Strengthening Statistics Education for Bachelor-Level Nursing Students in Open and Distance Learning: Insights from an Applied Healthcare Curriculum

Statistics plays an important role in nursing education as it supports clinical judgement, patient assessment, and the interpretation of healthcare findings. Many nursing undergraduates find this subject difficult, especially in Open and Distance Learning (ODL), where academic commitments must be managed together with shift duties and personal responsibilities. This study explores learning behaviour, performance patterns, and topic-level mastery among more than 300 nursing students enrolled in an online statistics course built around healthcare examples. The course uses e-lessons, a digital flipbook, guided discussions, online quizzes, and a final examination. Results show that learners perform well in descriptive topics that use common patient data but face difficulty with inferential topics such as sampling, probability, confidence intervals, and hypothesis testing. Engagement peaks were recorded before assessments, showing that many students study close to deadlines. The study outlines suggestions for improving support, such as structured scaffolding, healthcare-based examples, and early alerts for students who require additional help.

Statistics, nursing students, open and distance learning, healthcare data

1. Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32, 347–364. https://doi.org/10.1007/BF00138871 [Google Scholar] [Crossref]

2. Chiesi, F., & Primi, C. (2020). Enhancing statistics comprehension in higher education: A review of recent research. Frontiers in Psychology, 11, 1473. https://doi.org/10.3389/fpsyg.2020.01473 [Google Scholar] [Crossref]

3. Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction (4th ed.). Wiley. [Google Scholar] [Crossref]

4. Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics for study success: Reflections on current empirical findings. Revue Internationale des Technologies en Pédagogie Universitaire, 17(2), 38–52. https://doi.org/10.18162/ritpu-2020-v17n2-06 [Google Scholar] [Crossref]

5. Johar, N. A., Kew, S. N., Tasir, Z., & Koh, E. (2023). Learning analytics on student engagement to enhance students’ learning performance: A systematic review. Sustainability, 15(10), 7849. https://doi.org/10.3390/su15107849 [Google Scholar] [Crossref]

6. Jonassen, D. H. (1999). Designing constructivist learning environments. In C. M. Reigeluth (Ed.), Instructional-design theories and models (Vol. 2, pp. 215–239). Lawrence Erlbaum. [Google Scholar] [Crossref]

7. Knowles, M. S., Holton, E. F., & Swanson, R. A. (2015). The adult learner: The definitive classic in adult education and human resource development (8th ed.). Routledge. [Google Scholar] [Crossref]

8. Kozlovski, E., Zinn, B., & Zieffler, A. (2023). Designing multimedia resources to support introductory statistics learning. Journal of Statistics Education, 31(1), 45–60. https://doi.org/10.1080/10691898.2023.2180754 [Google Scholar] [Crossref]

9. Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press. [Google Scholar] [Crossref]

10. Merriam, S. B., & Bierema, L. L. (2014). Adult learning: Linking theory and practice. Jossey-Bass. [Google Scholar] [Crossref]

11. Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1 [Google Scholar] [Crossref]

12. Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40. [Google Scholar] [Crossref]

13. Soffer, T., & Cohen, A. (2019). Students’ engagement characteristics predict success and completion of online courses. Journal of Computer Assisted Learning, 35(3), 378–389. https://doi.org/10.1111/jcal.12340 [Google Scholar] [Crossref]

14. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. [Google Scholar] [Crossref]

15. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. [Google Scholar] [Crossref]

• The Net Relative Run-Ratio Method (NRRR), a Foolproof Technique to Replace the Net Run Rate (NRR) Method in Evaluating the Authority of Match-Wins
• Statistical Role of CB-SEM Vs PLS-SEM in the Field of Social Science
• Predictive Modelling and Statistical Analysis of Housing Prices in Lagos State, Nigeria
• Collocational Patterns of Guru in American Business vs. Spiritual Discourse
• A Comparative Analysis of Heuristic and Dynamic Algorithms for Route Optimization in Johor’s Delivery Hubs