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Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan

Author

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  • Rimsha Asad

    (University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46300, Pakistan)

  • Saud Altaf

    (University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46300, Pakistan)

  • Shafiq Ahmad

    (Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Haitham Mahmoud

    (Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Shamsul Huda

    (School of Information Technology, Deakin University, Burwood, VIC 3128, Australia)

  • Sofia Iqbal

    (Space and Upper Atmosphere Research Commission, Islamabad 44000, Pakistan)

Abstract

Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students’ performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.

Suggested Citation

  • Rimsha Asad & Saud Altaf & Shafiq Ahmad & Haitham Mahmoud & Shamsul Huda & Sofia Iqbal, 2023. "Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan," Sustainability, MDPI, vol. 15(6), pages 1-24, March.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5431-:d:1101654
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    References listed on IDEAS

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    1. Sadaf Roohi Tauqir & Syed Shahid Hussain & Sarwar M. Azhar, 2014. "The Role of Vice Chancellors to Promote Higher Education in Pakistan: A Critical Review of Higher Education Commission (HEC) Pakistan’s Reforms, 2002," South Asian Journal of Management Sciences (SAJMS), Iqra University, Iqra University, vol. 8(1), pages 46-59, Spring.
    2. Shailendra Palvia & Prageet Aeron & Parul Gupta & Diptiranjan Mahapatra & Ratri Parida & Rebecca Rosner & Sumita Sindhi, 2018. "Online Education: Worldwide Status, Challenges, Trends, and Implications," Journal of Global Information Technology Management, Taylor & Francis Journals, vol. 21(4), pages 233-241, October.
    3. Yijun Zhao & Yi Ding & Yangqian Shen & Samuel Failing & Jacqueline Hwang, 2022. "Different Coping Patterns among US Graduate and Undergraduate Students during COVID-19 Pandemic: A Machine Learning Approach," IJERPH, MDPI, vol. 19(4), pages 1-16, February.
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    Cited by:

    1. Lihong Zhao & Jiaolong Ren & Lin Zhang & Hongbo Zhao, 2023. "Quantitative Analysis and Prediction of Academic Performance of Students Using Machine Learning," Sustainability, MDPI, vol. 15(16), pages 1-18, August.

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