Mathematical Modelling in Life Sciences Education: A Structured Systematic Review of Its Role in Developing Problem-Solving, Data Literacy and Scientific Reasoning among Secondary School Learners
DOI:
https://doi.org/10.57125/FED.2026.09.17Keywords:
secondary education; interdisciplinary STEM; quantitative reasoning; model-based inquiry; systems thinking; graph interpretation; evidence-based reasoning; biology pedagogyAbstract
Mathematical modelling is increasingly important in Life Sciences because biological explanation depends on quantitative data, graphical representation, simulation, prediction and systems analysis. Yet secondary-school mathematics and Life Sciences are commonly taught separately, leaving evidence on modelling dispersed across mathematics education, biology education, data literacy and interdisciplinary STEM research. This structured systematic review synthesised literature published from 2017–2026 on how mathematical, computational and model-based approaches support secondary learners’ problem-solving, data literacy and scientific reasoning in Life Sciences and closely related STEM contexts. PRISMA 2020 guided reporting. Searches used ERIC, PubMed/PMC, major publisher platforms, institutional research portals, general scholarly-web discovery and backward citation chaining; Scopus and Web of Science were not directly queried. We captured 95 record appearances, removed 22 duplicates, screened 73 unique records, assessed 60 reports in detail, and included 55 substantive sources. We appraised empirical studies using MMAT-consistent domains and assessed reviews for transparency in search, selection, and synthesis. The evidence indicates that modelling is most productive when learners frame biologically meaningful problems, identify variables and assumptions, construct representations, analyse authentic data, interpret outputs, test explanations against evidence and revise models. Benefits recur for systems thinking, graph interpretation, causal reasoning and transfer, whereas interpretation and validation remain persistent weaknesses. The review proposes a BioMath Modelling–Reasoning Cycle and argues that mathematics should function as an explanatory language for the Life Sciences rather than as a peripheral calculation tool. It identifies implications for curriculum, teacher education, assessment and resource-constrained contexts.
References
Angra, A., & Gardner, S. M. (2017). Reflecting on graphs: Attributes of graph choice and construction practices in biology. CBE—Life Sciences Education, 16(3), Article ar53. https://doi.org/10.1187/cbe.16-08-0245
Armutcu, Y., & Bal, A. P. (2023). The effect of mathematical modelling activities on students' mathematical modelling skills within STEM education. International Journal of Contemporary Educational Research, 10(1), 42–55. https://doi.org/10.33200/ijcer.1131928
Bai, Y., Campbell, T., & Gray, R. E. (2026). Model-based inquiry: Designing for opportunities to learn. School Science and Mathematics, 1–15. https://doi.org/10.1111/ssm.70028
Beswick, K., & Fraser, S. (2019). Developing mathematics teachers' 21st-century competence for teaching in STEM contexts. ZDM Mathematics Education, 51(6), 955–965. https://doi.org/10.1007/s11858-019-01084-2
Binali, T., Chang, C.-H., Chang, Y.-J., & Chang, H.-Y. (2024). High school and college students' graph-interpretation competence in scientific and everyday data-visualisation contexts. Science & Education, 33(3), 763–785. https://doi.org/10.1007/s11191-022-00406-3
Brown, R. E., & Bogiages, C. A. (2019). Professional development through STEM integration: How early career math and science teachers respond to experiencing integrated STEM tasks. International Journal of Science and Mathematics Education, 17(1), 111–128. https://doi.org/10.1007/s10763-017-9863-x
Bulut, N., & Borromeo Ferri, R. (2026). AI-powered learning: Exploring secondary school students' use of ChatGPT for solving mathematical modelling problems. ZDM Mathematics Education. Advance online publication. https://doi.org/10.1007/s11858-026-01803-6
Burgin, S. R., Oramous, J., Kaminski, M., Stocker, L., & Moradi, M. (2018). High school biology students use of visual molecular dynamics as an authentic tool for learning about modeling as a professional scientific practice. Biochemistry and Molecular Biology Education, 46(3), 230–236. https://doi.org/10.1002/bmb.21113
