Student Performance Prediction Using Machine Learning
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Date
2026
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SDU University
Abstract
The early identification of academically at-risk students remains one of the most pressing problems in contemporary higher education. As universities accumulate large amounts of administrative, demographic and behavioural data through their Virtual Learning Environments, machine learning has emerged as a powerful instrument for transforming raw educational data into actionable early-warning signals. This dissertation develops, evaluates and explains a comprehensive machine learning pipeline for predicting student academic performance on the Open University Learning Analytics Dataset (OULAD), a publicly available benchmark containing detailed records for 32,593 learners across 22 module-presentations. The work is framed around four research questions concerning the most predictive student features, the most suitable learning algorithm for the task, the relative contribution of individual pipeline components and the practical interpretability of the resulting models. To answer these questions, the dissertation proposes an end-to-end methodology that combines multi-source data integration, domain-driven feature engineering, hybrid feature selection, synthetic minority over-sampling, Bayesian hyperparameter optimisation using Optuna and post-hoc model interpretation through SHapley Additive exPlanations (SHAP). Four representative classifiers are compared on the same task: a regularised Logistic Regression baseline, Random Forest, XGBoost and LightGBм. Experimental results demonstrate that gradient-boosted tree ensembles, and in particular an Optuna-tuned XGBoost model, consistently outperform the alternative baselines on the held-out test set, reaching a test area under the receiver operating characteristic curve of 0.97 and a test F1 score of 0.90 with a moderate overfitting gap. Engagement-related features-including the number of active days on the Virtual Learning Environment, total clicks and an engineered score_consistency indicator-are identified as the most influential predictors by both global feature-importance methods and SHAP analysis. An ablation study reveals that feature engineering, class-imbalance handling and hyperparameter tuning each contribute incrementally to the final performance and that their combination yields the best balance of accuracy, generalisation and computational efficiency. The dissertation also discusses educational, ethical and deployment-related implications of the proposed pipeline. SHAP-based explanations are shown to produce individualised, actionable insights that can be conveyed to educators and policymakers without requiring a deep understanding of the underlying algorithms. Limitations associated with the use of a single benchmark dataset, with binary outcome modelling and with the absence of causal claims are explicitly acknowledged, and directions for future research are outlined, including temporal modelling, fairness-aware learning and integration with existing learning analytics dashboards. Overall, the dissertation contributes a reproducible methodological template that bridges predictive accuracy and humaninterpretable insight in the context of student performance prediction.
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Keywords
student performance prediction, educational data mining, learning analytics, machine learning, XGBoost, feature engineering
Citation
Syzdykov D / Student Performance Prediction Using Machine Learning / SDU University / Department of Information Systems