Student Performance Detection Using Machine Learning Techniques

Student performance detection has become an essential research area in educational data mining, as accurate prediction of academic outcomes can support timely interventions and enhance learning efficiency. Traditional evaluation methods often rely on periodic examinations and teacher assessments, which may not capture the complex and multifaceted factors influencing student performance. Machine Learning (ML) techniques provide a promising alternative by analyzing large volumes of educational data and identifying hidden patterns that contribute to academic success or failure. This study proposes an ML-based framework for predicting student performance by considering academic, behavioral, and socio-economic features, such as attendance, previous grades, study habits, parental background, and participation in extracurricular activities. The dataset undergoes preprocessing, feature selection, and balancing techniques to ensure model reliability. Several ML algorithms—including Decision Trees, Random Forest, Support Vector Machines (SVM), Logistic Regression, and Neural Networks—are evaluated to determine the most effective approach for prediction. Performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are employed for comprehensive evaluation. Experimental results demonstrate that ensemble-based models, particularly Random Forest, achieve superior accuracy compared to single classifiers, indicating their effectiveness in capturing nonlinear relationships within the data. The findings highlight that feature such as attendance and internal assessment scores strongly influence performance prediction. This research not only contributes to improving academic planning but also assists educators in early detection of at-risk students, enabling targeted support and personalized learning strategies. Ultimately, the integration of ML into educational systems can reduce dropout rates, enhance student outcomes, and foster data-driven decision-making in academia.