Comparing Student Challenges During COVID-19 Across Higher Education Institutions in Four Countries: A Mixed Methods Analysis Using the Shannon Diversity Index
DOI:
https://doi.org/10.33423/ka3xn658Keywords:
higher education, COVID-19, university students, mixed methods research, Shannon Diversity Index, Shannon-Based Thematic Diversity Index, thematic analysis, quantitizing qualitative data, student challenges, online learning, cross-national comparison, educational disruption, pandemic responseAbstract
This mixed methods research study compares the diversity of challenges experienced by university students during COVID-19 across five institutional contexts in four countries: South Africa (two universities), the United States, Brazil, and Chile. Using previously published thematic findings, themes were quantitized and analyzed via the Shannon Diversity Index (SDI), yielding what is termed the Shannon-Based Thematic Diversity Index (SBTDI), which captures both the range and evenness of challenges. Results indicate variability in thematic diversity across institutions, with the New York sample showing the highest diversity and Wits University the lowest. Findings suggest that student challenges are multidimensional, underscoring the importance of designing comprehensive interventions for future pandemics and other large-scale disruptions.
References
Ahmed, H., Mohammed, O., Mohammed, L., Mohamed Salih, D., Ahmed, M., Masaod, R., … Elkhidir, I. (2022). Prevalence of medical students’ satisfaction with online education during COVID-19 pandemic: A systematic review and meta-analysis. MedEdPublish, 12, 16. https://doi.org/10.12688/mep.19028.2 DOI: https://doi.org/10.12688/mep.19028.1
Aisha, N., & Ratra, A. (2022). Online education amid COVID-19 pandemic and its opportunities, challenges and psychological impacts among students and teachers: A systematic review. Asian Association of Open Universities Journal, 17(3), 242–260. https://doi.org/10.1108/AAOUJ-03-2022-0028 DOI: https://doi.org/10.1108/AAOUJ-03-2022-0028
Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020). Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability, 12(20), 8438. https://doi.org/10.3390/su12208438 DOI: https://doi.org/10.3390/su12208438
Aucejo, E.M., French, J., Araya, M.P.U., & Zafar, B. (2020). The impact of COVID-19 on student experiences and expectations: Evidence from a survey. Journal of Public Economics, 191, 104271. https://doi.org/10.1016/j.jpubeco.2020.104271 DOI: https://doi.org/10.1016/j.jpubeco.2020.104271
Bao, W. (2020). COVID‐19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging Technologies, 2(2), 113–115. https://doi.org/10.1002/hbe2.191 DOI: https://doi.org/10.1002/hbe2.191
Chileya, P. (2023). Pandemic-induced online learning and its impact on mental health of high school and tertiary students. Pan-African Journal of Health and Environmental Science, 1(2), 69–82. https://doi.org/10.56893/ajhes.2022-v1i2.256 DOI: https://doi.org/10.56893/ajhes.2022-v1i2.256
Collingridge, D.S. (2013). A primer on quantitized data analysis and permutation testing. Journal of Mixed Methods Research, 7(1), 81–97. https://doi.org/10.1177/1558689812454457 DOI: https://doi.org/10.1177/1558689812454457
Cramarenco, R.E., Burcă-Voicu, M.I., & Dabija, D.C. (2023). Student perceptions of online education and digital technologies during the COVID-19 pandemic: A systematic review. Electronics, 12(2), 319. https://doi.org/10.3390/electronics12020319 DOI: https://doi.org/10.3390/electronics12020319
Creamer, E.G. (2018). An introduction to fully integrated mixed methods research. Sage. DOI: https://doi.org/10.4135/9781071802823
Gorelick, R. (2006). Combining richness and abundance into a single diversity index using matrix analogues of Shannon's and Simpson's indices. Ecography, 29(4), 525–530. https://doi.org/10.1111/j.0906-7590.2006.04601.x DOI: https://doi.org/10.1111/j.0906-7590.2006.04601.x
Hitchcock, J.H., & Onwuegbuzie, A.J. (2020). Developing mixed methods crossover analysis approaches. Journal of Mixed Methods Research, 14(1), 63–83. https://doi.org/10.1177/1558689819841782 DOI: https://doi.org/10.1177/1558689819841782
Kajjimu, J., Dreifuss, H., Tagg, A., Dreifuss, B., & Bongomin, F. (2023). Undergraduate learning in the COVID-19 pandemic: Lessons learned and ways forward. Advances in Medical Education and Practice, 355–361. https://doi.org/10.2147/AMEP.S395445 DOI: https://doi.org/10.2147/AMEP.S395445
