IDEAS home Printed from https://ideas.repec.org/a/vrs/organi/v58y2025i3p296-310n1006.html
   My bibliography  Save this article

User Evaluation of a Machine Learning-Based Student Performance Prediction Platform

Author

Listed:
  • Hoti Arbër H.

    (South East European University, Faculty of Computer Science, Tetovo, Republic of North Macedonia)

  • Zenuni Xhemal

    (South East European University, Faculty of Computer Science, Tetovo, Republic of North Macedonia)

  • Hamiti Mentor

    (South East European University, Faculty of Computer Science, Tetovo, Republic of North Macedonia)

  • Ajdari Jaumin

    (South East European University, Faculty of Computer Science, Tetovo, Republic of North Macedonia)

Abstract

Background/Purpose The integration of machine learning in education has opened new possibilities for predicting student performance and enabling early interventions. While most of the work has been focused on prediction algorithms design and evaluations, little work has been done on user-centric evaluations. Methodology This study evaluates a web-based platform designed for student performance prediction using various machine learning algorithms. Users, including students, professors, and career counselors, tested the platform and provided feedback on usability, accuracy, and recommendation likelihood. Results Results indicate that the platform is user-friendly, requires minimal technical support, and delivers reliable predictions. Conclusion Users strongly endorsed its adoption, highlighting its potential to assist educators in identifying at-risk students and improving academic outcomes.

Suggested Citation

  • Hoti Arbër H. & Zenuni Xhemal & Hamiti Mentor & Ajdari Jaumin, 2025. "User Evaluation of a Machine Learning-Based Student Performance Prediction Platform," Organizacija, Sciendo, vol. 58(3), pages 296-310.
    • Handle: RePEc:vrs:organi:v:58:y:2025:i:3:p:296-310:n:1006
    DOI: 10.2478/orga-2025-0018
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/orga-2025-0018
    Download Restriction: no
    File URL: https://libkey.io/10.2478/orga-2025-0018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    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:vrs:organi:v:58:y:2025:i:3:p:296-310:n:1006. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.