IDEAS home Printed from https://ideas.repec.org/a/pkp/ijoeap/v8y2020i2p268-277id640.html
   My bibliography  Save this article

Theorising Machine Learning as an Alternative Pathway for Higher Education in Africa

Author

Listed:
  • Kehdinga George Fomunyam

Abstract

Machine learning technology is currently a new frontier for higher education globally, and the African higher education system needs to change in tandem with this technological trend in order to combat challenges faced by the system. These challenges include lack of institutional research to discover new knowledge, unfavorable methods of instruction, especially the language conflict, access to education for marginalized and isolated communities, high dropout rates, depleted infrastructure and unavailability of resources, overpopulated classrooms, and a biased grading system. This paper discusses alternative machine learning solutions to these challenges faced by the African higher education system, in order to ensure that students develop the skills needed to thrive in this digital era. Findings reveal three key technological solutions that can provide alternative solutions to these challenges, and they include customized/personalized learning, predictive analytics and digital administrative management, and virtual assistance. This paper concludes that for Africa, Catching up with the world goes beyond adopting these new innovations to facilitate learning. Recommendations include rethinking the content of the African curriculum, developing an unbiased education system, and adopting a suitable medium of instruction.

Suggested Citation

  • Kehdinga George Fomunyam, 2020. "Theorising Machine Learning as an Alternative Pathway for Higher Education in Africa," International Journal of Education and Practice, Conscientia Beam, vol. 8(2), pages 268-277.
  • Handle: RePEc:pkp:ijoeap:v:8:y:2020:i:2:p:268-277:id:640
    as

    Download full text from publisher

    File URL: https://archive.conscientiabeam.com/index.php/61/article/view/640/953
    Download Restriction: no

    File URL: https://archive.conscientiabeam.com/index.php/61/article/view/640/3939
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vidya Anderson & Manavvi Suneja & Jelena Dunjic, 2023. "Sensing and Measurement Techniques for Evaluation of Nature-Based Solutions: A State-of-the-Art Review," Land, MDPI, vol. 12(8), pages 1-39, July.

    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:pkp:ijoeap:v:8:y:2020:i:2:p:268-277:id:640. 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: Dim Michael (email available below). General contact details of provider: https://archive.conscientiabeam.com/index.php/61/ .

    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.