Buthelezi, P. Z. G., & Mthembu, M. I. (2025). Integrating mathematical modelling of disease transmission into Life Sciences classrooms: A pedagogical approach. Journal of Cultural Analysis and Social Change, 10(2). https://doi.org/10.64753/jcasc.v10i2.2303
Campillay-Llanos, W., & Cárcamo-Mansilla, N. (2025). How do we model what we model? An interdisciplinary approach for educational integration between mathematics and biology. Discover Education, 4, Article 145. https://doi.org/10.1007/s44217-025-00584-6
Chen, C.-S., & Lin, J.-W. (2019). A practical action research study of the impact of maker-centred STEM-PjBL on a rural middle school in Taiwan. International Journal of Science and Mathematics Education, 17(Suppl. 1), 85–108. https://doi.org/10.1007/s10763-019-09961-8
Christensen, D., & Lombardi, D. (2023). Biological evolution learning and computational thinking: Enhancing understanding through integration of disciplinary core knowledge and scientific practice. International Journal of Science Education, 45(4), 293–313. https://doi.org/10.1080/09500693.2022.2160221
Dauer, J. T., Bergan-Roller, H. E., King, G. P., Kjose, M., Galt, N. J., & Helikar, T. (2019). Changes in students' mental models from computational modeling of gene regulatory networks. International Journal of STEM Education, 6, Article 38. https://doi.org/10.1186/s40594-019-0193-0
Dorfner, T., Förtsch, C., Germ, M., & Neuhaus, B. J. (2018). Biology instruction using a generic framework of scientific reasoning and argumentation. Teaching and Teacher Education, 75, 232–243. https://doi.org/10.1016/j.tate.2018.07.003
Dvir, M., & Ben-Zvi, D. (2022). Students' actual purposes when engaging with a computerized simulation in the context of citizen science. British Journal of Educational Technology, 53, 1202–1220. https://doi.org/10.1111/bjet.13238
Farrell, K. J., & Carey, C. C. (2018). Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses. Ecology and Evolution, 8(16), 7744–7751. https://doi.org/10.1002/ece3.4363
Friedrich, A., Schreiter, S., Vogel, M., Becker-Genschow, S., Brünken, R., Kuhn, J., Lehmann, J., & Malone, S. (2024). What shapes statistical and data literacy research in K-12 STEM education? A systematic review of metrics and instructional strategies. International Journal of STEM Education, 11, Article 58. https://doi.org/10.1186/s40594-024-00517-z
Frischemeier, D. (2021). A first introduction to data science education in secondary schools: Teaching and learning about data exploration with CODAP using survey data. Teaching Statistics. Advance online publication. https://doi.org/10.1111/test.12283
Fuhrmann, T., Schneider, B., & Blikstein, P. (2018). Should students design or interact with models? Using the Bifocal Modelling Framework to investigate model construction in high school science. International Journal of Science Education, 40(8), 867–893. https://doi.org/10.1080/09500693.2018.1453175
Gao, X., Li, P., Shen, J., & Sun, H. (2020). Reviewing assessment of student learning in interdisciplinary STEM education. International Journal of STEM Education, 7, Article 24. https://doi.org/10.1186/s40594-020-00225-4
Gebre, E. (2018). Young adults' understanding and use of data: Insights for fostering secondary school students' data literacy. Canadian Journal of Science, Mathematics and Technology Education, 18(4), 330–341. https://doi.org/10.1007/s42330-018-0034-z
Gilissen, M. G. R., Knippels, M.-C. P. J., & van Joolingen, W. R. (2020). Bringing systems thinking into the classroom. International Journal of Science Education, 42(8), 1253–1280. https://doi.org/10.1080/09500693.2020.1755741
Gilissen, M. G. R., Knippels, M.-C. P. J., & van Joolingen, W. R. (2021). Fostering students' understanding of complex biological systems. CBE—Life Sciences Education, 20(3), Article ar37. https://doi.org/10.1187/cbe.20-05-0088
Goos, M., Carreira, S., & Namukasa, I. K. (2023). Mathematics and interdisciplinary STEM education: Recent developments and future directions. ZDM Mathematics Education, 55, 1199–1217. https://doi.org/10.1007/s11858-023-01533-z
Hong, Q. N., Pluye, P., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., O'Cathain, A., Rousseau, M.-C., & Vedel, I. (2018). Mixed Methods Appraisal Tool (MMAT), version 2018: User guide. McGill University.
