Abstract
Many areas of mathematics naturally lend themselves to machine-based computing environments, which suggests that computational environments may serve as useful mediating tools for the teaching and learning of mathematical content. While some mathematics classes are leveraging the use of computational tools, the implementation of computer programming to teach and learn mathematics is not widespread. In this study, I highlight the mathematical activity of four undergraduate students who used Python to solve mathematical tasks in the context of set theory and logic. To understand how the students leveraged the computer programming environment, I use the analytical framework known as the instrumental approach, which can be utilized to investigate the confluence of an artifact (often a piece of technology) and the human mind to solve a mathematical problem. Results indicate that the students were able to use Python and its computational capabilities such as For Loops, If Statements, and Functions as artifacts to reason about propositional statements, set intersection, and subsets. Specifically, three instrumented action schemes emerged from their work on three different tasks. These schemes describe the use of Python in creative ways to solve mathematical tasks, which suggests various implications for teaching and research.
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The datasets generated and analyzed during the current study are not publicly available due the fact that they constitute an excerpt of research in progress, but are available from the corresponding author on reasonable request and subject to approval through the California State University, Long Beach Institutional Review Board.
Notes
A 4-year HSI is a bachelor’s-granting (at minimum) institution in which at least 25% of the student population identify as Latinx.
References
Abrahamson, D., & Bakker, A. (2016). Making sense of movement in embodied design for mathematics learning. Cognitive Research: Principles and Implications, 1(1), 1–13.
Adiredja, A. (2019). Anti-deficit narratives: Engaging the politics of research on mathematical sense making. Journal for Research in Mathematics Education, 50(4), 401–435.
Artigue, M. (2002). Learning mathematics in a CAS environment: The genesis of a reflection about instrumentation and the dialectics between technical and conceptual work. International Journal of Computers for Mathematical Learning, 7(3), 245–274.
Bagni, G. (2006). Some cognitive difficulties related to the representations of two major concepts of set theory. Educational Studies in Mathematics, 62(3), 259–280.
Ball, L., Drijvers, P., Ladel, S., Siller, H.-S., Tabach, M., & Vale, C. (Eds.). (2018). Uses of technology in primary and secondary mathematics education: Tools, topics and trends. Springer.
Bogusevschi, D., Muntean, C., & Muntean, G. (2020). Teaching and learning physics using 3D virtual learning environment: A case study of combined virtual reality and virtual laboratory in secondary school. Journal of Computers in Mathematics and Science Teaching, 39(1), 5–18.
Buteau, C., Gueudet, G., Muller, E., Mgombelo, J., & Sacristán, A. (2020). University students turning computer programming into an instrument for ‘authentic’ mathematical work. International Journal of Mathematical Education in Science and Technology, 51(7), 1020–1041.
David, E., & Zazkis, D. (2019). Characterizing introduction to proof courses: A survey of US R1 and R2 course syllabi. International Journal of Mathematical Education in Science and Technology, 51(3), 388–404.
Dawkins, P. (2017). On the importance of set-based meanings for categories and connectives in mathematical logic. International Journal of Research in Undergraduate Mathematics Education, 3(3), 496–522.
Dawkins, P., & Cook, J. (2017). Guiding reinvention of conventional tools of mathematical logic: Students’ reasoning about mathematical disjunctions. Educational Studies in Mathematics, 94(3), 241–256.
Dogan-Dunlap, H. (2006). Lack of set theory relevant prerequisite knowledge. International Journal of Mathematical Education in Science and Technology, 37(4), 401–410.
Drijvers, P., Godino, J., Font, V., & Trouche, L. (2013). One episode, two lenses: A reflective analysis of student learning with computer algebra from instrumental and onto-semiotic perspectives. Educational Studies in Mathematics, 82(1), 23–49.
Fey, J. (1989). Technology and mathematics education: A survey of recent developments and important problems. Educational Studies in Mathematics, 20(3), 237–272.
