IDEAS home Printed from https://ideas.repec.org/a/avo/emipdu/v32y2023i2p361-374.html
   My bibliography  Save this article

Student Success Prediction Using Artificial Neural Networks

Author

Listed:
  • Teo Ljubicic

    (A1 Hrvatska d.o.o.)

  • Marko Hell

    (University of Split, The Faculty of Economics, Business and Tourism)

Abstract

Many years of electronic data processing have enabled the storage of a large amount of data that can be used today to improve educational processes through machine learning algorithms. Using data from the Moodle distance learning system, an artificial neural network model was created to predict the final outcome of students at the end of their studies based on their final grades of the first year of study. In three artificial neural network models, the power of this algorithm was demonstrated, where all models achieved a very low error, and the artificial neural network model achieved the best results with two hidden layers of nine neurons, whose absolute error was 0.1920, and the squared error 0.0562. The research shows that artificial neural networks are very effective in predicting the final outcome of students based on the grade from the first year of study and that such models have the potential to become an auxiliary tool and means of decision-making in educational institutions.

Suggested Citation

  • Teo Ljubicic & Marko Hell, 2023. "Student Success Prediction Using Artificial Neural Networks," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, vol. 32(2), pages 361-374, december.
    • Handle: RePEc:avo:emipdu:v:32:y:2023:i:2:p:361-374
    DOI: 10.17818/EMIP/2023/2.3
    as

    Download full text from publisher

    File URL: https://hrcak.srce.hr/index.php/clanak/448585
    Download Restriction: no
    File URL: https://libkey.io/10.17818/EMIP/2023/2.3?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

    machine learning; deep learning; artificial neural networks; predictive modelling; education system;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • I20 - Health, Education, and Welfare - - Education - - - General
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    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:avo:emipdu:v:32:y:2023:i:2:p:361-374. 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: Nebojsa Stojcic (email available below). General contact details of provider: https://edirc.repec.org/data/oedubhr.html .

    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.