Educational Data Mining: Construction of a Tree-based Model to Predict Students’ Performance

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The proposed model achieved a classification accuracy of 0.7700 and an F1-score of 0.7888, indicating effectiveness.
L1-based feature selection highlighted four key indicators such as scholarship type and cumulative GPA, impacting predictions.
Classification and regression trees emerged as the most effective model in predicting students' academic success.
The study underscores the importance of understanding educational inputs to enhance learning outcomes.

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Data mining techniques predict students’ performance by identifying significant features, suggesting improvement methods.

Furkan Aydın (2025) studied this question.

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