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A Study on the Learning Early Warning Prediction Based on Homework Habits: Towards Intelligent Sustainable Evaluation for Higher Education

Author

Listed:
  • Wenkan Wen

    (School of Computer and Artificial Intelligence, Huaihua University, Huaihua 418000, China)

  • Yiwen Liu

    (School of Computer and Artificial Intelligence, Huaihua University, Huaihua 418000, China
    Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities, Huaihua 418000, China
    Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua 418000, China)

  • Zhirong Zhu

    (School of Computer and Artificial Intelligence, Huaihua University, Huaihua 418000, China)

  • Yuanquan Shi

    (School of Computer and Artificial Intelligence, Huaihua University, Huaihua 418000, China
    Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities, Huaihua 418000, China
    Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua 418000, China)

Abstract

Teachers need a technique to efficiently understand the learning effects of their students. Early warning prediction mechanisms constitute one solution for assisting teachers in changing their teaching strategies by providing a long-term process for assessing each student’s learning status. However, current methods of building models necessitate an excessive amount of data, which is not conducive to the final effect of the model, and it is difficult to collect enough information. In this paper, we use educational data mining techniques to analyze students’ homework data and propose an algorithm to extract the three main features: Degree of reliability, degree of enthusiasm, and degree of procrastination. Building a predictive model based on homework habits can provide an individualized evaluation of students’ sustainability processes and support teachers in adjusting their teaching strategies. This was cross-validated using multiple machine learning algorithms, of which the highest accuracy was 93.34%.

Suggested Citation

  • Wenkan Wen & Yiwen Liu & Zhirong Zhu & Yuanquan Shi, 2023. "A Study on the Learning Early Warning Prediction Based on Homework Habits: Towards Intelligent Sustainable Evaluation for Higher Education," Sustainability, MDPI, vol. 15(5), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4062-:d:1077859
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    References listed on IDEAS

    as
    1. Mohammad Salehi & Samaneh Gholampour, 2021. "Cheating on exams: Investigating Reasons, Attitudes, and the Role of Demographic Variables," SAGE Open, , vol. 11(2), pages 21582440211, May.
    2. Kyungyeul Kim & Han-Sung Kim & Jaekwoun Shim & Ji Su Park, 2021. "A Study in the Early Prediction of ICT Literacy Ratings Using Sustainability in Data Mining Techniques," Sustainability, MDPI, vol. 13(4), pages 1-11, February.
    Full references (including those not matched with items on IDEAS)

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