A Bibliometric Analysis of Student Academic Performance Prediction Using Data Mining in Education Research

Authors

Nwabisa Ledidi, Central University of Technology, South Africa

Muthoni Masinde, Central University of Technology, South Africa

Paulina Phoobane, Walter Sisulu University, South Africa

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/tbc

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

This paper presents a bibliometric study exploring how educational data mining (EDM) has advanced the prediction of student academic performance, using VOSviewer and R Studio, to analyse and visualise 1461 relevant publications extracted from the Web of Science and Scopus databases. The study reveals a considerable increase in publications on student academic performance prediction since 2016, with India, China, and Malaysia emerging as the leading contributors. It identifies "academic performance," "machine learning," and "education" as basic but foundational themes with high relevance but moderate development. In contrast, themes such as "dropouts," "college challenges," and "socioeconomic status" appear as underdeveloped or emerging, indicating areas requiring further exploration. The study also highlights limited research collaboration among African countries. Additionally, it notes the critical emerging trends such as adversarial machine learning and contrastive learning, representing key gaps for future inquiry.

Keywords

Education Data Mining (EDM), Student Academic Performance, Student Success, Performance Prediction Models, Data Mining Techniques, Bibliometric Analysis

Cite this paper

N. Ledidi, M. Masinde and P. Phoobane (2026) "A Bibliometric Analysis of Student Academic Performance Prediction Using Data Mining in Education Research", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/tbc

Access Paper

Click here to access this paper.

IST-Africa Community Members can download papers by logging into their existing account.

If you wish to become a member of IST-Africa Community, pleaseregister your profile and you will then receive login details which you can use to access the papers.

Access to papers in the IST-Africa 2026 Conference Proceedings is for INDIVIDUAL USE only. Neither the Conference Proceedings (or individual double blind peer reviewed papers) are to be included in any public or institutional repository or processed in the context of any AI tool or large language model without prior written permission from the Publishers.

While the IST-Africa Institute and IIMC retain copyright of the Conference Proceedings, each author retains copyright to their individual paper. No paper or part thereof may be reproduced without the written permission of both the Conference Proceedings Publishers and appropriate author(s).