Usage of data analytics to track university students’ performance in Africa : a scoping review

Abstract

Using data analytics to analyse university student performance in Africa is an attractive potential, given that the continent's institutions face unique problems such as enormous student populations, various educational backgrounds, and varying levels of funding. However, using data analytics can dramatically increase academic performance and institutional effectiveness. The increasing availability of educational data, as well as advancements in data analytics, have opened up new prospects to optimise academic performance tracking in Higher Education Institutions (HEIs). This scoping re-view paper investigates the use of data analytics to track and enhance university student performance across African institutions. The study synthesises findings from peer-reviewed studies published between 2010 and 2025, with an emphasis on techniques, tools, implementation contexts, and results. The paper emphasises the widespread use of Machine Learning (ML) models, predictive analytics, and Learning Management Systems (LMSs) for identifying at-risk students, understanding learning behaviours, and informing institutional decisions. It also uncovers challenges specific to the African context, including data quality, technological infrastructure, and policy limitations. The findings highlight the potential of data-driven approaches to support student success but emphasise the need for localised strategies and capacity building. This review contributes to a growing body of knowledge on educational data analytics and provides a foundation for future research and practice in African HEIs. The future research should focus more on expanding the research depth, real-world applications, interdisciplinary integration, and addressing contextual challenges related to the use of data analytics to track African university students.