Technology and mathematical modelling: addressing challenges, opening doors
DOI:
https://doi.org/10.48489/quadrante.23710Keywords:
modelling, real-world, simulation, system dynamics, technologyAbstract
In terms of achieving educational goals, technology impacts on the nature of mathematical accomplishment with respect to both scope and purpose. We review the use of technology, actual and potential, within mathematical modelling viewed as real-world problem solving. We consider its role within the total modelling process, as well as its manner of use within individual problem contexts, illustrating ways in which inappropriate uses of technology create problems within modelling activity, as well as how discerning use can increase the power and accessibility of models to new audiences. We then demonstrate how technology provides access to models unavailable to those equipped only with hand methods of solution. Here non-linearity and simultaneity among model relationships means that model equations need to be first developed, parameterised, and then solved by simulation. Methods provided by System Dynamics are illustrated by considering the problem of providing potable water for a population expanding into a warmer environment, with limited water reserves.
References
Artigue, M. (2002). Learning mathematics in a CAS environment: The genesis of a refection about instrumentation and the dialectics between technical and conceptual work. International Journal of Computers for Mathematical Learning, 7(3), 245–274. https://doi.org/10.1023/A:1022103903080
Australian Curriculum Assessment and Reporting Authority. (2017). Curriculum. Retrieved from https://www.acara.edu.au/curriculum
Borba, M. C., & Villarreal, M. E. (2005). Humans-with-media and the reorganization of mathematical thinking. New York: Springer. https://doi.org/10.1007/b105001
Clark-Wilson, A., Robutti, O., & Thomas, M. (2020). Teaching with digital technology. ZDM Mathematics Education, 52, 1223–1242. https://doi.org/10.1007/s11858-020-01196-0
Damkjaer, S., & Taylor, R. (2017). The measurement of water scarcity: Defining a meaningful indicator. Ambio, 46(5), 513-531. https://doi.org/10.1007/s13280-017-0912-z
Doerr, H. & Zangor, R. (2000). Creating meaning for and with the graphing calculator. Educational Studies in Mathematics, 41, 143-163. https://doi.org/10.1023/A:1003905929557
Fisher, D. M. (2017). Modeling dynamic systems: Lessons for a first course (3rd ed.). Lebanon, New Hamp-shire: isee systems, inc.
Fisher, D. M. (2018). Reflections on teaching system dynamics modeling to secondary school students for over 20 years. Systems Journal Special Edition: Theory and Practice of System Dynamics Modelling, 6(12). https://doi.org/10.3390/systems6020012
Fisher, D. M. (2021) Global understanding of complex systems problems can start in pre-college education. In F. Leung, G. Stillman, G. Kaiser, & K. L. Wong (Eds.), Mathematical modelling education in East and West. International perspectives on the teaching and learning of mathematical modelling (pp. 35-44). Cham: Springer. https://doi.org/10.1007/978-3-030-66996-6_3
Forrester, J. W. (1969). Principles of systems. Cambridge, Massachusetts: Wright-Allen Press.
Galbraith, P. (2010). Senior mathematical modelling and applications. Melbourne: MacMillan Education.
Galbraith, P. (2020). Modelling around and about COVID-19. Australian Mathematics Education Journal, 2(2), 33-39.
Galbraith, P., & Fisher, D. M. (2021). System dynamics: Adding a string to the modelling bow In F. Leung, G. Stillman, G. Kaiser, & K. L. Wong (Eds.), Mathematical modelling education in East and West. Interna-tional perspectives on the teaching and learning of mathematical modelling (pp. 619-629). Cham: Springer. https://doi.org/10.1007/978-3-030-66996-6_52
Galbraith, P., & Holton, D. (2018). Mathematical modelling: A guidebook for teachers and teams. Melbourne: ACER. Retrieved from https://www.immchallenge.org.au/files/IM2C-Teacher-and-student-guide-to-mathematical-modelling.pdf
Galbraith, P., Stillman G., Brown J., & Redmond T. (2018). A modelling challenge: Students modelling problems of their choice. In S. Schukajlow & W. Blum (Eds.), Evaluierte lernumgebungen zum modellieren. Realitätsbezüge im mathematikunterricht (pp. 193-214). Springer Spektrum, Wiesbaden. https://doi.org/10.1007/978-3-658-20325-2_10
Garfunkel, S., Niss, M., & Brown, J. (2021). Opportunities for modelling: An extra-curricular challenge. In F. Leung, G. Stillman, G. Kaiser, & K. Wong (Eds.), Modelling education in East and West. International perspectives on the teaching and learning of mathematical modelling (pp. 362-375). Cham: Springer. https://doi.org/10.1007/978-3-030-66996-6_30
Geiger, V. (2005). Master, servant, partner, and extension-of-self: A finer grained view of this taxonomy. In P. Clarkson, A. Downton, D. Gronn, M. Horne, A. McDonough, R. Pierce, & A. Roche (Eds.), Proceedings of the 28th annual conference of the Mathematics Education Research Group of Australasia: Building connections, theory, research and practice (pp. 369-376). MERGA.
