Assessing the impact of e-learning within the higher education sector: A perspective of South African universities
DOI:
https://doi.org/10.20525/ijrbs.v14i6.4119Keywords:
E-learning, South African Higher Education Institutions, Technology Acceptance ModelAbstract
This study aims to assess the e-learning difficulties faced by students at South African public higher education institutions, given the increasing reliance on online learning as a core mode of teaching and learning. The researchers critically analyzed previous research on technology in higher education to identify gaps in existing knowledge. The study adopted the Technology Acceptance Model (TAM) as the theoretical framework to guide the investigation. A quantitative research method was employed, using surveys distributed to experts from various faculties, departments, and students. This approach enabled the collection of data regarding the perceived challenges and barriers to e-learning. The findings revealed that students encountered significant challenges in e-learning, including issues related to digital literacy, limited access to resources, unreliable internet connectivity, and high data costs. These barriers are exacerbated by the disadvantaged and poverty-stricken backgrounds of many students in South African public institutions. Additionally, educators and course designers also face challenges in effectively implementing e-learning solutions. Addressing these challenges is essential for the successful adoption and integration of e-learning in South African higher education institutions. The study suggests that overcoming systemic issues—such as improving digital literacy, enhancing infrastructure, and reducing data costs—could unlock the full potential of e-learning. This could lead to more inclusive, accessible, and effective higher education. This study contributes to the existing body of knowledge by identifying specific barriers to e-learning adoption in South African public higher education institutions. It emphasizes the importance of addressing these challenges to harness the potential of technology-enhanced learning and promote inclusivity in higher education. The findings provide insights into how policy makers, educators, and institutions can better support students and course designers in the transition to e-learning environments.
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Aali, M., Narenji Thani, F., Keramati, M. R., & Garavand, A. (2020). A model for effectiveness of e-learning at university. Journal of Information Technology Management, 12(4), 121–140. https://doi.org/10.22059/jitm.2020.298696.2479
Al Kurdi, B., Alshurideh, M., & Salloum, S. A. (2020). Investigating a theoretical framework for e-learning technology acceptance. International Journal of Electrical and Computer Engineering, 10(6), 6484–6496. https://doi.org/10.11591/ijece.v10i6.pp6484-6496 DOI: https://doi.org/10.11591/ijece.v10i6.pp6484-6496
Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2019, October). Examining the main mobile learning system drivers’ effects: A mix empirical examination of both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM). In International Conference on Advanced Intelligent Systems and Informatics (pp. 406–417). Springer. https://doi.org/10.1007/978-3-030-31129-2_37 DOI: https://doi.org/10.1007/978-3-030-31129-2_37
Anthonysamy, L., Koo, A. C., & Hew, S. H. (2020). Self-regulated learning strategies in higher education: Fostering digital literacy for sustainable lifelong learning. Education and Information Technologies, 25(4), 2393–2414. https://doi.org/10.1007/s10639-020-10201-8 DOI: https://doi.org/10.1007/s10639-020-10201-8
Brenya, B. (2024). Higher education teacher educators’ perceptions about approaches to teaching in a blended learning mode in a developing country. E-Learning and Digital Media. https://doi.org/10.1177/20427530241239433 DOI: https://doi.org/10.1177/20427530241239433
Davis, F. D. (1989). Technology acceptance model: TAM. In M. N. Al-Suqri & A. S. Al-Aufi (Eds.), Information Seeking Behavior and Technology Adoption (pp. 205–219). https://quod.lib.umich.edu/b/busadwp/images/b/1/4/b1409190.0001.001.pdf
Glazer, F. S. (Ed.). (2023). Blended learning: Across the disciplines, across the academy. Taylor & Francis.
Greene, J. A., Seung, B. Y., & Copeland, D. Z. (2014). Measuring critical components of digital literacy and their relationships with learning. Computers & Education, 76, 55–69. https://doi.org/10.1016/j.compedu.2014.03.008 DOI: https://doi.org/10.1016/j.compedu.2014.03.008
Hamutoglu, N., Unveren-Bilgic, E., Salar, H., & Sahin, Y. U. S. U. F. (2021). The effect of e-learning experience on readiness, attitude, and self-control/self-management. Journal of Information Technology Education: Innovations in Practice, 20, 1–22. https://doi.org/10.28945/4822 DOI: https://doi.org/10.28945/4822
Helsper, E., & Smirnova, S. (2019). Youth inequalities in digital interactions and well-being.
