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Machine Learning Based Admission Data Processing for Early Forecasting Students' Learning Outcomes

Author

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  • Nguyen Thi Kim Son

    (Faculty of Natural Science, Hanoi Metropolitan University, Vietnam & Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam)

  • Nguyen Van Bien

    (Hanoi National University of Education, Hanoi, Vietnam)

  • Nguyen Huu Quynh

    (Thuyloi University, Hanoi, Vietnam)

  • Chu Cam Tho

    (The Vietnam Institute of Educational Sciences, Hanoi, Vietnam)

Abstract

In this paper, the authors explore the factors to improve the accuracy of predicting student learning outcomes. The method can remove redundant and irrelevant factors to get a “clean” data set without having to solve the NP-Hard problem. The method can improve the graduation outcome prediction accuracy through logistic regression machine learning method for “clean” data set. They empirically evaluate the training and university admission data of Hanoi Metropolitan University from 2016 to 2020. From data processing results and the support from the machine learning techniques application program, they analyze, evaluate, and forecast students' learning outcomes based on admission data, first-year, and second-year academic performance data. They then submit proposals of training and admission policies and methods of radically and quantitatively solving problems in university admissions.

Suggested Citation

  • Nguyen Thi Kim Son & Nguyen Van Bien & Nguyen Huu Quynh & Chu Cam Tho, 2022. "Machine Learning Based Admission Data Processing for Early Forecasting Students' Learning Outcomes," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 18(1), pages 1-15, January.
    • Handle: RePEc:igg:jdwm00:v:18:y:2022:i:1:p:1-15
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    References listed on IDEAS

    as
    1. Hyeon-Woo Kang & Hang-Bong Kang, 2017. "Prediction of crime occurrence from multi-modal data using deep learning," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-19, April.
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