Educational data mining (EDM) refers to analyzing large sets of learner data to uncover patterns that improve teaching and learning. Research by Romero and Ventura (2020) shows that EDM can predict student performance, identify at-risk learners, and personalize instruction based on behavioral and engagement data. It helps educators make informed decisions and design interventions before students fail or drop out.

However, EDM also has challenges. It cannot fully explain why students behave a certain way it shows patterns, not causes. Data can be biased or incomplete, and ethical issues arise around privacy and consent. Teachers must interpret findings alongside classroom observations and contextual knowledge. While EDM offers powerful insights into learning behaviors, it should complement not replace human judgment in education.

Reference:

Romero, C., & Ventura, S. (2020). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(3), 601–617.