Educational Data Mining (EDM) offers powerful capabilities to revolutionize education by applying sophisticated algorithms to student data, primarily to predict outcomes such as student success, failure, or dropout risk, allowing for proactive, targeted interventions. It excels at identifying Key Performance Indicators (KPIs) and uncovering hidden statistical relationships in large datasets, which can reveal optimal learning strategies and facilitate the creation of personalized, intelligent tutoring systems. By grouping learners into distinct behavioral profiles, EDM supports differentiated instruction and improves the overall effectiveness of curriculum design by validating the quality of learning materials.

However, EDM is fundamentally limited in what it can tell us and is constrained by ethical and technical challenges. Crucially, EDM can only determine correlation, not causation, meaning it cannot explain the underlying why behind student outcomes, nor can it capture essential qualitative information about a student's internal cognitive or emotional state. Its models are restricted by the data they are fed, making it impossible to evaluate aspects of the "unmeasured curriculum" like critical thinking or creativity. Finally, the field grapples with the ethical "black box" problem and the potential for algorithmic bias, making it difficult to gain stakeholder trust when predictions which may unfairly label or limit students cannot be fully explained.