Applying data analytics models to improve student academic performance

The integration of data analytics in education has opened new opportunities for improving student outcomes. Educational institutions are increasingly using data-driven approaches to monitor, predict, and enhance student academic performance. This article examines how data analytics models—such as predictive modeling, classification, and clustering—can be applied to identify learning patterns, detect at-risk students, and support personalized education strategies. The study highlights the benefits, challenges, and future potential of analytics in modern education systems.

Перевод пока недоступен