IDEAS home Printed from https://ideas.repec.org/a/alg/jijdte/v1y2016i1p1-11.html
   My bibliography  Save this article

Machine learning model for early detection of higher education students that need additional attention in introductory programming courses

Author

Listed:
  • Goran Djambic

    (University College Algebra)

  • Mirjana Krajcar

    (University College Algebra)

  • Daniel Bele

    (University College Algebra)

Abstract

Not all higher education students who enroll in introductory programming course successfully finish it, in particular if their major is not computer programming or computer science. From empirical evidence collected at the University College Algebra we have concluded that such students can be given a better passing chance if they are identified early in the semester and given additional attention in form of extra hours in classroom, guided by mentors. The challenge lies in being able to distinguish such students as early as possible so they can benefit from additional learning hours before the final exams. This paper proposes one such possible criterion: we develop a machine learning model based on previous generations of students and use it for current students to calculate the probability of finishing the course unsuccessfully. Such students are then classified as "requiring extra attention".

Suggested Citation

  • Goran Djambic & Mirjana Krajcar & Daniel Bele, 2016. "Machine learning model for early detection of higher education students that need additional attention in introductory programming courses," International Journal of Digital Technology and Economy, Algebra University College, vol. 1(1), pages 1-11, June.
    • Handle: RePEc:alg:jijdte:v:1:y:2016:i:1:p:1-11
    as

    Download full text from publisher

    File URL: http://algebra.hr/publications//ijdte/vol1_iss1/IJDTE_01_01_2016_P1.pdf
    Download Restriction: no
    ---><---

    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:alg:jijdte:v:1:y:2016:i:1:p:1-11. 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: Robert Kopal (email available below). General contact details of provider: https://algebra.university/ .

    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.