm. Cluster analysis identified eight main themes dominated by psychological-individual aspects (21 items), adaptive-personalization systems (20 items), learning technology (19 items), and AI-based management (17 items). These findings confirm that adaptive learning and learning style research have evolved from conceptual exploration to intelligent technology implementation, with learning style as the main foundation. This study contributes by mapping the evolution of the theme and identifying potential areas for future research, particularly in AI-based learning personalization.
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