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Quantitative Analysis and Prediction of Academic Performance of Students Using Machine Learning

Author

Listed:
  • Lihong Zhao

    (School of Fine Art, Shandong University of Technology, Zibo 255000, China)

  • Jiaolong Ren

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Lin Zhang

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Hongbo Zhao

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

Abstract

Academic performance evaluation is essential to enhance educational affection and improve educational quality and level. However, evaluating academic performance is difficult due to the complexity and nonlinear education process and learning behavior. Recently, machine learning technology has been adopted in Educational Data Mining (EDM) to predict and evaluate students’ academic performance. This study developed a quantitative prediction model of academic performance and investigated the performance of various machine learning algorithms and the influencing factors based on the collected educational data. The results conclude that machine learning provided an excellent tool to characterize educational behavior and represent the nonlinear relationship between academic performance and its influencing factors. Although the performance of various methods has some differences, all could be used to capture the complex and implicit educational law and behavior. Furthermore, machine learning methods that fully consider various factors have better prediction and generalization performance. In order to characterize the educational law well and evaluate accurately the academic performance, it is necessary to consider as many influencing factors as possible in the machine learning model.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12531-:d:1219639
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    References listed on IDEAS

    as
    1. 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.
    2. Aurora Sánchez & Cristian Vidal-Silva & Gabriela Mancilla & Miguel Tupac-Yupanqui & José M. Rubio, 2023. "Sustainable e-Learning by Data Mining—Successful Results in a Chilean University," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    3. Elli Doukanari & Despo Ktoridou & Leonidas Efthymiou & Epaminondas Epaminonda, 2021. "The Quest for Sustainable Teaching Praxis: Opportunities and Challenges of Multidisciplinary and Multicultural Teamwork," Sustainability, MDPI, vol. 13(13), pages 1-21, June.
    4. Chuang Bao & Yong Li & Xinmeng Zhao, 2023. "The Influence of Social Capital and Intergenerational Mobility on University Students’ Sustainable Development in China," Sustainability, MDPI, vol. 15(7), pages 1-20, April.
    5. Lifen Bai & Binbin Yang & Shichong Yuan, 2023. "Evaluating of Education Effects of Online Learning for Local University Students in China: A Case Study," Sustainability, MDPI, vol. 15(13), pages 1-13, June.
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