Leech, N.L., Onwuegbuzie, A.J., & Combs, J.C. (2011). Writing publishable mixed research articles: Guidelines for emerging scholars in the health sciences and beyond. International Journal of Multiple Research Approaches, 5, 7–24. https://doi.org/10.5172/mra.2011.5.1.7 DOI: https://doi.org/10.5172/mra.2011.5.1.7
Lorenzini, E., Guedes dos Santos, J.L., Schmidt, C.R., Will, D.E.M., Bazzo de Espíndola, M., Cerny, R.Z., … Ojo, E.O. (2022). University students’ readiness and attitudes to learn in the context of remote teaching during the COVID-19 pandemic. International Journal of Multiple Research Approaches, 14(3), 101–121. https://doi.org/10.29034/ijmra.v14n3editorial3 DOI: https://doi.org/10.29034/ijmra.v14n3editorial3
MacDonald, Z.G., Nielsen, S.E., & Acorn, J. (2016). Negative relationships between species richness and evenness render common diversity indices inadequate for assessing long-term trends in butterfly diversity. Biodiversity and Conservation, 26(3), 617–629. https://doi.org/10.1007/s10531-016-1261-0 DOI: https://doi.org/10.1007/s10531-016-1261-0
Marcon, E., Scotti, I., Hérault, B., Rossi, V., & Lang, G. (2014). Generalization of the partitioning of Shannon diversity. PLoS ONE, 9(3), e90289. https://doi.org/10.1371/journal.pone.0090289 DOI: https://doi.org/10.1371/journal.pone.0090289
McClure, D.R., Ojo, E.O., Schaefer, M.B., Bell, D., Abrams, S.S., & Onwuegbuzie, A.J. (2021). Online learning challenges experienced by university students in the New York City area during the COVID-19 pandemic: A mixed methods study. International Journal of Multiple Research Approaches, 13(2), 150–167. https://doi.org/10.29034/ijmra.v13n2editorial4 DOI: https://doi.org/10.29034/ijmra.v13n2editorial4
Means, B., & Neisler, J. (2021). Teaching and learning in the time of COVID: The student perspective. Online Learning, 25(1). https://doi.org/10.24059/olj.v25i1.2496 DOI: https://doi.org/10.24059/olj.v25i1.2496
Mendes, R.S., Evangelista, L.R., Thomaz, S., Agostinho, A., & Gomes, L. (2008). A unified index to measure ecological diversity and species rarity. Ecography, 31(4), 450–456. https://doi.org/10.1111/j.0906-7590.2008.05469.x DOI: https://doi.org/10.1111/j.0906-7590.2008.05469.x
Ojo, E.O., & Onwuegbuzie, A.J. (2020). University life in an era of disruption of COVID-19: A meta-methods and multi-mixed methods research study of perceptions and attitudes of South African students. International Journal of Multiple Research Approaches, 12(1), 20–55. https://doi.org/10.29034/ijmra.v12n1editorial3 DOI: https://doi.org/10.29034/ijmra.v12n1editorial3
Onwuegbuzie, A.J. (2003). Effect sizes in qualitative research: A prolegomenon. Quality & Quantity: International Journal of Methodology, 37, 393–409. https://doi.org/10.1023/A:1027379223537 DOI: https://doi.org/10.1023/A:1027379223537
Onwuegbuzie, A.J. (2021). Beyond identifying emergent themes in mixed methods research studies: The role of economic indices: The Thematic Herfindahl-Hirschman Index and the Thematic Concentration Ratio. International Journal of Multiple Research Approaches, 13(2), 137–149. https://doi.org/10.29034/ijmra.v13n2editorial3 DOI: https://doi.org/10.29034/ijmra.v13n2editorial3
Onwuegbuzie, A.J. (2024). Comparing ecology-based quantitized themes via the Shannon Diversity Index: Introducing the Shannon-Based Thematic Diversity Index. International Journal of Multiple Research Approaches, 16(1), 47–80. https://doi.org/10.29034/ijmra.v16n1a2 DOI: https://doi.org/10.29034/ijmra.v16n1a2
Onwuegbuzie, A.J. (2025). On quantitizing revisited. Frontiers in Psychology, 15, 1421525. https://doi.org/10.3389/fpsyg.2024.1421525 DOI: https://doi.org/10.3389/fpsyg.2024.1421525
Onwuegbuzie, A.J., & Combs, J.P. (2010). Emergent data analysis techniques in mixed methods research: A synthesis. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (2nd ed., pp. 397–430). Sage. DOI: https://doi.org/10.4135/9781506335193.n17
Onwuegbuzie, A.J., & Hitchcock, J.H. (2019a). Toward a fully integrated approach to mixed methods research via the 1 + 1 = 1 integration approach: Mixed Research 2.0. International Journal of Multiple Research Approaches, 11(1), 7–28. https://doi.org/10.29034/ijmra.v11n1editorial1 DOI: https://doi.org/10.29034/ijmra.v11n1editorial2
