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Constructing a system of covariational relationships: two contrasting cases

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Abstract

Although there is much research exploring students’ covariational reasoning, there is less research exploring the ways students can leverage such reasoning to coordinate more than two quantities. In this paper, we describe a system of covariational relationships as a comprehensive image of how two varying quantities, having the same attribute across different objects, each covary with respect to a third quantity and in relation to each other. We first describe relevant theoretical constructs, including reasoning covariationally to construct relationships between quantities and reasoning covariationally to compare quantities. We then present a conceptual analysis entailing three interrelated activities we conjectured could support students in reasoning covariationally to conceive of a system of covariational relationships and represent the system graphically. We provide results from two teaching experiments with four middle school students engaging in tasks designed with our conceptual analysis in mind. We highlight two different ways students reasoned covariationally compatible with our conceptual analysis. We discuss the implications of our results and provide areas for future research.

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Notes

  1. We note that this construct is distinct from but related to a system of equations. We discuss some similarities and differences in greater detail in Sect. 4.

  2. All demographic information is from parental surveys.

References

  • Bergeron, L. (2015). IB Mathematics compatibility study: Curriculum and assessment comparison. International Baccalaureate Organization. Retrieved January 10, 2021, from https://www.ibo.org/globalassets/publications/ib-research/dp/maths-comparison-summary-report.pdf

  • Carlson, M. P., Jacobs, S., Coe, E., Larsen, S., & Hsu, E. (2002). Applying covariational reasoning while modeling dynamic events: A framework and a study. Journal for Research in Mathematics Education, 33(5), 352–378. https://doi.org/10.2307/4149958

    Article  Google Scholar 

  • Castillo-Garsow, C., Johnson, H. L., & Moore, K. C. (2013). Chunky and smooth images of change. For the Learning of Mathematics, 33(3), 31–37.

    Google Scholar 

  • Clement, J. (2000). Analysis of clinical interviews: Foundations and model viability. In A. E. Kelly & R. A. Lesh (Eds.), Handbook of research design in mathematics and science education (pp. 547–589). Lawrence Erlbaum Associates Inc.

    Google Scholar 

  • Dougherty, B. (2008). Measure up: A quantitative view of early algebra. In J. J. Kaput, D. W. Carraher, & M. L. Blanton (Eds.), Algebra in the early grades. Routledge.

    Google Scholar 

  • Ellis, A. B., Ozgur, Z., Kulow, T., Williams, C., & Amidon, J. (2012). Quantifying exponential growth: The case of the jactus. In R. Mayes & L. L. Hatfield (Eds.), Quantitative reasoning and mathematical modeling: A driver for STEM integrated education and teaching in context (pp. 93–112). University of Wyoming.

    Google Scholar 

  • Häggström, J. (2008). Teaching systems of linear equations in Sweden and China: What is made possible to learn? [Doctoral thesis]. University of Gothenburg.

    Google Scholar 

  • Johnson, H. L. (2012). Reasoning about variation in the intensity of change in covarying quantities involved in rate of change. The Journal of Mathematical Behavior, 31(3), 313–330. https://doi.org/10.1016/j.jmathb.2012.01.001

    Article  Google Scholar 

  • Johnson, H. L., McClintock, E. D., & Gardner, A. (2020). Opportunities for reasoning: Digital task design to promote students’ conceptions of graphs as representing relationships between quantities. Digital Experiences in Mathematics Education, 6, 340–366. https://doi.org/10.1007/s40751-020-00061-9

    Article  Google Scholar 

  • Jones, S. R., & Jeppson, H. P. (2020). Students’ reasoning about multivariational structures. In A. I. Sacristan, J. C. Cortes-Zavala, & P. M. Ruiz-Arias (Eds.), Proceedings of the 42nd annual conference of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1139–1147). PME-NA.

  • Lee, H. Y., & Hardison, H. L. (2017). Motivating the Cartesian plane: Using one point to represent two points. In E. Galindo & J. Newton, (Eds.), Proceedings of the 39th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 379–382). Hoosier Association of Mathematics Teacher Educators.

  • Lobato, J., & Thanheiser, E. (2002). Developing understanding of ratio and measure as a foundation for slope. In B. Litwiller & G. Bright (Eds.), Making sense of fractions, ratios, and proportionals: 2002 yearbook (pp. 162–175). National Council of Teachers of Mathematics.

    Google Scholar 

  • Moore, K. C. (2014). Quantitative reasoning and the sine function: The case of Zac. Journal for Research in Mathematics Education, 45(1), 102–138. https://doi.org/10.5951/jresematheduc.45.1.0102

    Article  Google Scholar 

  • Moore, K. C., & Carlson, M. P. (2012). Students’ images of problem contexts when solving applied problems. Journal of Mathematical Behavior, 31(1), 48–59. https://doi.org/10.1016/j.jmathb.2011.09.001

    Article  Google Scholar 

  • Moore, K. C., Stevens, I. E., Paoletti, T., Hobson, N. L., & Liang, B. (2019). Pre-service teachers’ figurative and operative graphing actions. The Journal of Mathematical Behavior, 56, 1–18. https://doi.org/10.1016/j.jmathb.2019.01.008

  • Olive, J., & Çağlayan, G. (2008). Learners’ difficulties with quantitative units in algebraic word problems and the teacher’s interpretation of those difficulties. International Journal of Science and Mathematics Education, 6(2), 269–292. https://doi.org/10.1007/s10763-007-9107-6

    Article  Google Scholar 

  • Paoletti, T., & Vishnubhotla, M. (in press). Leveraging covariational reasoning and emergent shape thinking to distinguish nonlinear and linear relationships. In G. Karagoz, I. O. Zembat, S. Arslan, & P. W. Thompson (Eds.), Quantitative reasoning in mathematics and science education. Springer.