Hounshell, A. G., Farrell, K. J., & Carey, C. C. (2021). Macrosystems EDDIE teaching modules increase students' ability to define, interpret, and apply concepts in macrosystems ecology. Education Sciences, 11(8), Article 382. https://doi.org/10.3390/educsci11080382
Hurt, T., Greenwald, E., Allan, S., Cannady, M. A., Krakowski, A., Brodsky, L., Collins, M. A., Montgomery, R., & Dorph, R. (2023). The computational thinking for science (CT-S) framework: Operationalizing CT-S for K–12 science education researchers and educators. International Journal of STEM Education, 10, Article 1. https://doi.org/10.1186/s40594-022-00391-7
Jiang, S., Nocera, A., Tatar, C., Yoder, M. M., Chao, J., Wiedemann, K., Finzer, W., & Rosé, C. P. (2022). An empirical analysis of high school students' practices of modelling with unstructured data. British Journal of Educational Technology, 53(5), 1114–1133. https://doi.org/10.1111/bjet.13253
Julie, C. (2020). Modelling competencies of school learners in the beginning and final year of secondary school mathematics. International Journal of Mathematical Education in Science and Technology, 51(8), 1181–1195. https://doi.org/10.1080/0020739X.2020.1725165
Just, J., & Siller, H.-S. (2022). The role of mathematics in STEM secondary classrooms: A systematic literature review. Education Sciences, 12(9), Article 629. https://doi.org/10.3390/educsci12090629
Kang, J., Na, C., Diederich, M., Swanson, H., & Yan, L. (2026). Cultivating science data literacy in K-16 science education through data practices: A systematic review. Instructional Science, 54, Article 50. https://doi.org/10.1007/s11251-026-09803-5
Kervinen, A., & Aivelo, T. (2023). Secondary school students' responses to epistemic uncertainty during an ecological citizen science inquiry. Science Education, 107(5), 1352–1379. https://doi.org/10.1002/sce.21809
Kjelvik, M. K., & Schultheis, E. H. (2019). Getting messy with authentic data: Exploring the potential of using data from scientific research to support student data literacy. CBE—Life Sciences Education, 18(2), Article es2. https://doi.org/10.1187/cbe.18-02-0023
Kohen, Z., & Orenstein, D. (2021). Mathematical modeling of tech-related real-world problems for secondary school-level mathematics. Educational Studies in Mathematics, 107, 71–91. https://doi.org/10.1007/s10649-020-10020-1
Lankers, A., Timm, J., & Schmiemann, P. (2023). Students' systems thinking while modeling a dynamic ecological system. Frontiers in Education, 8, Article 1187237. https://doi.org/10.3389/feduc.2023.1187237
Luo, M., Sun, D., Zhu, G., Zhu, L., & Jia, F. (2025). Factors influencing scientific reasoning ability in junior secondary students: Examining gender and grade-level predictive differences. Thinking Skills and Creativity, 57, Article 101824. https://doi.org/10.1016/j.tsc.2025.101824
Manunure, K., Zezekwa, N., Sunzuma, G., & Mutseekwa, C. (2026). Integrating science and mathematics at secondary school level: Key elements of practice. STEM Education, 6(1), 35–55. https://doi.org/10.3934/steme.2026003
Miller, E., Manz, E., Russ, R., & Stroupe, D. (2018). Addressing the epistemic elephant in the room: Epistemic agency and the Next Generation Science Standards. Journal of Research in Science Teaching, 55(7), 1053–1075. https://doi.org/10.1002/tea.21459
Moore-Anderson, C. (2021). Designing a curriculum for the networked knowledge facet of systems thinking in secondary biology courses: A pragmatic framework. Journal of Biological Education, 57(2), 370–385. https://doi.org/10.1080/00219266.2021.1909641
Musaeus, L. H., Tatar, D. G., & Musaeus, P. (2024). Computational modelling in high school biology: A teaching intervention. Journal of Biological Education, 58(4), 812–828. https://doi.org/10.1080/00219266.2022.2118353
O'Reilly, C. M., Gougis, R. D., Klug, J. L., Carey, C. C., Richardson, D. C., Bader, N. E., Soule, D. C., Castendyk, D., Meixner, T., Stomberg, J., Weathers, K. C., & Hunter, W. (2017). Using large data sets for open-ended inquiry in undergraduate science classrooms. BioScience, 67(12), 1052–1061. https://doi.org/10.1093/biosci/bix118
