Data-Driven Insights for Academic Success: Predicting Student Performance Using Machine Learning

Abstract

Student performance prediction is a vital aspect of modern education systems, offering insights that enable educators to identify
students at risk of underperforming and implement targeted interventions. This study proposes a comprehensive data-driven approach for predicting student performance in online exams using multiple machine learning algorithms, including Decision Tree Classifiers, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Naïve Bayes, Random Forest, and Support Vector Machines (SVM). The model's predictive capability is assessed using evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² Score. Comparative analysis reveals that Gradient Boosting and Random Forest outperform other models in terms of accuracy and robustness, achieving lower error rates and higher R² scores. Feature importance analysis further identifies key academic and behavioral factors that influence student outcomes, including study time, attendance, previous performance, and participation in interactive activities. The results highlight the effectiveness of machine learning algorithms in uncovering hidden patterns within student data, facilitating personalized learning experiences and proactive academic support. Additionally, the study provides actionable insights for educational institutions, empowering data-driven decision-making to enhance student learning outcomes. Future work will involve expanding the dataset with additional socio-economic and psychological factors, applying deep learning models, and developing real-time predictive systems for continuous academic monitoring. This research underscores the transformative potential of machine learning in the education sector, promoting academic success and institutional effectiveness.