Lecturers’ Perspectives on the Benefits and Challenges of Implementing Learning Analytics in South African Higher Education

Authors

  • Fezile Treasure Matsebula
  • Ernest Mnkandla
  • Themba Masombuka

Keywords:

Learning Analytics; Artificial Intelligence; Digital Transformation, South Africa, Lecturer, Higher Education

Abstract

This study explores lecturers’ perspectives on the benefits and challenges of implementing Learning Analytics (LA) in South African higher education institutions. LA offers potential to improve student outcomes, teaching effectiveness, and institutional decision-making through the use of data-driven insights. In the South African context, where universities face persistent challenges related to student retention, performance, and resource constraints, LA could be transformative. However, successful adoption depends heavily on lecturer engagement, digital literacy, and institutional readiness. Using a qualitative case study approach, data were collected through open-ended questionnaire with 41 academic staff across four public institutions of higher education. Thematic analysis of the data revealed that lecturers recognize several key benefits of LA, including early identification of at-risk students, improved academic planning, enhanced student support, and better monitoring of performance. At the same time, they highlighted critical barriers such as lack of awareness, ethical concerns about data use, limited technical infrastructure, inadequate institutional support, and increased workload. These findings underscore the importance of addressing systemic, ethical, and capacity-related issues to ensure the responsible and effective implementation of LA. The study contributes empirical evidence from a developing context and offers practical insights for institutional leaders, policymakers, and educators seeking to leverage LA as a strategic tool for educational transformation in South Africa.

https://doi.org/10.26803/ijlter.25.1.15

References

Aldowah, H., Al-Samarraie, H., Alzahrani, A. I., & Alalwan, N. (2021). Factors affecting student dropout in MOOCs: A cause and effect decision?making model. Journal of Computing in Higher Education, 33(2), 249–283. https://doi.org/10.1007/s12528-019-09241-y

Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17. https://doi.org/10.5281/zenodo.3554657

Baker, R. S. J. d., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 61–75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4

Berg, M., & Seeber, B. K. (2016). The slow professor: Challenging the culture of speed in the academy (New foreword by S. Collini). University of Toronto Press.

Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, Science & Technology Education, 5(3), 235–245. https://doi.org/10.12973/ejmste/75275

Bozalek, V., Ng'ambi, D., Wood, D., Herrington, J., Hardman, J., & Amory, A. (2014). Activity theory, authentic learning and emerging technologies: Towards a transformative higher education pedagogy. Routledge. https://doi.org/10.4324/9781315771823

Brown, C., & Czerniewicz, L. (2010). Debunking the “digital native”: Beyond digital apartheid, towards digital democracy. Journal of Computer Assisted Learning, 26(5), 357–369. https://doi.org/10.1111/j.1365-2729.2010.00369.x

Bryk, A. S., Gomez, L. M., Grunow, A., & LeMahieu, P. G. (2015). Learning to improve: How America's schools can get better at getting better. Harvard Education Press. https://doi.org/10.17763/9781612507910

Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40–57. https://eric.ed.gov/?id=EJ769402

Cardol, H., Mignon, I., & Lantz, B. (2025). Rethinking the forecasting of innovation diffusion: A combined actor- and system approach. Technological Forecasting and Social Change, 214, 124058. https://doi.org/10.1016/j.techfore.2025.124058

Clow, D. (2012). The learning analytics cycle: Closing the loop effectively. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 134–138). https://dl.acm.org/doi/10.1145/2330601.2330636

Czerniewicz, L., Deacon, A., Fife, M., Small, J., & Walji, S. (2017). MOOC-making and open educational practices. Journal of Computing in Higher Education, 29(1), 81–97. https://doi.org/10.1007/s12528-016-9128-7

Department of Higher Education and Training (DHET). (2024). Higher education enrolment and graduation statistics 2023. DHET. https://www.dhet.gov.za

Dube, B. (2020). Rural online learning in the context of COVID-19 in South Africa: Agonising over prospects and challenges. International Journal of Research in Business and Social Science, 9(5), 1–13. https://doi.org/10.17583/remie.2020.5607

Elias, T. (2011). Learning analytics definitions, processes and potentials. Society for Learning Analytics Research (SoLAR). https://www.solaresearch.org

Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317. https://doi.org/10.1504/IJTEL.2012.051816

Gasevic, D., Dawson, S., & Siemens, G. (2015). Let's not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10.1007/s11528-014-0822-x

