ABSTRACT
Predicting student academic performance has become a crucial focus in educational data mining, enabling timely interventions to support at-risk students and improve overall outcomes. This paper proposes a novel PolyFeatureML that enhances accuracy by generating higher-order interactions between features, capturing complex relationships that may not be evident through traditional methods. PolyFeature significantly improves the predictive capabilities of the models by enriching the feature space with non-linear interactions, making them particularly useful for identifying patterns in student performance data. Also, the study explores the use of various machine learning (ML) models, such as Logistic Regression, Decision Trees, SVM, Random Forests, LightGBM, XGBoost, CatBoost, and Neural Networks, to predict student performance based on demographic, academic, and behavioral data. The models are evaluated using multiple metrics, including accuracy, precision, recall, F1-score, specificity, and ROC-AUC, offering a comprehensive view of their performance. Our findings demonstrate that ensemble models like Random Forests and boosting techniques achieve high accuracy, while the integration of PolyFeatures leads to significant improvements in performance across various classifiers. This study reports a framework for data-driven educational policies, offering insights on how the proposed model can improve the prediction accuracy, supporting early intervention approaches, and ultimately enhancing student outcomes.
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