Geiger, V., Faragher, R., & Goos, M. (2010). Cas-enabled technologies as ‘agents provocateurs’ in teaching and learning mathematical modelling in secondary school classrooms. Mathematics Education Research Journal, 22, 48–68. https://doi.org/10.1007/BF03217565GGRG
Geiger, V., Galbraith, P., Niss, M., & Delzoppo, C. (2021). Developing a task design and implementation framework for fostering mathematical modelling competencies. Educational Studies in Mathematics. https://doi.org/10.1007/s10649-021-10039-y
Goos, M., Galbraith, P., Renshaw, P., & Geiger, V. (2003). Perspectives on technology mediated learning in secondary school mathematics classrooms. Journal of Mathematical Behavior, 22(1), 73-89. https://doi.org/10.1016/S0732-3123(03)00005-1
Greefrath, G., Siller, H. S., & Weitendorf, J. (2011). Modelling considering the influence of technology. In G. Kaiser, W. Blum, R. Borromeo Ferri, & G. Stillman (Eds.) Trends in teaching and learning of mathematical modelling (pp. 315–329). Dordrecht: Springer. https://doi.org/10.1007/978-94-007-0910-2_32
Guin, D., & Trouche, L. (1999). The complex process of converting tools into mathematical instruments: The case of calculators. The International Journal of Computers for Mathematical Learning, 3(3), 195–227. https://doi.org/10.1023/A:1009892720043
Jankvist, U.T., Misfeldt, M., & Aguilar, M.S. (2019). What happens when CAS procedures are objectified? – The case of “solve” and “desolve”. Educational Studies in Mathematics 101, 67–81. https://doi.org/10.1007/s10649-019-09888-5
Julie, C. (2002). Making relevance relevant in mathematics teacher education. Proceedings of the second International Conference on the Teaching of Mathematics at the Undergraduate Level [CD]. Hoboken, NJ: Wiley.
Kaiser, G., & Sriraman, B. (2006). A global survey of international perspectives on modelling in mathematics education. ZDM, 38(3), 302–310. https://doi.org /10.1007/BF02652813
Leduc, M., Matthews, H. D., & de Elia, R. (2015). Quantifying the limits of a linear temperature response to cumulative CO2 emissions. Journal of Climate, 28, 9955–9968. https://doi.org/10.1175/JCLI-D-14-00500.1
Meadows, D. H., Meadows, D. L., Randers, J., & Behrens, W. W. (1972). The limits to growth. London: Pan Books Ltd.
Meadows, D. H., Meadows, D. L., & Randers, J. (1992). Beyond the limits: Confronting global collapse, envisaging a sustainable future. Vermont: Chelsea Green Publishing.
Molina-Toro, J. F., Rendon-Mesa, P. A., & Villa-Ochoa, T. (2019). Research trends in digital technologies and modeling in mathematics education. EURASIA Journal of Mathematics, Science and Technology Education, 15(8), 1-13. https://doi.org/10.29333/ejmste/108438
National Governors Association Center for Best Practices & Council of Chief State School Officers. (2010). Common Core State Standards for Mathematics. Washington, DC: Authors.
Niss, M. (2010). Modeling a crucial aspect of students’ mathematical modeling. In R. Lesh, P. L. Galbraith, C. Haines, & A. Hurford. (Eds.), Modeling students’ mathematical competencies. ICMTA 13 (pp. 43-59). Boston, Massachusetts: Springer.
Niss, M., & Blum, W. (2020). The learning and teaching of mathematical modelling. Oxford: Routledge.
OECD (2021). Description of mathematical literacy for 2021. Retrieved from https://pisa2021-maths.oecd.org/
Stillman, G. (2011). Applying metacognitive knowledge and strategies in applications and modelling tasks at secondary school. In G. Kaiser, W. Blum, R. Borromeo Ferri, & G. Stillman (Eds.), Trends in teaching and learning of mathematical modelling (pp. 165 - 180). Springer Dordrecht. https://doi.org/10.1007/978-94-007-0910-2_18
Stillman, G., Galbraith, P., Brown, J., & Edwards, I. (2007). A framework for success in implementing mathematical modelling in the secondary classroom. In J. Watson & K. Beswick (Eds.), Mathematics: essential research, essential practice. Proceedings of the 30th annual conference of the Mathematics Research Group of Australasia (Vol. 2, pp. 688-707). Adelaide: MERGA2007.
Treilibs, V., Burkhardt, H., & Low, B. (1980). Formulation processes in mathematical modelling, Nottingham: Shell Centre Publications.
Villarreal, M. E., Esteley, C. B., & Smith, S. (2018). Pre-service teachers’ experiences within modelling scenarios enriched by digital technologies. ZDM - Mathematics Education, 50(1-2), 327-341. https://doi.org/10.1007/s11858-018-0925-5
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