Exploring factors influencing academic literacy – A data-driven perspective

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