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Predicting High-Risk Students Using Learning Behavior

Author

Listed:
  • Tieyuan Liu

    (Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541000, China
    School of Information and Communication, Guilin University of Electronic Technology, Guilin 514000, China)

  • Chang Wang

    (Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541000, China)

  • Liang Chang

    (Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541000, China)

  • Tianlong Gu

    (Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541000, China
    College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou 510000, China)

Abstract

Over the past few years, the growing popularity of online education has enabled there to be a large amount of students’ learning behavior data stored, which brings great opportunities and challenges to the field of educational data mining. Students’ learning performance can be predicted, based on students’ learning behavior data, so as to identify at-risk students who need timely help to complete their studies and improve students’ learning performance and online teaching quality. In order to make full use of these learning behavior data, a new prediction method was designed based on existing research. This method constructs a hybrid deep learning model, which can simultaneously obtain the temporal behavior information and the overall behavior information from the learning behavior data, so that it can more accurately predict the high-risk students. When compared with existing deep learning methods, the experimental results show that the proposed method offers better predicting performance.

Suggested Citation

  • Tieyuan Liu & Chang Wang & Liang Chang & Tianlong Gu, 2022. "Predicting High-Risk Students Using Learning Behavior," Mathematics, MDPI, vol. 10(14), pages 1-15, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2483-:d:864426
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    References listed on IDEAS

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    1. Antonio Hernández-Blanco & Boris Herrera-Flores & David Tomás & Borja Navarro-Colorado, 2019. "A Systematic Review of Deep Learning Approaches to Educational Data Mining," Complexity, Hindawi, vol. 2019, pages 1-22, May.
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    Cited by:

    1. Carmen Lacave & Ana Isabel Molina, 2023. "Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education," Mathematics, MDPI, vol. 11(6), pages 1-4, March.

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