Kaliisa, R., Rienties, B., Mørch, A. I., & Kluge, A. (2022). Student dropout in MOOCs: A cause and effect decision?making model. Computers and Education: Open, 3, 100073. https://doi.org/10.1016/j.caeo.2022.100073

Knight, S., Buckingham Shum, S., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 1–23. https://doi.org/10.18608/JLA.2014.12.3

Kshetri, N. (2013). Privacy and security issues in cloud computing: The role of institutions and institutional evolution. Telecommunications Policy, 37(4–5), 372–386. https://doi.org/10.1016/j.telpol.2012.04.011

Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008

Mardiana, H. (2024). Perceived impact of lecturers’ digital literacy skills in higher education institutions. SAGE Open, 14, 1–12. https://doi.org/10.1177/215824402412569

Matsebula, F., Mnkandla, E., & Masumbuka, T. (2025). A learning analytics framework for higher education in South Africa. In Innovative technologies and learning (pp. 251–263). Springer. https://doi.org/10.1007/978-3-031-98185-2_27

Matsebula, F., & Mnkandla, E. (2016). Information systems innovation adoption in higher education: Big data and analytics. In 2016 International Conference on Advances in Computing and Communication Engineering (ICACCE) (pp. 326–329). https://doi.org/10.1109/ICACCE.2016.8073769

Matsebula, F., & Mnkandla, E. (2017). A big data architecture framework for learning analytics in higher education. 2017 IEEE AFRICON Conference. https://doi.org/10.1109/AFRCON.2017.8095610

Mhlongo, S., Mbatha, K., Ramatsetse, B., & Dlamini, R. (2023). Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review. Heliyon, 9(6), e16348. https://doi.org/10.1016/j.heliyon.2023.e16348

Mpungose, C. B. (2020). Emergent transition from face-to-face to online learning in a South African university in the context of coronavirus. Humanities and Social Sciences Communications, 7(1), 1–9. https://doi.org/10.1057/s41599-020-00603-x

Mutongoza, B. H. (2025). Nothing but noise: Challenges impeding the transformation of higher education in South Africa. Interdisciplinary Journal of Education Research, 7(1), a06. https://doi.org/10.38140/ijer-2025.vol7.1.06

Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016

Peña-Ayala, A. (2024). A learning design cooperative framework to instill 21st century education. Telematics and Informatics, 62(1), 1–16. https://doi.org/10.1016/j.tele.2021.101632

Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R. (2024). Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework. Heliyon, 10(18), e37569. https://doi.org/10.1016/j.heliyon.2024.e37569

Romero, C., & Ventura, S. (2024). Educational data mining and learning analytics: An updated survey. Journal of Educational Data Mining, 16(1), 1–34. https://doi.org/10.5281/zenodo.8102345

Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior, 107, 105512. https://doi.org/10.1016/j.chb.2018.05.004

Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252–254). https://doi.org/10.1145/2330601.23306

Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–32. https://doi.org/10.17471/2499-4324/195

Sithole, V. L., & Mbukanma, I. (2024). Prospects and challenges to ICT adoption in teaching and learning at rural South African universities: A systematic review. Research in Education and Social Sciences at Tertiary Level (RESSAT), 9(3), 178–193. https://ressat.org

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1509–1528. https://doi.org/10.1177/0002764213479366

Statistics South Africa (StatsSA). (2023). General household survey 2023: Education and ICT access. Statistics South Africa. https://www.statssa.gov.za

Traxler, J., & Wishart, J. (Eds.). (2011). Making mobile learning work: Case studies of practice. ESCalate. https://www.researchgate.net/publication/279693158_Making_mobile_learning_work_case_studies_of_practice

Warschauer, M. (2003). Technology and social inclusion: Rethinking the digital divide. MIT Press.

Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 93–108). Lawrence Erlbaum Associates https://doi.org/10.4324/9781410602350-12

Zimmerman, B. J., & Schunk, D. (2011). Motivational sources and outcomes of self-regulated learning and performance. In B. Zimmerman & D. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 49–64). Routledge. https://doi.org/10.4324/9780203839010-8

Downloads

Published

2026-01-30

How to Cite

Matsebula, F. T. ., Mnkandla, E. ., & Masombuka, T. . (2026). Lecturers’ Perspectives on the Benefits and Challenges of Implementing Learning Analytics in South African Higher Education. International Journal of Learning, Teaching and Educational Research, 25(1), 293–309. Retrieved from http://www.ijlter.myres.net/index.php/ijlter/article/view/2665