Ingham-Broomfield, R. (2014). A nurses' guide to quantitative research. Australian Journal of Advanced Nursing, 32(2), 32–38. https://search.informit.org/doi/epdf/10.3316/ielapa.116609264549547 DOI: https://doi.org/10.37464/2015.322.1573
Kanyane, M. (2023). Digital work–Transforming the higher education landscape in South Africa. In Publishing. https://doi.org/10.1007/978-3-031-26490-0_9 DOI: https://doi.org/10.1007/978-3-031-26490-0_9
Kibuku, R. N., Ochieng, D. O., & Wausi, A. N. (2020). E-learners’ challenges and coping strategies in interactive and collaborative e-learning in Kenya. Journal of Education and Training Studies, 8(11), 1–11. https://doi.org/10.11114/jets.v8i11.5004 DOI: https://doi.org/10.11114/jets.v8i11.5004
Lee, J., Moon, J., & Cho, B. (2015). The mediating role of self-regulation between digital literacy and learning outcomes in the digital textbook for middle school English. Educational Technology International, 16(1), 58–83. https://doi.org/10.21240/mpaed/35/2019.10.17.X DOI: https://doi.org/10.21240/mpaed/35/2019.10.17.X
List, A. (2019). Defining digital literacy development: An examination of pre-service teachers’ beliefs. Computers & Education, 138, 146–158. https://doi.org/10.1016/j.compedu.2019.03.009 DOI: https://doi.org/10.1016/j.compedu.2019.03.009
Luescher, T., Mncwango, B., Fongwa, S., Oppelt, T., Mthombeni, Z., & Paterson, M. (2021). The state of transformation in South Africa’s public universities. https://hdl.handle.net/20.500.11910/21354
Mahyoob, M. (2020). Challenges of e-learning during the COVID-19 pandemic experienced by EFL learners. Arab World English Journal, 11(4). https://dx.doi.org/10.24093/awej/vol11no4.23 DOI: https://doi.org/10.24093/awej/vol11no4.23
Mavutha, W., & Mabotja, T. (2024). Digital literacy: A foreign language for students from rural areas in South Africa. International Journal of Research in Business and Social Science, 13(5), 784–793. https://doi.org/10.20525/ijrbs.v13i5.3315 DOI: https://doi.org/10.20525/ijrbs.v13i5.3315
Mohajan, H. K. (2020). Quantitative research: A successful investigation in natural and social sciences. Journal of Economic Development, Environment and People, 9(4), 50–79. https://www.ceeol.com/search/article-detail?id=939590 DOI: https://doi.org/10.26458/jedep.v9i4.679
Nikou, S., Kim, S., Lim, C., & Maslov, I. (2021). Satisfaction with e-learning systems during the COVID-19 pandemic – A comparative study. https://hdl.handle.net/10419/238042
Nguyen, B. H. M. (2022). Students’ experiences of flow, satisfaction, and learning outcome in an online learning context (Master’s thesis, NTNU). https://hdl.handle.net/11250/3011171
Padayachee, P., Wagner-Welsh, S., & Johannes, H. (2018). Online assessment in Moodle: A framework for supporting our students. South African Journal of Higher Education, 32(5), 211–235. https://hdl.handle.net/10520/EJC-117d96000f DOI: https://doi.org/10.20853/32-5-2599
Panyajamorn, T., Suanmali, S., Kohda, Y., Chongphaisal, P., & Supnithi, T. (2018). Effectiveness of e-learning design in Thai public schools. Malaysian Journal of Learning and Instruction, 15(1), 1–34.
Pillay, D. (2022). Developing creativity in entrepreneurs – A scoping review (Doctoral dissertation, Stellenbosch University). http://hdl.handle.net/10019.1/124551
Puriwat, W., & Tripopsakul, S. (2021). Explaining an adoption and continuance intention to use contactless payment technologies: During the COVID-19 pandemic. Emerging Science Journal, 5(1), 85–95. https://doi.org/10.18178/ijiet.2021.11.8.1536 DOI: https://doi.org/10.28991/esj-2021-01260
Setiawan, H., & Surtikanti, H. K. (2023, May). Blended learning research trends in biology education: A systematic review of literature from 2016 to 2022. In 3rd International Conference on Biology, Science and Education (IcoBioSE 2021) (pp. 341–353). Atlantis Press. https://doi.org/10.2991/978-94-6463-166-1_45 DOI: https://doi.org/10.2991/978-94-6463-166-1_45
Siron, Y., Wibowo, A., & Narmaditya, B. S. (2020). Factors affecting the adoption of e-learning in Indonesia: Lesson from Covid-19. JOTSE: Journal of Technology and Science Education, 10(2), 282–295. http://dx.doi.org/10.3926/jotse.1025 DOI: https://doi.org/10.3926/jotse.1025
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended technology acceptance model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11). https://doi.org/10.1016/j.heliyon.2020.e05410 DOI: https://doi.org/10.1016/j.heliyon.2020.e05410
Tejedor, S., Cervi, L., Pérez-Escoda, A., & Jumbo, F. T. (2020). Digital literacy and higher education during COVID-19 lockdown: Spain, Italy, and Ecuador. Publications, 8(4), 48. DOI: https://doi.org/10.3390/publications8040048
Teo, T., Huang, F., & Hoi, C. K. W. (2018). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475. https://doi.org/10.1080/10494820.2017.1341940 DOI: https://doi.org/10.1080/10494820.2017.1341940
Thaba-Nkadimene, K. L. (2020). The influence of educational provision on teacher performance and learner outcomes among Limpopo primary schools. South African Journal of Education, 40(4). https://doi.org/10.15700/saje.v40n4a2039 DOI: https://doi.org/10.15700/saje.v40n4a2039
Troll, E. S., Friese, M., & Loschelder, D. D. (2021). How students’ self-control and smartphone use explain their academic performance. Computers in Human Behavior, 117, 106624. https://doi.org/10.1016/j.chb.2020.106624 DOI: https://doi.org/10.1016/j.chb.2020.106624
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Copyright (c) 2025 Tshepo Mabotja, Winiswa Mavutha
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