The 20-Year Evolution of Distance Education: A Bibliometric and LDA-Based Topic Model Analysis

Although research on distance education has increased substantially in recent years, comprehensive analyses capturing the overall thematic development of the field remain limited. Modeling studies are necessary to address this gap and to uncover long-term research trends. This study aimed to reveal the trends of research conducted in the field of distance education in the last two decades by using machine learning methodology and bibliometric analysis. The unique aspect of the study was its use of the Latent Dirichlet Allocation (LDA) algorithm, a machine learning method for analysing large-scale data. Within the scope of the study, 54,444 articles in the Web of Science database were analysed. Bibliometric analysis revealed that distance education had gained significant momentum, especially in the post-pandemic period, with a wide range of applications in different disciplines such as education, management, and health. As a result of LDA analysis, 19 thematic topics were identified. Among these, digitalization, artificial intelligence, Web-based learning, and social interaction and collaboration stood out. Time series analysis showed an increasing trend for topics such as artificial intelligence and emergency distance education over the years, with less interest shown in topics such as Web-based learning and program design. The study emphasized that distance education is in a technology-driven transformation process and pointed to areas that will offer new opportunities for researchers. Furthermore, recommendations for decision-makers, such as investments in digital infrastructure, integration of artificial intelligence-based systems, and the establishment of quality standards, also emerged.