Educational Data Mining (EDM) offers vast possibilities for improving teaching and learning by transforming raw educational data into meaningful insights. One of its key potentials is personalized learning, where data is used to identify each student’s strengths, weaknesses, and learning preferences, allowing teachers to tailor lessons and materials to individual needs. EDM also enables the early identification of at-risk students by analyzing patterns in attendance, participation, and performance, helping educators provide timely interventions before problems worsen. Moreover, it supports data-driven decision-making, guiding school leaders in designing curricula, allocating resources, and evaluating programs based on solid evidence rather than assumptions. Another advantage is enhanced assessment and feedback, as EDM allows for continuous tracking of student progress and the provision of instant, formative feedback. Furthermore, it contributes to educational research by uncovering hidden patterns and relationships between teaching practices, student behaviors, and learning outcomes.

However, despite these benefits, EDM also faces several challenges. Data privacy and ethical concerns are major issues, as collecting and analyzing student data raise questions about consent, confidentiality, and potential misuse. Data quality and integration can also be problematic since inconsistent or incomplete data may lead to inaccurate conclusions, and combining information from multiple sources is often complex. Additionally, there is the risk of misinterpretation and misuse of results if educators lack sufficient understanding of data analysis, leading to poor decisions. Technical and resource limitations, such as inadequate infrastructure or lack of skilled personnel, can further hinder implementation, especially in under-resourced schools. Finally, algorithmic bias and fairness remain pressing concerns, as models trained on biased data may reinforce existing inequalities among students. In summary, while educational data mining holds immense promise for transforming education through personalization and evidence-based improvement, it must be implemented carefully and ethically to avoid unintended negative consequences.