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., . . . Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Quigley, D., McNamara, C., Ostwald, J., & Sumner, T. (2017). Using learning analytics to understand scientific modeling in the classroom. Frontiers in ICT, 4, Article 24. https://doi.org/10.3389/fict.2017.00024
Sari, I. R., Ramli, M., & Harlita. (2026). Data literacy in STEM-based biology education: A systematic review of trends and empirical studies. Florea: Jurnal Biologi dan Pembelajarannya, 13(2), 81–92. https://doi.org/10.25273/florea.v13i2.24913
Schreiter, S., Friedrich, A., Fuhr, H., Malone, S., Brünken, R., Kuhn, J., & Vogel, M. (2024). Teaching for statistical and data literacy in K-12 STEM education: A systematic review on teacher variables, teacher education, and impacts on classroom practice. ZDM Mathematics Education, 56, 31–45. https://doi.org/10.1007/s11858-023-01531-1
Stöger, B., & Nerdel, C. (2025). How to measure mathematical modelling in science education? An atomistic approach. Progress in Science Education, 8(2), 51–69. https://doi.org/10.25321/prise.2025.1529
Sun, X., Ren, W., Liu, L., Xu, J., Gao, M., & Li, M. (2026). The impact of computational modeling on students' systems thinking in science education: A meta-analysis in K-16. Journal of Research in Science Teaching, 63(2), 107–123. https://doi.org/10.1002/tea.70028
Thobejane, M. I., Mtshali, M. S., & Masha, M. F. (2026). Unlocking scientific literacy: The role of e-modules and learning applications in South African Grade 11 Life Sciences classrooms. Education Sciences, 16(3), Article 395. https://doi.org/10.3390/educsci16030395
Tripto, J., Assaraf, O. B.-Z., Snapir, Z., & Amit, M. (2017). How is the body's systemic nature manifested amongst high school biology students? Instructional Science, 45(1), 73–98. https://doi.org/10.1007/s11251-016-9390-0
Tullis, J. G., & Goldstone, R. L. (2017). Instruction in computer modelling can support the broad application of complex systems knowledge. Frontiers in Education, 2, Article 4. https://doi.org/10.3389/feduc.2017.00004
Udosen, A. N., & Magana, A. J. (2026). Model-based reasoning in STEM education: A systematic literature review. International Journal of STEM Education, 13, Article 30. https://doi.org/10.1186/s40594-026-00621-2
Upmeier zu Belzen, A., Engelschalt, P., & Krüger, D. (2021). Modeling as scientific reasoning—The role of abductive reasoning for modeling competence. Education Sciences, 11(9), Article 495. https://doi.org/10.3390/educsci11090495
Van Vo, D., & Csapó, B. (2023). Exploring inductive reasoning, scientific reasoning and science motivation, and their role in predicting STEM achievement across grade levels. International Journal of Science and Mathematics Education, 21, 2375–2398. https://doi.org/10.1007/s10763-022-10349-4
Verhoeff, R. P., Knippels, M.-C. P. J., Gilissen, M. G. R., & Boersma, K. T. (2018). The theoretical nature of systems thinking: Perspectives on systems thinking in biology education. Frontiers in Education, 3, Article 40. https://doi.org/10.3389/feduc.2018.00040
Xiang, L., Goodpaster, S., & Mitchell, A. (2022). Supporting three-dimensional learning on ecosystems using an agent-based computer model. Journal of Science Education and Technology, 31(4), 473–489. https://doi.org/10.1007/s10956-022-09968-x
Zangori, L., Peel, A., Kinslow, A., Friedrichsen, P., & Sadler, T. D. (2017). Student development of model-based reasoning about carbon cycling and climate change in a socio-scientific issues unit. Journal of Research in Science Teaching, 54(10), 1249–1273. https://doi.org/10.1002/tea.21404
Zhao, F., & Schuchardt, A. (2021). Development of the Sci-math Sensemaking Framework: Categorising sensemaking of mathematical equations in science. International Journal of STEM Education, 8, Article 10. https://doi.org/10.1186/s40594-020-00264-x
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
