Educational data mining (EDM) uses data from learning environments to uncover patterns in student behavior and improve instructional strategies. A notable study applied EDM techniques to an online flipped classroom, using clustering methods to analyze student interactions. Researchers identified specific learning strategies students adopted throughout the course, such as how they self-regulated depending on the topic. These findings provided actionable insights for improving course design and offering targeted feedback to enhance learning outcomes​.

EDM can reveal a wide range of student behaviors, such as engagement levels, resource utilization, and learning patterns. It enables educators to track progress, identify learning strategies, and assess the effectiveness of instructional designs. For example, in the aforementioned study, the data collected allowed instructors to fine-tune the course structure, optimizing student success based on observed learning trends. This type of analysis is valuable because it provides concrete evidence of how students engage with course materials over time.

However, while EDM can offer detailed insights into student actions and learning processes, there are limitations to what it can reveal. EDM may not fully capture qualitative aspects such as student motivation, emotions, or the social dynamics of learning environments, which can also influence educational outcomes. Therefore, while EDM is a powerful tool for improving instructional design, it should be used in conjunction with other data sources, such as surveys or interviews, to gain a more comprehensive understanding of student learning.