IDEAS home Printed from https://ideas.repec.org/a/igg/jdwm00/v18y2022i1p1-15.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.313585
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kandaswamy Paramasivan & Rahul Subburaj & Saish Jaiswal & Nandan Sudarsanam, 2022. "Empirical evidence of the impact of mobility on property crimes during the first two waves of the COVID-19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-14, December.
    2. Kim, Eun-Sung, 2020. "Deep learning and principal–agent problems of algorithmic governance: The new materialism perspective," Technology in Society, Elsevier, vol. 63(C).
    3. Jules F. Cacho & Jeremy Feinstein & Colleen R. Zumpf & Yuki Hamada & Daniel J. Lee & Nictor L. Namoi & DoKyoung Lee & Nicholas N. Boersma & Emily A. Heaton & John J. Quinn & Cristina Negri, 2023. "Predicting Biomass Yields of Advanced Switchgrass Cultivars for Bioenergy and Ecosystem Services Using Machine Learning," Energies, MDPI, vol. 16(10), pages 1-16, May.
    4. Alves, Luiz G.A. & Ribeiro, Haroldo V. & Rodrigues, Francisco A., 2018. "Crime prediction through urban metrics and statistical learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 435-443.
    5. Wang, Jia & Hu, Jun & Shen, Shifei & Zhuang, Jun & Ni, Shunjiang, 2020. "Crime risk analysis through big data algorithm with urban metrics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    6. Hyunjin Lee & Taesik Lee, 2021. "Demand modelling for emergency medical service system with multiple casualties cases: k-inflated mixture regression model," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 1090-1115, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jdwm00:v:18:y:2022:i:1:p:1-15. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.