Learning Analytics in Medical and Health Professions Education: A Bibliometric Mapping of Research Structure and Thematic Patterns (2014–2026)
A Bibliometric Mapping of Research Structure and Thematic Patterns (2014–2026)
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Abstract
This study aimed to map the development, intellectual structure, and thematic patterns of learning analytics research in medical and health professions education. A bibliometric and science-mapping analysis was conducted using 177 publications indexed in the Web of Science Core Collection between 2014 and 2026. Data were analysed with the Bibliometrix package and its Biblioshiny interface. The analysis examined annual scientific production, source distribution, author productivity, country-level production, three-field relationships, keyword co-occurrence, and thematic structure. The results indicated rapid growth in publication output, particularly after 2021, with a marked increase from 2024 onwards. Publications were distributed across established medical education and health professions education journals, suggesting growing bibliometric visibility within the field. Authorship and Lotka’s Law patterns indicated a collaborative but unevenly consolidated research community. The thematic structure showed visible associations among learning analytics, feedback, performance, competence, technology, artificial intelligence, big data, educational data mining, and machine learning. By contrast, programmatic assessment, workplace-based assessment, and entrustable professional activities appeared less central in the mapped literature. These findings should be interpreted as patterns in Web of Science-indexed publications rather than direct evidence of educational effectiveness or implementation quality. The study contributes by organising the field’s publication structure and identifying areas where future research could connect learning analytics more explicitly with longitudinal, workplace-based, and curriculum-level assessment practices.
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