Onwuegbuzie, A.J., & Hitchcock, J.H. (2019b). Using mathematical formulae as proof for integrating mixed methods research and multiple methods research approaches: A call for multi-mixed methods and meta-methods in a mixed research 2.0 era. International Journal of Multiple Research Approaches, 11(3), 213–234. https://doi.org/10.29034/ijmra.v11n3editorial2 DOI: https://doi.org/10.29034/ijmra.v11n3editorial2
Onwuegbuzie, A.J., & Hitchcock, J.H. (2022). Towards a comprehensive meta-framework for full integration in mixed methods research. In J.H. Hitchcock & A.J. Onwuegbuzie (Eds.), Routledge handbook for advancing integration in mixed methods research (pp. 565–606). Routledge. DOI: https://doi.org/10.4324/9780429432828-43
Onwuegbuzie, A.J., Hitchcock, J.H., Natesan, P., & Newman, I. (2018). Using fully integrated Bayesian thinking to address the 1 + 1 = 1 integration challenge. International Journal of Multiple Research Approaches, 10, 666–678. https://doi.org/10.29034/ijmra.v10n1a43 DOI: https://doi.org/10.29034/ijmra.v10n1a43
Onwuegbuzie, A.J., & Johnson, R.B. (2021a). Mapping the emerging landscape of mixed analysis. In A.J. Onwuegbuzie & R.B. Johnson (Eds.), The Routledge reviewer’s guide to mixed analysis (pp. 1–22). Routledge. DOI: https://doi.org/10.4324/9780203729434-1
Onwuegbuzie, A.J., & Johnson, R.B. (Eds.). (2021b). The Routledge reviewer’s guide to mixed methods analysis. Routledge. DOI: https://doi.org/10.4324/9780203729434
Onwuegbuzie, A.J., & Leech, N.L. (2004). Enhancing the interpretation of “significant” findings: The role of mixed methods research. The Qualitative Report, 9(4), 770–792. Retrieved from https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1913&context=tqr
Onwuegbuzie, A.J., Leech, N.L., & Collins, K.M.T. (2011). Innovative qualitative data collection techniques for conducting literature reviews. In M. Williams & W.P. Vogt (Eds.), The Sage handbook of innovation in social research methods (pp. 182–204). Sage. DOI: https://doi.org/10.4135/9781446268261.n13
Onwuegbuzie, A.J., & Ojo, E.O. (2021). University students’ experiences of learning in an online environment in COVID-19 pandemic: A meta-methods research study of perceptions and attitudes of South African students. Journal of Pedagogical Research, 5(4), 1–18. https://doi.org/10.33902/JPR.C DOI: https://doi.org/10.33902/JPR.2021472164
Onwuegbuzie, A.J., Ojo, E.O., Burger, A., Crowley, T., Adams, S.P., & Bergsteedt, B. (2020). Challenges experienced by students at Stellenbosch University that hinder their ability successfully to learn online during the COVID-19 era: A demographic and spatial analysis. International Journal of Multiple Research Approaches, 12(3), 240–281. https://doi.org/10.29034/ijmra.v12n3editorial2 DOI: https://doi.org/10.29034/ijmra.v12n3editorial2
Onwuegbuzie, A.J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 351–383). Sage.
Pla, L. (2004). Bootstrap confidence intervals for the Shannon biodiversity index: A simulation study. Journal of Agricultural, Biological, and Environmental Statistics, 9(1), 42–56. https://doi.org/10.1198/1085711043136 DOI: https://doi.org/10.1198/1085711043136
Sandelowski, M., Voils, C.I., & Knafl, G. (2009). On quantitizing. Journal of Mixed Methods Research, 3(3), 208–222. https://doi.org/10.1177/1558689809334210 DOI: https://doi.org/10.1177/1558689809334210
Sharashy, O. (2022). Plant biodiversity on coastal rocky ridges habitats with reference to census data in Ras El-Hekma and Omayed Area, Egypt. Journal of Pure & Applied Sciences, 21(1), 41–45. https://doi.org/10.51984/jopas.v21i1.1578 DOI: https://doi.org/10.51984/jopas.v21i1.1578
Stirling, G., & Wilsey, B. (2001). Empirical relationships between species richness, evenness, and proportional diversity. The American Naturalist, 158(3), 286–299. https://doi.org/10.1086/321317 DOI: https://doi.org/10.1086/321317
Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches (Vol. 46). Sage.
Yeboah, D., Chen, H.Y., & Kingston, S. (2016). Tree species richness decreases while species evenness increases with disturbance frequency in a natural boreal forest landscape. Ecology and Evolution, 6(3), 842–850. https://doi.org/10.1002/ece3.1944 DOI: https://doi.org/10.1002/ece3.1944
Zapata, S.M., & Onwuegbuzie, A.J. (2024). The impact of COVID-19 on Chilean University students: Obstacles that impacted their effective online learning. Current Psychology. https://doi.org/10.1007/s12144-024-06371-0 DOI: https://doi.org/10.1007/s12144-024-06371-0