Educational data mining (EDM) has become a powerful tool for understanding and improving student learning. A representative study by Yağcı (2022) applied machine-learning algorithms such as random forests, support vector machines, and logistic regression to predict student performance based on midterm grades and other course-related features. The study collected detailed student data through a learning management system and used it to identify patterns, correlations, and trends that could help forecast final grades. By analyzing such data, teachers can detect students who may struggle, enabling early interventions to improve academic outcomes.

EDM provides valuable insights beyond simple grade prediction. It can reveal learning trajectories, engagement patterns, clusters of students with similar performance profiles, and bottleneck courses where many learners struggle. For example, a 2023 study on course-trajectory visualization used EDM to map entire student pathways, showing where dropouts or slowdowns tend to occur. These analytics allow teachers and administrators to make data-driven decisions, provide targeted support, and optimize curricula. Additionally, students benefit from immediate feedback, self-monitoring opportunities, and enhanced accountability for their learning progress (Yağcı, 2022; Educational Technology Journal, 2023).

Despite its strengths, EDM has notable limitations. It depends heavily on the quality and completeness of data, often missing qualitative aspects of learning such as motivation, emotional factors, or socio-economic challenges. While models can identify correlations, they cannot fully explain causation, meaning they may flag a student as “at risk” without revealing why. Furthermore, predictive models risk reinforcing existing biases if the underlying data reflects inequities. Therefore, while EDM offers powerful insights for educational decision-making, it must be interpreted carefully and supplemented with human judgment and qualitative understanding to ensure ethical and effective application (Khalid et al., 2023; MDPI, 2023).

References: Yağcı, M. (2022). Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learning Environments. https://slejournal.springeropen.com/articles/10.1186/s40561-022-00192-z

Educational Technology Journal. (2023). Examining students’ course trajectories using data mining and visualization approaches. https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-023-00423-4

Khalid, K., et al. (2023). Educational data mining and its limitations: a critical review. KJ Computer & Information Science Journal. https://kjcis.kiet.edu.pk/index.php/kjcis/article/view/212

MDPI. (2023). Ethical considerations in educational data mining and learning analytics. https://www.mdpi.com/2073-431X/14/2/68