Published 2025-05-29
Keywords
- Covariational Reasoning
Copyright (c) 2025 Kevin Moore

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abstract
Covariational reasoning has emerged as a productive construct to characterize students’ mathematical development. Researchers have illustrated its importance for major middle, secondary, and undergraduate mathematical concepts including rate of change, accumulation, and modeling. Within this line of work, several researchers have indicated differences between experiential and conceptual time with respect to the covariational relationships students construct. I draw on this body of literature and return to Piaget’s perspective of time to provide a framework for the role of time in students’ (co)variational reasoning. The framework also clarifies the nature of the multiplicative objects underlying students’ (co)variational relationships. To illustrate the framework and capture its emergence from second-order models of students’ mathematics, I also describe the framework as it relates to students’ engagement in a task.
References
- Byerley, C., & Thompson, P. W. (2017). Secondary mathematics teachers’ meanings for measure, slope, and rate of change. The Journal of Mathematical Behavior, 48, 168-193.
- 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
- Castillo-Garsow, C. (2010). Teaching the Verhulst model: A teaching experiment in covariational reasoning and exponential growth [Ph.D. Dissertation]. Arizona State University: USA.
- Castillo-Garsow, C. (2012). Continuous quantitative reasoning. In R. Mayes & L. L. Hatfield (Eds.), Quantitative Reasoning and Mathematical Modeling: A Driver for STEM Integrated Education and Teaching in Context (pp. 55-73). University of Wyoming.
- Ellis, A. B. (2011). Algebra in the middle school: Developing functional relationships through quantitative reasoning. In J. Cai & E. Knuth (Eds.), Early Algebraization (pp. 215-238). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-17735-4_13
- Ellis, A. B., Özgür, Z., Kulow, T., Williams, C. C., & Amidon, J. (2015). Quantifying exponential growth: Three conceptual shifts in coordinating multiplicative and additive growth. The Journal of Mathematical Behavior, 39, 135-155. https://doi.org/10.1016/j.jmathb.2015.06.004
- Fraisse, P. (1984). Perception and estimation of time. Annual Review of Psychology, 35, 1-36.
- Gantt, A. L., Paoletti, T., & Corven, J. (2023). Exploring the Prevalence of Covariational Reasoning Across Mathematics and Science Using TIMSS 2011 Assessment Items. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-023-10353-2
- Inhelder, B., & Piaget, J. (1964). The early growth of logic in the child: Classification and seriation. Routledge & Kegan Paul.
- Johnson, H. L. (2015a). Secondary students’ quantification of ratio and rate: A framework for reasoning about change in covarying quantities. Mathematical Thinking and Learning, 17(1), 64-90.
- Johnson, H. L. (2015b). Together yet separate: Students’ associating amounts of change in quantities involved in rate of change. Educational Studies in Mathematics, 1-22. https://doi.org/10.1007/s10649-014-9590-y
- Kant, I. (1781/2003). Critique of pure reason (M. Weigelt, Trans.). Penguin Classics.
- Keene, K. A. (2007). A characterization of dynamic reasoning: Reasoning with time as parameter. The Journal of Mathematical Behavior, 26(3), 230-246. https://doi.org/https://doi.org/10.1016/j.jmathb.2007.09.003
- Kerrigan, S. (2023). Modeling middle grade students' algebraic and covariational reasoning using unit transformations and working memory [Ph.D. Dissertation]. Virginia Tech: USA.
- Kertil, M., Erbas, A. K., & Cetinkaya, B. (2019). Developing prospective teachers’ covariational reasoning through a model development sequence. Mathematical Thinking and Learning, 21(3), 207-233. https://doi.org/10.1080/10986065.2019.1576001
- Lee, H. Y., Hardison, H., & Paoletti, T. (2020). Foregrounding the background: Two uses of coordinate systems. For the Learning of Mathematics, 40(2), 32-37.
- Lee, H. Y., Moore, K. C., & Tasova, H. I. (2019). Reasoning within quantitative frames of reference: The case of Lydia. The Journal of Mathematical Behavior, 53, 81-95.
- Liang, B., & Moore, K. C. (2021). Figurative and operative partitioning activity: A student’s meanings for amounts of change in covarying quantities. Mathematical Thinking & Learning, 23(4), 291-317.
- Ludwig, W., & Luciano, B. (2021). Tractatus Logico-Philosophicus : Centenary Edition (Vol. Centenary edition / edited and with a foreword by Luciano Bazzocchi ; introduction by P.M.S. Hacker) [Book]. Anthem Press. https://search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib&db=nlebk&AN=2922129&site=eds-live&custid=uga1
- Moore, K. C. (2014). Quantitative reasoning and the sine function: The case of Zac. Journal for Research in Mathematics Education, 45(1), 102-138.
- Moore, K. C. (2021). Graphical shape thinking and transfer. In C. Hohensee & J. Lobato (Eds.), Transfer of Learning: Progressive Perspectives for Mathematics Education and Related Fields (pp. 145-171). Springer.
