Original Research

Exploring factors influencing academic literacy – A data-driven perspective

Janus Roestenburg, Cornelius J. Kruger, Mariska Nel, Zander Janse van Rensburg
South African Journal of Information Management | Vol 26, No 1 | a1729 | DOI: https://doi.org/10.4102/sajim.v26i1.1729 | © 2024 Janus Roestenburg, Cornelius J. Kruger, Mariska Nel, Zander Janse van Rensburg | This work is licensed under CC Attribution 4.0
Submitted: 19 June 2023 | Published: 29 March 2024

About the author(s)

Janus Roestenburg, Department of Computer Science and Information Systems, Faculty of Natural and Agricultural Sciences, North-West University, Potchefstroom, South Africa
Cornelius J. Kruger, Department of Computer Science and Information Systems, Faculty of Natural and Agricultural Sciences, North-West University, Potchefstroom, South Africa
Mariska Nel, School of Languages: Academic Literacy, Faculty of Humanities, North-West University, Mahikeng, South Africa
Zander Janse van Rensburg, Writing Centre, Faculty of Humanities, North-West University, Potchefstroom, South Africa

Abstract

Background: Data science and machine learning have shown their usefulness in business and are gaining prevalence in the educational sector. In illustrating the potential of educational data mining (EDM) and learning analytics (LA), this article illustrates how such methods can be applied to the South African higher education institution (HEI) environment to enhance the teaching and learning of academic literacy modules.

Objectives: The objective of this study is to determine if data science and machine learning methods can be effectively applied to the context of academic literacy teaching and learning and provide stakeholders with valuable decision support.

Method: The method applied in this study is a variation of the knowledge discovery and data mining process specifically adapted for discovery in the educational environment.

Results: This study illustrates that utilising educational data can support the educational environment by measuring pedagogical support, examining the learning process, supporting strategic decision-making, and predicting student performance.

Conclusion: Educators can improve module offerings and students’ academic acculturation by applying EDM and LA to data collected from academic literacy modules.

Contribution: This manuscript contributes to the field of EDM and LA by illustrating that methods from these research fields can be applied to the South African educational context and produce valuable insights using local data, providing practical proof of its feasibility and usefulness. This is aligned with the scope of this journal as it pertains to innovations in information management and competitive intelligence.


Keywords

educational data mining; learning analytics; academic literacy; machine learning; applied linguistics; student support; student success; academic acculturation

JEL Codes

I21: Analysis of Education

Sustainable Development Goal

Goal 4: Quality education

Metrics

Total abstract views: 253
Total article views: 161


Crossref Citations

No related citations found.