  • Paoletti, T., Greenstein, S., Vishnubhotla, M., & Mohamed, M. M. (2019a). Designing tasks and 3D physical manipulatives to promote students’ covariational reasoning. In M. Graven, H. Venkat, A. Essien, & P. Vale (Eds.), Proceedings of the 43rd conference of the International Group for the Psychology of Mathematics Education (Vol 3, pp. 193–200). PME.

  • Paoletti, T., Vishnubhotla, M., Mohamed, M. M., & Cella, R. G. (2019b). Comparative and conditional inequalities: A distinction emerging from student thinking. In S. Otten, A. G. Candela, Z. de Araujo, C. Haines, & C. Munter (Eds.), Proceedings of the forty-first annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 186–190). University of Missouri.

  • Paoletti, T., & Moore, K. C. (2017). The parametric nature of two students’ covariational reasoning. The Journal of Mathematical Behavior, 48, 137–151. https://doi.org/10.1016/j.jmathb.2017.08.003

    Article  Google Scholar 

  • Sfard, A., & Linchevski, L. (1994). The gains and the pitfalls of reification — The case of algebra. Educational Studies in Mathematics, 26(2–3), 191–228. https://doi.org/10.1007/BF01273663

    Article  Google Scholar 

  • Smith, J., & Thompson, P. W. (2008). Quantitative reasoning and the development of algebraic reasoning. In J. J. Kaput, D. W. Carraher, & M. L. Blanton (Eds.), Algebra in the early grades (pp. 95–132). Lawrence Erlbaum Associates.

    Google Scholar 

  • Steffe, L. P., & Thompson, P. W. (2000). Teaching experiment methodology: Underlying principles and essential elements. In R. A. Lesh & A. E. Kelly (Eds.), Handbook of research design in mathematics and science education (pp. 267–307). Erlbaum.

    Google Scholar 

  • Steffe, L. P. (1991). The constructivist teaching experiment: Illustrations and implications. In E. von Glasersfeld (Ed.), Radical constructivism in mathematics education (pp. 177–194). Kluwer Academic Publishers. https://doi.org/10.1007/0-306-47201-5_9

  • Stevens, I. E., Paoletti, T., Moore, K. C., Liang, B., & Hardison, H. H. (2017). Principles for designing tasks that promote covariational reasoning. In A. Weinberg, C. Rasmussen, J. Rabin, M. Wawro, & S. Brown (Eds.), Proceedings of the Twentieth Annual Conference on Research in Undergraduate Mathematics Education (pp. 928–936).

  • Stevens, I. (2019). The role of multiplicative objects in a formula. In A. Weinberg, D. Moore-Russo, H. Soto, & M. Wawro (Eds.), Proceedings of the 22nd annual conference on research in mathematics education (pp. 273–281).

  • Thompson, P. W. (1994). Students, functions, and the undergraduate curriculum. CBMS Issues in Mathematics Education, 4, 21–41.

    Article  Google Scholar 

  • Thompson, P. W. (1995). Notation, convention, and quantity in elementary mathematics. In J. Sowder & B. Schapelle (Eds.), Providing a foundation for teaching middle school mathematics (pp. 199–221). SUNY Press.

    Google Scholar 

  • Thompson, P. W., & Carlson, M. P. (2017). Variation, covariation and functions: Foundational ways of mathematical thinking. In J. Cai (Ed.), Compendium for research in mathematics education (pp. 421–456). National Council of Teachers of Mathematics.

    Google Scholar 

  • Thompson, P. W. (2008). Conceptual analysis of mathematical ideas: Some spadework at the foundations of mathematics education. In O. Figueras, J. L. Cortina, S. Alatorre, T. Rojano, & A. Sépulveda (Eds.), Proceedings of the annual meeting of the international group for the psychology of mathematics education (Vol. 1, pp. 31–49). PME.

  • Thompson, P. W. (2011). Quantitative reasoning and mathematical modeling. In S. Chamberlin, L. L. Hatfield, & S. Belbase (Eds.), New perspectives and directions for collaborative research in mathematics education: Papers from a planning conference for WISDOM^e. University of Wyoming.

  • van Reeuwijk, M. (1995). The role of realistic situations in developing tools for solving systems of equations. [Paper presentation]. American Educational Research Association Annual Meeting, San Francisco, CA, United States. Retrieved January 10, 2021, from http://www.fisme.science.uu.nl/publicaties/literatuur/3781.pdf

  • van Reeuwijk, M. (2001). From informal to formal, progressive formalization: An example on “solving systems of equations.” In H. Chick, K. Stacey, J. Vincent, & J. Vincent (Eds.), The future of the teaching and learning of algebra: Proceedings of the 12th ICMI Study Conference (Vol. 2, pp. 613– 620). Melbourne, Australia: The University of Melbourne.

  • Yin, R. K. (2018). Case study research and applications: Design and methods (Sixth edition). SAGE.

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This material is based upon work supported by the Spencer Foundation under Grant No. 201900012.

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Correspondence to Teo Paoletti.

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Paoletti, T., Gantt, A. & Vishnubhotla, M. Constructing a system of covariational relationships: two contrasting cases. Educ Stud Math 110, 413–433 (2022). https://doi.org/10.1007/s10649-021-10134-0

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