Age-Related Differences in Learner Satisfaction, Engagement, and Usability Perceptions among South African Online Learners across Different Provinces: A Cross-Sectional Quantitative Study

Authors

  • Karl Tischlhauser Infomage RIMS Group (IRG)
  • Prof. Wynand Goosen Infomage RIMS Group (IRG)
  • Thabiso Mypane Infomage RIMS Group (IRG)

DOI:

https://doi.org/10.47604/ijodl.3727

Keywords:

Online Learning, Age Differences, Learner Satisfaction, Engagement, Usability

Abstract

Purpose: This study examines whether learner satisfaction, engagement, skill development, and perceptions of usability and convenience differ across age groups in an online learning context.

Methodology: A mono-method quantitative, cross-sectional design was employed. Data were collected using a structured Likert-scale survey administered via Microsoft Forms to 596 learners enrolled in digital learning programs offered by Infomage & RIMS Group in South Africa. A non-probability sampling approach was used. Descriptive statistics and one-way ANOVA were conducted to assess differences across age cohorts (18–24, 25–34, and 35–44 years). Given unequal group sizes and variances, Welch’s ANOVA and Games-Howell post hoc tests were applied to ensure robustness.

Findings: The results revealed no statistically significant differences across age groups for satisfaction, engagement, or perceived skill development. However, perceptions of usability and convenience differed significantly (p = .027), with older learners reporting more positive experiences. While post hoc comparisons did not reach statistical significance, observed trends suggest that age may influence usability expectations. The null hypotheses for Composites 1, 2, and 3 failed to be rejected, while the null hypothesis for Composite 4 was rejected.

Unique Contribution to Theory, Practice and Policy: This study contributes to theory by identifying usability, rather than satisfaction or engagement, as the primary dimension influenced by age in digital learning environments. Practically, the findings highlight the importance of aligning platform design with diverse user expectations and cognitive preferences. From a policy perspective, the results support the development of inclusive digital learning strategies that account for usability differences across age groups to enhance accessibility and learner experience.

Downloads

Download data is not yet available.

References

Alam, A., & Mohanty, A. (2023). Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Engineering, 10.

Beutell, N. J. (2013). Generational differences in work–family conflict and synergy. Journal of Family Issues, 34(5), 619–642.

Bryant, J. (2013). Older students are more satisfied with online learning than traditional-aged students. Retrieved from https://www.ruffalonl.com/blog/student-success/older-students-satisfied-online-learning-traditional-aged-students/

Campbell, A. (2023). Understanding educational technology and its impact on the learning landscape. Retrieved from https://www.turnitin.com/blog/understanding-educational-technology-and-its-impact-on-the-learning-landscape

Carioli, S., & Peru, A. (2019). Teaching online reading strategies using the think-aloud technique: Evidence from an experimental study. Italian Journal of Educational Technology, 27, 279–294.

Castillo-Canales, D., et al. (2023). Ed-tech landscape and challenges. Retrieved from https://southernvoice.org/wp-content/uploads/2023/11/Ed-tech-LAC-Castillo-et-al-2023.pdf

Cole, M. T., Shelley, D. J., & Swartz, L. B. (2014). Online instruction, e-learning, and student satisfaction: A three-year study. International Review of Research in Open and Distributed Learning, 15(6), 111–131.

Czerniewicz, L., Agherdien, N., Badenhorst, J., Belluigi, D., Chambers, T., Chili, M., … Wissing, G. (2020). A wake-up call: Equity, inequality and COVID-19 emergency remote teaching in South Africa. Postdigital Science and Education, 2, 946–967.

Czerniewicz, L., & Brown, C. (2013). The habitus and technological practices of rural students: A case study. Information Technology for Development, 19(4), 317–331.

Danielle Morin, H. S., & R. S. (2019). Understanding online learning based on different age categories. Gale Academic OneFile, 307.

Department of Communications and Digital Technologies. (2020). South Africa National Digital and Future Skills Strategy. Government of South Africa.

D’Souza, S. A., et al. (2024). Assessment of time-related deficits in older adults: A scoping review protocol. BMJ Open, 14(9).

Fasbender, U., Gerpott, F. H., & Rinker, L. (2022). Getting ready for the future: Is it worth it? A dual pathway model of age and technology acceptance at work. Journal of Occupational and Organizational Psychology, 95(2), 358–375.

Framework. (2023). Top 3 EdTech trends to watch in 2023. Retrieved from https://www.linkedin.com/pulse/top-3-edtech-trends-watch-2023-framework-development-group-epg9f/

Frost, J. (2025). Benefits of Welch’s ANOVA compared to the classic one-way ANOVA. Retrieved from https://statisticsbyjim.com/anova/welchs-anova-compared-to-classic-one-way-anova/

Holt, C. J., et al. (2023). Evaluating and expanding usability and user satisfaction of an online research portal. Australasian Journal of Educational Technology, 39(1), 1–15.

Jan, S.-L. (2014). Sample size determinations for Welch’s test in one-way heteroscedastic ANOVA. British Journal of Mathematical and Statistical Psychology, 67(1), 72–93.

Liu, L., & Haque, M. (2017). Age difference in research course satisfaction in a blended Ed.D. program: A moderated mediation model of the effects of internet self-efficacy and statistics anxiety. Retrieved from https://ojdla.com/archive/summer202/liu_haque202.pdf

Microsoft Corporation. (2021). Microsoft Excel (Version 16.0) [Computer software]. Microsoft Corporation.

Nguyen, H. H., Tuong, H. A., Hoang-Thi, M., & Van Nguyen, T. (2022). Factors influencing online learner performance during the coronavirus pandemic: A case study in Vietnamese universities. European Journal of Educational Research, 11(1), 1–15.

Reddy, P., Chaudhary, K., & Hussein, S. (2023). A digital literacy model to narrow the digital literacy skills gap. Education and Information Technologies, 28, 1–19.

Research ICT Africa. (2021). The state of ICT in South Africa. Retrieved from https://researchictafrica.net

Sehloho, M. (2024). Five EdTech trends on the rise. Retrieved from https://www.connectingafrica.com/skills-and-training/five-edtech-trends-on-the-rise

Sewlal, C. (2024). Understanding self-directed learning: Fostering independence and curiosity in children. Retrieved from https://openmindscampus.co.za/understanding-self-directed-learning/

Shanganlall, A. (2023). EdTech statistics 2023. Retrieved from https://www.classter.com/blog/edtech/edtech-statistics-2023/

Tariq, M. F., Zia, T., & Mayah, K. (2024). Determining levels of satisfaction with online learning at private higher education in London. SSRN Electronic Journal.

The Jamovi Project. (2025). Jamovi (Version 2.6) [Computer software]. Retrieved from https://www.jamovi.org

World Bank. (2020). Digital economy for Africa initiative. Retrieved from https://www.worldbank.org

Yu, Q. (2022). Factors influencing online learning satisfaction. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC9039172/

Downloads

Published

2026-04-28

How to Cite

Tischlhauser, K., Goosen, W., & Mypane, T. (2026). Age-Related Differences in Learner Satisfaction, Engagement, and Usability Perceptions among South African Online Learners across Different Provinces: A Cross-Sectional Quantitative Study. International Journal of Online and Distance Learning, 6(1), 14–25. https://doi.org/10.47604/ijodl.3727

Issue

Section

Articles

Similar Articles

<< < 1 2 3 

You may also start an advanced similarity search for this article.