Digital Learning Dynamics: A Bibliometric Exploration of Research Trends, Thematic Clusters and Collaborations

Abstract

This bibliometric study analyzes the dynamic landscape of digital learning research from 2013 to 2023, examining global trends, thematic clusters, intellectual structure, and collaborations. Using bibliometric techniques on 292 high-impact journal articles, the study revealed sustained research interest on digital learning, accelerated by COVID-19. Key trends include artificial intelligence, gamification, learning analytics, and personalized learning, indicating a shift towards learner-centered and data-driven approaches. Factorial analysis identified four thematic clusters: collaborative constructivist learning, learning analytics and data mining, learner motivation and engagement, and personalized virtual environments. Co-citation analysis further revealed  the enduring influence of foundational frameworks like self-regulated learning, social constructivism, and technology acceptance. The study exposes disparities in research output and collaborations across world regions. The findings provide insights for researchers, educators, and policymakers navigating digital learning's future. Future research should prioritize inclusive global perspectives, ethical considerations, and empirical validation of emerging technologies. This study contributes a knowledge base to advance theory and practice in digital learning, emphasizing interdisciplinary and contextually-sensitive approaches to address evolving challenges and harness technological possibilities.