- Moore, K. C., Liang, B., Stevens, I. E., Tasova, H. I., & Paoletti, T. (2022). Abstracted Quantitative Structures: Using Quantitative Reasoning to Define Concept Construction. In G. Karagöz Akar, İ. Ö. Zembat, S. Arslan, & P. W. Thompson (Eds.), Quantitative Reasoning in Mathematics and Science Education (pp. 35-69). Springer International Publishing. https://doi.org/10.1007/978-3-031-14553-7_3
- Moore, K. C., Paoletti, T., & Musgrave, S. (2013). Covariational reasoning and invariance among coordinate systems. The Journal of Mathematical Behavior, 32(3), 461-473. https://doi.org/10.1016/j.jmathb.2013.05.002
- Moore, K. C., Stevens, I. E., Paoletti, T., Hobson, N. L. F., & Liang, B. (2019). Pre-service teachers’ figurative and operative graphing actions. The Journal of Mathematical Behavior, 56. https://doi.org/10.1016/j.jmathb.2019.01.008
- Paoletti, T. (2020). Reasoning about relationships between quantities to reorganize inverse function meanings: The case of Arya. The Journal of Mathematical Behavior, 57, 100741. https://doi.org/https://doi.org/10.1016/j.jmathb.2019.100741
- Paoletti, T., Gantt, A. L., & Corven, J. (2023). A Local Instruction Theory for Emergent Graphical Shape Thinking: A Middle School Case Study. Journal for Research in Mathematics Education, 54(3), 202-224.
- 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
- Patterson, C. L., & McGraw, R. (2018). When time is an implicit variable: An investigation of students’ ways of understanding graphing tasks. Mathematical Thinking and Learning, 20(4), 295-323. https://doi.org/10.1080/10986065.2018.1509421
- Piaget, J. (1954). The construction of reality in the child [doi:10.1037/11168-000]. Basic Books. https://doi.org/10.1037/11168-000
- Piaget, J. (1970). The child's conception of time (A. Pomerans, Trans.). Basic Books.
- Piaget, J. (2001). Studies in reflecting abstraction. Psychology Press Ltd.
- Rodriguez, J.-M. G., Bain, K., Towns, M. H., Elmgren, M., & Ho, F. M. (2019). Covariational reasoning and mathematical narratives: investigating students’ understanding of graphs in chemical kinetics [10.1039/C8RP00156A]. Chemistry Education Research and Practice, 20(1), 107-119. https://doi.org/10.1039/C8RP00156A
- Saldanha, L. A., & Thompson, P. W. (1998). Re-thinking co-variation from a quantitative perspective: Simultaneous continuous variation. In S. B. Berensen, K. R. Dawkings, M. Blanton, W. N. Coulombe, J. Kolb, K. Norwood, & L. Stiff (Eds.), Proceedings of the 20th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (Vol. 1, pp. 298-303). ERIC Clearinghouse for Science, Mathematics, and Environmental Education.
- Sokolowski, A. (2020). Developing Covariational Reasoning Among Students Using Contexts of Formulas. Physics educator., 2(4). https://doi.org/10.1142/S266133952050016X
- Stalvey, H. E., & Vidakovic, D. (2015). Students’ reasoning about relationships between variables in a real-world problem. The Journal of Mathematical Behavior, 40, 192-210.
- Steffe, L. P. (2001). A new hypothesis concerning children’s fractional knowledge. The Journal of Mathematical Behavior, 20(3), 267-307. https://doi.org/https://doi.org/10.1016/S0732-3123(02)00075-5
- Steffe, L. P., & Olive, J. (2010). Children's Fractional Knowledge. Springer.
- 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.
- Tasova, H. I., & Moore, K. C. (2020). Framework for representing a multiplicative object in the context of graphing. In A. I. Sacristán, J. C. Cortés-Zavala, & P. M. Ruiz-Arias (Eds.), Mathematics Education Across Cultures: Proceedings of the 42nd Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education, Mexico (pp. 210-219). Cinvestav/PME-NA.
- Thompson, P. W. (1994). Images of rate and operational understanding of the fundamental theorem of calculus. Educational Studies in Mathematics, 26(2-3), 229-274. https://doi.org/10.1007/BF01273664
- 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 (pp. 33-57).
- Thompson, P. W. (2012). Advances in research on quantitative reasoning. In R. Mayes & L. L. Hatfield (Eds.), WISDOMe monographs (Vol. 2) Quantitative reasoning: Current state of understanding (pp. 143-148). University of Wyoming.
- Thompson, P. W., Byerley, C., & O’Bryan, A. E. (2024). Figurative and operative imagery: Essential aspects of reflection in the development of schemes and meanings. In P. C. Dawkins, A. J. Hackenberg, & A. Norton (Eds.), Piaget’s Genetic Epistemology in Mathematics Education Research. Springer, Cham.
- Thompson, P. W., & Carlson, M. P. (2017). Variation, covariation, and functions: Foundational ways of thinking mathematically. In J. Cai (Ed.), Compendium for Research in Mathematics Education (pp. 421-456). National Council of Teachers of Mathematics.
- Thompson, P. W., Hatfield, N., Yoon, H., Joshua, S., & Byerley, C. (2017). Covariational reasoning among U.S. and South Korean secondary mathematics teachers. The Journal of Mathematical Behavior, 48, 95-111. https://doi.org/10.1016/j.jmathb.2017.08.001
- von Glasersfeld, E. (1984). Thoughts about space, time, and the concept of identity. In A. Pedretti (Ed.), Of of: A book conference (pp. 21-36). Princelet Editions.
- von Glasersfeld, E. (1995). Radical constructivism: A way of knowing and learning. Falmer Press. https://doi.org/10.4324/9780203454220
- von Glasersfeld, E. (1997). The conceptual construction of time Mind and Time, Neuchâtel.
- Yoon, H., Byerley, C. O.., Joshua, S., Moore, K., Park, M. S., Musgrave, S., Valaas, L., & Drimalla, J. (2021). United States and South Korean citizens’ interpretation and assessment of COVID-19 quantitative data. The Journal of Mathematical Behavior, 62, 100865. https://doi.org/https://doi.org/10.1016/j.jmathb.2021.100865