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A Bibliometric Analysis of Research on Dropout in Open and Distance Learning
Summary
This bibliometric analysis maps research trends on student dropout in open and distance learning, finding a shift from traditional retention concepts toward data-driven approaches like learning analytics, educational data mining, and AI-based prediction. While not directly about microplastics, it illustrates how bibliometric methods can track the evolution of research fields.
The purpose of this study is to examine research on school dropout in open and distance education in the Web of Science (WoS) database using bibliometric analysis and to reveal trends in this area. In line with this goal, a total of 1,615 studies published between 1980 and 2022 were identified in the Web of Science (WoS) indexes. Descriptive and evaluative bibliometric methods were employed in the analysis of these publications, and the results were visualized using VOSviewer software. According to the research findings, studies on school dropout in open and distance education intensified in 2019. The analysis revealed that E.T. Pascarella is the most cited author, and F.D. Pereira has the highest co-authorship network. An examination based on institutions showed that the Universitat Autònoma de Barcelona has the highest number of publications, while the University of Michigan ranks first in terms of citation numbers. Furthermore, in terms of publication productivity and citation numbers, the United States is ranked first, followed by Spain. Looking at the keywords used in articles on the topic, early studies emphasized concepts such as “distance education”, “student retention”, “attrition”, “student success”, “social integration”, “academic integration”, “survival analysis”, “education policy”, “graduation”, and “financial aids”. In subsequent years, these were replaced by terms such as “learning analytics”, “educational data mining”, “systematic review”, “engagement”, “drop prediction”, “decision tree”, “student performance”, “gamification”, “massive open online course- MOOC”, and “artificial intelligence”. In this context, discussions were conducted within the framework of the literature, and various recommendations were provided based on the obtained findings.
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