Correlation between Mathematical Literacy and Students’ Understanding of Population Growth and Genetics Concepts among Secondary School Biology students in Delta State, Nigeria
DOI:
https://doi.org/10.64348/zije.2026308Abstract
This study explored the relationship between mathematical literacy and students’ understanding of population growth and genetics in biology. A correlational research design was adopted, involving 240 Senior Secondary II students selected using stratified random sampling. Data were collected using a Mathematical Literacy Test (MLT) and a Biology Quantitative Concepts Achievement Test (BQC-AT). Pearson Product-Moment Correlation and simple linear regression were used to analyze the data at a 0.05 significance level. Findings revealed a significant positive relationship between mathematical literacy and students’ understanding of population growth (r = 0.62, p < .001) and genetics (r = 0.58, p < .001). Regression analyses showed that mathematical literacy significantly predicted achievement in population growth (R² = 0.384) and genetics (R² = 0.336), indicating that students with higher mathematical literacy performed better in quantitative Biology tasks. The study concludes that mathematical literacy is a key determinant of conceptual understanding in biology. Based on the findings, it is recommended that biology and mathematics curricula be integrated to emphasize quantitative reasoning and that teachers receive professional development on incorporating mathematical concepts into biology instruction. Strengthening students' mathematical literacy will enhance their ability to interpret biological data, solve quantitative problems, and improve overall performance in biology.
References
American Biology Teacher Staff. (2025). Quantitative reasoning in the context of science phenomena. The American Biology Teacher, 87(6), 308–320. https://bioone.org/journals/the-american-biology-teacher/volume-87/issue-6/abt.2025.87.6.308 DOI: https://doi.org/10.1525/abt.2025.87.6.308
BioLibreTexts. (2023). Population and quantitative genetics: Applying probability and statistical reasoning. https://bio.libretexts.org
BioLibreTexts. (2024). Population ecology: Mathematical models and growth curves. https://bio.libretexts.org
BioSystems. (2024). A mathematical framework for the statistical interpretation of biological growth models. Bio Systems, 246, 105342. https://doi.org/10.1016/j.biosystems.2024.105342 DOI: https://doi.org/10.1016/j.biosystems.2024.105342
BioSystems. (2024). Advanced mathematical frameworks for interpreting population dynamics. Biosystems, 210, 104586. https://doi.org/10.1016/j.biosystems.2024.104586
Eurasia Journal of Mathematics, Science and Technology Education. (2025). Mathematical literacy and its influencing factors: A decade of research findings (2015–2024). 21(7), em2671. DOI: https://doi.org/10.29333/ejmste/16615
Fisher, R. A. (1918). The correlation between relatives on the supposition of Mendelian inheritance. Transactions of the Royal Society of Edinburgh, 52(2), 399–433. https://doi.org/10.1017/S0080456800012163 DOI: https://doi.org/10.1017/S0080456800012163
Fong, C. J., Snodgrass Rangel, V., & Others. (2021). Synergistic effects of students’ mathematics and science motivational beliefs on achievement. International Journal of STEM Education.
Fong, T., Lee, S., & Zhang, M. (2021). Early mathematical language and longitudinal STEM achievement: A review. International Journal of STEM Education, 8(12), 1–15. https://doi.org/10.1186/s40594-021-00275-2 DOI: https://doi.org/10.1186/s40594-021-00275-2
Hester, K., Leonard, M., & Fisher, P. (2014). Integrating mathematics into biology education: Improving student performance in quantitative biology. PLoS ONE, 9(7), e101204. https://pmc.ncbi.nlm.nih.gov/articles/PMC3940463
Hill, W. G. (2010). Understanding quantitative genetics (2nd ed.). Oxford University Press.
Hsu, J. L., Gartland, S., Prate, J., & Hohensee, C. (2025). Investigating student noticing of quantitative reasoning in introductory biology labs. CBE—Life Sciences Education, 24(1), ar14.
Hsu, S. (2025). Quantitative reasoning in biology laboratory contexts: Linking data interpretation to conceptual understanding. CBE—Life Sciences Education, 24(4), 124–138. https://www.lifescied.org/doi/10.1187/cbe.24-04-0124
Hsu, S., Johnson, R., & Kim, D. (2025). Student noticing of quantitative features in biology laboratory contexts: Implications for conceptual understanding. CBE—Life Sciences Education, 24(4), 124–138. https://www.lifescied.org/doi/10.1187/cbe.24-04-0124 DOI: https://doi.org/10.1187/cbe.24-04-0124
International Journal of STEM Education. (2022). Integrated instruction in mathematics and science: Cross-cutting skills for STEM learning. International Journal of STEM Education, 9(1), 1–18. https://doi.org/10.1186/s40594-022-00315-1
K., R., Smith, J., & Tan, P. (2025). Trends and challenges in mathematical literacy education: A global review (2015–2024). Eurasia Journal of Mathematics, Science and Technology Education, 21(1), 1–21. https://doi.org/10.29333/ejmste/12435
Kaur, T., McLoughlin, E., & Grimes, P. (2022). Mathematics and science across the transition from primary to secondary school: A systematic literature review. International Journal of STEM Education, 9, 13. DOI: https://doi.org/10.1186/s40594-022-00328-0
Kunwar, L. B. (2018). Mathematical modeling of population growth for single species. Academic Voices, 8(1), 55– DOI: https://doi.org/10.3126/av.v8i1.74048
Maerten-Rivera, J. M., et al. (2010). Reading and mathematics equally important to science achievement: Results from nationally-representative data. Learning and Individual Differences, 58, 1–9. DOI: https://doi.org/10.1016/j.lindif.2017.07.001
Maerten-Rivera, J., Myers, M., Lee, O., & Penfield, R. (2010). The relationship between mathematics and science achievement in elementary students: Evidence from TIMSS 2007. School Science and Mathematics, 110(3), 150–160. https://doi.org/10.1111/j.1949-8594.2010.00017.x DOI: https://doi.org/10.1111/j.1949-8594.2010.00017.x
Maghfiroh, N. (2025). Integrating quantitative literacy in genetics education: Implications for student reasoning and achievement. Education in Science Journal, 12(2), 45–59. https://files.eric.ed.gov/fulltext/EJ1477265.pdf
Mayes, R., Owens, D., Dauer, J., &Rittschof, K. (2022). A quantitative reasoning framework and the importance of quantitative modeling in biology. Applied and Computational Mathematics, 11(1), 1–17. https://doi.org/10.11648/j.acm.20221101.11 DOI: https://doi.org/10.11648/j.acm.20221101.11
Meta, A., & Amin, I. (2024). Quantitative literacy in ecological and population growth instruction: A systematic review. Meta: Journal of Science Education Research, 9(1), 55–77. https://meta.amiin.or.id/index.php/meta/article/download/142/55/688
National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.
OECD. (2019). PISA 2018 assessment and analytical framework: Mathematics, reading, science and financial literacy. OECD Publishing.
Piaget, J. (1970). Science of education and the psychology of the child. Orion Press.
Steen, L. A. (2001). Mathematics and democracy: The case for quantitative literacy. National Council on Education and the Disciplines.



