Machine Learning-Based Student Academic Performance Prediction in a Nigerian University Context: A Comparative Evaluation of Classification Algorithms

Abstract

Academic failure and attrition remain persistent problems in Nigerian higher education, where institutions typically respond only after a student has already failed an examination. Educational data mining has shown that machine learning can flag at risk students early, yet most published work relies on datasets and institutional practices that do not reflect the realities of resource constrained African universities, limiting practical applicability. This study develops and benchmarks a machine learning framework for predicting first-year undergraduate academic outcomes at a single private Nigerian university, comparing six classification algorithms, identifying the most informative predictors, and assessing computational feasibility and fairness for single institution deployment. Six classifiers, Random Forest, XGBoost, LightGBM, Support Vector Machine, Logistic Regression, and k-Nearest Neighbour, were trained on 1,200 student records covering demographics, study habits, socioeconomic indicators, and first-year CGPA. After preprocessing, SMOTE class balancing, and Information Gain feature selection, models were evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and PRAUC, with SHAP values used to interpret feature contributions. Random Forest achieved the highest accuracy (91.3%) and AUCROC (0.947), closely followed by XGBoost (91.1%, 0.945) and LightGBM (90.8%, 0.940); all three ensemble methods substantially outperformed SVM, Logistic Regression, and k-NN. Attendance rate, hours of self-study per day, and internet access were the three most predictive features under both Information Gain and SHAP analysis. A preliminary fairness audit found modestly lower recall for low-income students (81.5%) than high-income students (87.0%). The framework runs on standard institutional hardware without GPU infrastructure, making early-warning deployment computationally feasible for resource-constrained universities. Machine learning can support academic advising as a decision-support tool, though institutional governance, ethical oversight, and further fairness auditing are needed before operational use (SDG 4: Quality Education).