Predicting Students’ Academic Performance Using Data Mining Method

Predicting students' academic performance is a critical task in educational institutions to identify students likely to succeed or need intervention. This study leverages data mining techniques to analyze and predict student outcomes, contributing to more informed decision-making in educational management. Using data from various academic sources, the research implements three popular data mining algorithms—Naive Bayes, Multilayer Perceptron, and C4.5 Decision Tree—to classify student success. These techniques allow the identification of patterns and dependencies in student data, providing insights that can assist educators in creating tailored interventions. The results indicate that the Naive Bayes algorithm outperforms other classifiers in terms of prediction accuracy, making it a viable tool for predicting academic performance. This research underscores the potential of data mining to enhance educational outcomes by allowing proactive responses to students' academic needs.

Academic performance prediction, Student performance analysis, Data mining in education

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