Educational Data Mining: A Bibliometric Analysis of an Emerging Field

We are now able to collect enormous amounts of information at the learner level. Mining educational data to provide data-driven analytics has spurred great interest among researchers and policymakers that continues to grow. This growing research area is called educational data mining (EDM). Yet the growing interest in the topic has also resulted in a fragmented body of literature. This recent growth justifies and renders it important to synthesize the extant body of multidisciplinary research to bring this literature together into a systematic whole and to assess the extent of our current knowledge. To this purpose, this article provides a bibliometric review of the accumulated literature (<inline-formula> <tex-math notation="LaTeX">$N=194$ </tex-math></inline-formula>) on educational data mining during 2015&#x2013;2019. Findings suggest that interest in educational data mining has increased in recent years. The studies in this stream of research mainly focus on using state-of-the-art EDM techniques to optimize prediction models to accurately predict learners&#x2019; academic performance and to detect behaviors of learners for timely intervention. In addition, our findings show that EDM literature contains publications of researchers from diverse countries. Most studies were a result of collaborations between multiple authors, and most authors collaborated with authors from the same country. The United States, China, and Spain are the countries with the most prolific publications in EDM literature. For future research, EDM researchers should increase discussions on connecting theories with EDM techniques, ethics and privacy issues, and international collaboration.

Abstract licence: CC BY-NC-ND

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