“The most vitally characteristic fact about mathematics is, in my opinion, its quite peculiar relationship to the natural sciences, or, more generally, to any science which interprets experience on a higher than purely descriptive level.”.
(von Neumann, 1947).
Abstract
In this article, an analysis of six fisheries biology assignments involving mathematical models is conducted, focusing on the type of participation that they offer. The purpose of the analysis is to identify the characteristics of the mathematical discourse of the assignments and to expose their relationship with the biological context with the purpose of understanding the role of mathematics discourse in other academic discourses. The theoretical orientation of the study is informed by commognitive theory which views learning of an academic discourse as the process of becoming a participant in that discourse. Data analysis revealed various aspects of mathematics wordings, tools and routines essential in the expected solution trajectories of the assignments and two types of required transitions, intrinsic and extrinsic, between mathematics and biology narratives. The article offers analytical and theoretical explanations for why this is the case as well as directions for future research.
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Notes
A program package developed for conducting various analyses using fisheries data, see: http://www.fao.org/fishery/topic/16072/en
Similar to FiSAT, see: https://www.hi.no/hi/forskning/forskningsgrupper/havforskning-i-utviklingsland
The authors acknowledge not being specialists in fisheries biology and apologize in advance for any misinterpretation or naïve descriptions of the assignments.
The term ‘assignment’ refers to each file-document provided by the professor to the students (home-assignment means students work on it outside classroom, after lecture sessions). An assignment may include several questions and a question may contain more than one task situation, in the sense of Lavie et al. (2018).
References
Allen, R. L. (1975). Models for fish populations: a review. NZ Operational Research, 4(1), 1–20.
Andersen, J. (2007). Enriching the teaching of biology with mathematical concepts. American Biology Teacher, 69(4), 205–209.
Bakker, A., & Akkerman, S. F. (2014). A boundary-crossing approach to support students’ integration of statistical and work-related knowledge. Educational Studies in Mathematics, 86, 223–237.
Blum, W., Galbraith, P. L., Henn, H.-W., & Niss, M. (2007). Modelling and applications in mathematics education; the 14th ICMI study (Vol. 10). Springer.
Boix-Mansilla, V. (2010). Learning to synthesize: the development of interdisciplinary understanding. In R. Frodeman, J. T. Klein, & C. Mitcham (Eds.), The Oxford Handbook of Interdisciplinarity (pp. 288–306). Oxford University Press.
Borromeo Ferri, R., & Lesh, R. (2013). Should intepretation systems be considered to be models if they only function implicitely? In G. Stillman, G. Kaiser, W. Blum, & J. Brown (Eds.), Teaching Mathematical Modelling: Connecting to Research and Practice, International Perspectives on the Teaching and Learning of Mathematical Modelling (pp. 57–66). Springer.
Dierdorp, A., Bakker, A., Eijkelhof, H., & van Maanen, J. (2011). Authentic practices as contexts for learning to draw inferences beyond correlated data. Mathematical Thinking and Learning, 13, 132–151.
Duran, P. A., & Marshall, J. A. (2019). Mathematics for biological sciences undergraduates: a needs assessment. International Journal of Mathematical Education in Science and Technology, 50(6), 807–824.
Greenfrath, G. (2011). Using technologies: new possibilities of teaching and learning modelling - overview. In G. Kaiser, W. Blum, R. Borremeo Ferri, & G. Stillman, Trends in Teaching and Learning of Mathematical Modelling (pp. 301–304). Dordrecht: Springer Netherlands.
Hunt, J. (2007). Communicating big themes in applied mathematics. In C. Haines, P. Galbraith, W. Blum, & S. Khan (Eds.), Mathematical modelling (ICTMA 12): Education, engineering and economics: Proceedings from the Twelfth International Conference on the Teaching of Mathematical Modelling and Applications (pp. 2–24). Horwood.
Jungck, J. (2005). Challenges, connections, complexities: Educating for collaboration. In L. A. Steen (Ed.), Math & Bio 2010: Linking Undergraduate Disciplines (pp. 1–12). The Mathematical Association of America.
Jungck, J. R. (2011). Mathematical biology education: Modeling making meaning. Mathematical Modelling of Natural Phenomena, 6, 1–21.
Jungck, J. R., Robeva, R., & Gross, L. J. (2020). Mathematical biology education: Changes, communities, connections, and challenges. Bulletin of Mathematical Biology, 82(117). https://doi.org/10.1007/s11538-020-00793-0
Kent, P., & Noss, R. (2001). Finding a role for technology in service mathematics for engineers and scientists. In D. Holton (Ed.), The Teaching and Learning of Mathematics at University Level: An ICMI Study (pp. 395–404). Kluwer Academic Publishers.
Lavie, I., Steiner, A., & Sfard, A. (2018). Routines we live by: From ritual to exploration. Educational Studies in Mathematics. https://doi.org/10.1007/s10649-018-9817-4
Lesh, R., & Lehrer, R. (2003). Models and modeling perspectives on the development of students and teachers. Mathematical Thinking and Learning, 5(2–3), 109–129.
Lloyd, E. (1988). A defense of a model-based account of theories in the context of evolutionary theory and population genetics. Princeton University Press.
