Recommendation Systems in Education : A Bibliometric Analysis

In today's digital age, recommendation systems have become an important tool in many sectors, including education. This study presents a comprehensive bibliometric analysis of the literature related to recommendation systems in an educational context. Using major scientific databases and bibliometric analysis tools, we evaluate publications from the last 5 years to identify research trends, collaboration patterns, and methodological developments. Our results show a significant increase in the number of publications and interest in this field, with a wide variety of research themes ranging from personalized learning to optimization of educational resources. The most dominant analytics techniques include collaborative filtering and content-based filtering, while hybrid and deep learning approaches show growing potential. In addition, we identified geographic and institutional concentrations of research, as well as a set of challenges such as data privacy and the need for adaptation to learner dynamics. This research underscores the importance of recommendation systems in improving tailored educational experiences and supporting data-driven academic decisions. We also offer insights for future research directions, including the need for interdisciplinary research and closer international collaboration.