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Transforming educational insights: strategic integration of federated learning for enhanced prediction of student learning outcomes

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Abstract

Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements, which are essential in improving learning experiences and overall outcomes. Educational data mining (EDM) is a thriving research field that employs data mining and machine learning methods to extract valuable insights from educational databases, primarily focused on predicting students’ academic performance. This study proposes a novel federated learning (FL) standard that ensures the confidentiality of the dataset and allows for the prediction of student grades, categorized into four levels: low, good, average, and drop. Optimized features are incorporated into the training process to enhance model precision. This study evaluates the optimized dataset using five machine learning (ML) algorithms, namely support vector machine (SVM), decision tree, Naïve Bayes, K-nearest neighbors, and the proposed federated learning model. The models’ performance is assessed regarding accuracy, precision, recall, and F1-score, followed by a comprehensive comparative analysis. The results reveal that FL and SVM outperform the alternative models, demonstrating superior predictive performance for student grade classification. This study showcases the potential of federated learning in effectively utilizing educational data from various institutes while maintaining data privacy, contributing to educational data mining and machine learning advancements for student performance prediction.

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Acknowledgements

The authors would like to thank China’s National Key R & D Program for providing the experimental facilities to perform these experiments. The author would like to thank Artificial Intelligence and Data Analytics Lab (AIDA) CCIS Prince Sultan University for their support.

Funding

This study is supported by the National Key R &D Program of China with project no. 2020YFB2104402.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100024, China

    Umer Farooq & Jianqiang Li

  2. Faculty of Information Sciences, Division of Science and Technology, University of Education, Lahore, 54000, Pakistan

    Umer Farooq, Shahid Naseem & Luqman Mustafa

  3. Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh, 11586, Kingdom of Saudi Arabia

    Tariq Mahmood, Amjad Rehman & Tanzila Saba

  4. Faculty of Information Sciences, University of Educatio, Vehari Campus, Vehari, 61161, Pakistan

    Tariq Mahmood

  5. Beijing Engineering Research Center for IoT Software and Systems, Beijing, 100124, China

    Jianqiang Li

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  1. Umer Farooq
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  2. Shahid Naseem
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Contributions

Farooq, Naseem, Mahmood, LI,  Saba, Rehman, and Mustafa conceived the study and experimented. Farooq, Naseem, Mahmood, Saba, and Li reviewed, drafted, and revised the study. Naseem, Mahmood, Rehman and Mustafa contributed to the design and analyzed data. Farooq, Mahmood, Saba, Li, and Mustafa conducted the proofreading of the study. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Tariq Mahmood.

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Farooq, U., Naseem, S., Mahmood, T. et al. Transforming educational insights: strategic integration of federated learning for enhanced prediction of student learning outcomes. J Supercomput 80, 16334–16367 (2024). https://doi.org/10.1007/s11227-024-06087-9

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