Mapping the Integration of Adaptive Learning and Learning Analytics in Education: A Bibliometric Study (2004 – 2025)

Abstract

This study presents a bibliometric analysis of research on the integration of adaptive learning and learning analytics in education from 2004 to 2025, based on 1,850 publications. Using publication and citation analysis, collaboration networks, keyword co-occurrence, and thematic evolution analysis, the study examines the development of this research area, its main contributors, research themes, and patterns of growth. The findings show a steady increase in publications and citation impact, reflecting growing interest in data-driven approaches and the increasing use of artificial intelligence in education. However, the high proportion of conference papers and the relatively small number of review studies suggest that this area is still developing, with rapid growth occurring faster than the development of a more structured knowledge base. Citation and authorship analysis indicates that research influence is concentrated among a relatively small group of active authors and institutions, while collaboration patterns highlight the strong role of the United States alongside increasing but uneven international participation. Thematic evolution analysis shows a shift from early research on intelligent tutoring systems and student modelling to more data-focused and learner-centred approaches, including learning analytics, machine learning, and Bayesian modelling, with growing attention in recent studies to multimodal learning analytics and explainable artificial intelligence. Overall, adaptive learning and learning analytics form an increasingly connected and interdisciplinary research area in education, although knowledge production remains concentrated in specific regions and research groups, indicating the need for broader collaboration and further theoretical development.