One notable research study by Romero and Ventura (2020) explored the use of educational data mining (EDM) to analyze patterns of student behavior in online learning platforms. The study used data such as login frequency, time spent on tasks, and quiz scores to predict academic performance and identify at-risk students. The results showed that EDM can provide valuable insights for teachers and administrators by highlighting students who need additional support and by improving the design of digital learning environments.

The possibilities of EDM are vast—it can enhance personalized learning, optimize curriculum design, and inform educational policy through data-driven decisions. However, the challenges are equally significant. Data privacy, ethical use, and the risk of overgeneralization can limit the effectiveness of EDM. Moreover, while EDM can tell us what students do, it cannot fully explain why they behave that way, as it often lacks qualitative context. Therefore, EDM works best when combined with human judgment and pedagogical insight.

References:

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.

Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning Analytics (pp. 61–75). Springer.