Morgan, C., & Sfard, A. (2016). Investigating changes in high-stake mathematics examinations: a discursive approach. Research in Mathematics Education, 18(2), 92–119.
Morgan, M. S., & Morrison, M. (1999). Models as mediators: Perspectives on natural and social science. Cambridge University Press.
Nardi, E., Ryve, A., Stadler, E., & Viirman, O. (2014). Commognitive analyses of the learning and teaching of mathematics at university level: the case of discursive shifts in the study of Calculus. Research in Mathematics Education, 16(2), 182–198.
Niss, M. (2010). Modelling a crucial aspect of students’ mathematical modelling. In R. Lesh, P. L. Galbraith, C. R. Haines, & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies (pp. 43–59). Springer.
Niss, M., Blum, W., & Galbraith, P. (2007). Introduction. In W. Blum, P. L. Galbraith, H.-W. Henn, & M. Niss (Eds.), Modelling and applications in mathematics educations: The 14th ICMI study (pp. 3–32). Springer.
Odenbaugh, J. (2005). Idealized, inaccurate but successful: a pragmatic approach to evaluating models in theoretical ecology. Biology and Philosophy, 20, 231–255.
Odenbaugh, J. (2009). Models in biology. In E. Craig, Routledge encyclopedia of philosophy. London: Routledge.
Passmore, C., Gouvea, J. S., & Giere, R. (2014). Models in science and in learning science: Focusing scientific practice on sense-making. In M. Mathews (Ed.), International Handbook of Research in History, Philosophy and Science Teaching (pp. 1171–1202). Springer Science+Business Media.
Scheaffer, R. L. (2011). Statistic education. In M. Lovric (Ed.), International Encyclopedia of Statistical Science (pp. 1482–1484). Springer.
Sfard, A. (2008). Thinking as communicating, human development, the growth of discouses. Cambrige University Press.
Smith, C., & Morgan, C. (2016). Curricular orientations to real-world contexts in mathematics. The Curriculum, 27(1), 24–45.
Smith, E., Haarer, S., & Confrey, J. (1997). Seeking diversity in mathematics education: Mathematical modeling in the practice of biologists and mathematicians. Science & Education, 6(5), 441–472.
Sparre, P., & Venema, S. C. (1998). Introduction to tropical fish stock assessment, Part I: Manual. (Vol. 306). FAO Fisheries Technical Paper.
Starfield, A. M., Smith, K. A., & Bleloch, A. L. (1994). How to model it: Problem solving for the computer age. Edina, MN: Burgess International Group.
Steen, L. A. (2005). Math and bio 2010: Linking undergradute disciplines. Mathematical Association of America.
Stillman, G. (2000). Impact of prior knowledge of task context on approaches to applications tasks. Journal of Mathematical Behaviour, 19, 333–361.
Stillman, G. (2019). State of the Art on Modelling in Mathematics Education-Lines of Inquiry. In G. Stillman , & J. P. Brown, Lines of Inquiry in Mathematical Modelling Research in Education (pp. 1-20). ICME-13 Monographs.
Tetaj, F. (2021). An analytical scheme to characterise the mathematical discourse of biology tasks. In F. Leung, G. A. Stillman, G. Kaiser, & K. L. Wong, Mathematical Modelling Education in East and West; ICTMA proceedings (pp. 641–650). Cham, Switzerland: Springer Nature.
Viirman, O., & Nardi, E. (2019). Negotiating different disciplinary discourses: Biology students’ ritualized and exploratory participation in mathematical modeling activities. Educational Studies in Mathematics, 101, 233–252.
von Neumann, J. (1947). The Mathematician. In R. B. Heywood, Works of the Mind (Vol. 1, pp. 180–196). Chicago: University of Chicago Press.
Wake, G. (2014). Making sense of and with mahtematics: the interface between academic mathematics and mathematics in practice. Educational Studies in Mathematics, 86, 271–290.
Williams, J., & Wake, G. (2007). Black boxes in workplace mathematics. Educational Studies in Mathematics, 64, 317–343.
Williams, J., Roth, W.-M., Swanson, D., Doig, B., Groves, S., Omuvwie, M., . . . Mousoulides, N. (2016). Interdisciplinary Mathematics Education: A state of the Art. Springer International Publishing.
Yin, R. K. (2018). Case study research and applications, design and methods (6th ed.). SAGE Publications.
Acknowledgements
We acknowledge the support from MatRIC, Centre for research, innovation, and coordination of mathematics teaching, and bioCEED, Centre for excellence in biology education. We wish to thank the biology professor of the EFAM course for his kind cooperation in this research and the anonymous reviewers for their helpful comments.
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Floridona Tetaj was the main contributor to all aspects of the paper and wrote the original draft. Olov Viirman contributed to theoretical framework, data analysis and discussion, and to preparing the final draft.
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Tetaj, F., Viirman, O. Analysing the Mathematical Discourse of Biology Assignments: The Case of a Graduate Fisheries Management Course. Int. J. Res. Undergrad. Math. Ed. 9, 375–397 (2023). https://doi.org/10.1007/s40753-022-00205-9
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DOI: https://doi.org/10.1007/s40753-022-00205-9