Student Academic Performance and Dropout Risk Prediction Using Machine Learning

Student dropout and inconsistent academic performance continue to pose serious challenges in higher education,

affecting both institutional outcomes and student success. Early identification of students at risk enables institutions to

implement timely interventions and improve retention rates. This study presents a machine learning-based framework for

predicting student academic outcomes and dropout risk using historical educational data.

The proposed model is developed using a dataset from the UCI Machine Learning Repository that includes academic,

demographic, and socioeconomic attributes. Multiple machine learning algorithms, including Logistic Regression, Random

Forest, and XGBoost, are implemented and evaluated for classifying student outcomes. Experimental results indicate that

XGBoost achieves the best performance, with a test accuracy of approximately 87.45%, outperforming the other models.

To enhance interpretability, Shapley Additive explanations (SHAP) are utilised to examine feature contributions. The findings

reveal that academic performance indicators, particularly second-semester results and the completion of curricular units, play a

significant role in predicting student dropout.