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    • Machine Learning Based Evaluation and Prediction of Student Performance in Virtual Learning Environment

    Original Research (Published On: 17-Aug-2026 )
    DOI : https://doi.org/10.54364/AAIML.2026.64333

    Saba Mahmood, Umair Abbasi, Saba Mahmood, Heba Fasihuddin, Safa Habibullah, Manal Linjawi and Ali Daud

    Adv. Artif. Intell. Mach. Learn., 6 (4):6014-6032

    1. Saba Mahmood: Bahria University Islamabad

    2. Umair Abbasi: Bahria University Islamabad

    3. Saba Mahmood: Bahria University Islamabad

    4. Heba Fasihuddin: Department of Information Systems and Technology College of Computer Science and Engineering, University of Jeddah Jeddah, Saudi Arabia

    5. Safa Habibullah: Department of information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia

    6. Manal Linjawi: Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arab.

    7. Ali Daud: Department of Resilience, Rabdan Academy, Abu Dhabi, UAE.

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    DOI: 10.54364/AAIML.2026.64333

    Article History: Received on: 16-Jan-26, Accepted on: 10-Aug-26, Published on: 17-Aug-26

    Corresponding Author: Saba Mahmood

    Email: smahmood.buic@bahria.edu.pk

    Citation: Umair Abbasi, et al. Machine Learning Based Evaluation and Prediction of Student Performance in Virtual Learning Environment. Advances in Artificial Intelligence and Machine Learning. 2026;6(4):333. https://dx.doi.org/10.54364/AAIML.2026.64333


    Abstract

    In recent years, the rapid expansion of Virtual Learning Environments (VLEs)and online education

    platforms has significantly transformed higher education.These education setups have introduced

    new challenges for example increased student attrition and disengagement. Predicting student per-

    formance within these digital frameworks is essential to enabling timely interventions and improving

    academic outcomes. This research addresses the prediction of high risk students by leveraging the

    Open University Learning Analytics Dataset (OULAD) that comprises demographic, assessment,

    and detailed engagement data for over 32,000 students. Recognizing gaps in the literature, this study

    develops a robust ensemble based machine learning pipeline with Particle Swarm Optimization(PSO)

    as a feature selection strategy. The results demonstrate that PSO reduced the feature space by ap-

    proximately 40% while maintaining high predictive performance (F1 ≈ 0.937, AUC ≈ 0.980) in

    comparison to Gini importance and Factor Analysis of Mixed Data(FAMD). Overall 94% accuracy

    and AUC scores near 0.981 is achieved that is comparable to advanced deep learning benchmarks.

    The inclusion of SHAP analysis improves the interpretation of the proposed scheme. This work con-

    tributes a balanced, explainable framework for educational early warning systems thereby, bridging

    methodological gaps in comparative feature selection techniques and offering practical insights for

    scalable deployment in online learning contexts.


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