Fischbein, E., & Baltsan, M. (1998). The mathematical concept of set and the ‘collection’ model. Educational Studies in Mathematics, 37(1), 1–22.
Freudenthal, H. (1991). Revisiting mathematics education: China lectures. Kluwer Academic Publishers.
Gravemeijer, K. (1999). How emergent models may foster the constitution of formal mathematics. Mathematical Thinking and Learning, 1(2), 155–177.
Gravemeijer, K. (2020a). A socio-constructivist elaboration of realistic mathematics education. In M. van den Heuvel-Panhuizen (Ed.), National reflections on the Netherlands didactics of mathematics (pp. 217–233). Springer.
Gravemeijer, K. (2020b). Emergent modeling: An RME design heuristic elaborated in a series of examples. Educational Designer, 4(13), 1–31.
Guin, D., & Trouche, L. (1998). The complex process of converting tools into mathematical instruments: The case of calculators. International Journal of Computers for Mathematical Learning, 3(3), 195–227.
Harel, I., & Papert, S. (1991). Software design as a learning environment. In I. Harel & S. Papert (Eds.), Constructionism (pp. 41–84). Ablex Publishing Corporation.
Hawthorne, C., & Rasmussen, C. (2015). A framework for characterizing students’ thinking about logical statements and truth tables. International Journal of Mathematical Education in Science and Technology, 46(3), 337–353.
Hickmott, D., Prieto-Rodriguez, E., & Holmes, K. (2018). A scoping review of studies on computational thinking in K–12 mathematics classrooms. Digital Experiences in Mathematics Education, 4(1), 48–69.
Inglis, M., & Alcock, L. (2012). Expert and novice approaches to reading mathematical proofs. Journal for Research in Mathematics Education, 43(4), 358–390.
Leng, N. (2011). Using an advanced graphing calculator in the teaching and learning of calculus. International Journal of Mathematical Education in Science and Technology, 42(7), 925–938.
Lestari, I., Kesumawati, N., & Ningsih, Y. (2020). Mathematical representation of Grade 7 students in set theory topics through problem-based learning. Infinity Journal, 9(1), 103–110.
Levine, B., Mauntel, M., Zandieh, M., & Plaxco, D. (2020). Chase that rabbit! Designing vector unknown: A linear algebra game. In S. Karunakaran, Z. Reed & A. Higgins (Eds.), Proceedings of the 23rd Annual Conference on Research in Undergraduate Mathematics Education (pp. 1262–1263). SIGMAA RUME.
Linchevski, L., & Vinner, S. (1988). The naive concept of sets in elementary teachers. In A. Borbas (Ed.), Proceedings of the 12th International Conference of the Psychology of Mathematics Education (pp. 471–478). PME.
Lockwood, E., & Mørken, K. (2021). A call for research that explores relationships between computing and mathematical thinking and activity in RUME. International Journal of Research in Undergraduate Mathematics Education, 7(3), 404–416.
Lockwood, E., DeJarnette, A., & Thomas, M. (2019). Computing as a mathematical disciplinary practice. Journal of Mathematical Behavior, 54, 100668.
Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60.
Martinez IV, A. (2022). Bridging the gap between set theory and logic: Leveraging computing as a mediating tool for learning. University of California San Diego. https://escholarship.org/uc/item/3297p2zx
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). A literature review of the challenges and opportunities of the transition from industry 4.0 to society 5.0. Energies, 15(17), 6276.
Oates, G. (2011). Sustaining integrated technology in undergraduate mathematics. International Journal of Mathematical Education in Science and Technology, 42(6), 709–721.
Papert, S. (1980). Mindstorms: children, computers, and powerful ideas. Basic books.
Pawa, S., Laosinchai, P., Nokkaew, A., & Wongkia, W. (2020). Students’ conception of set theory through a board game and an active-learning unit. International Journal of Innovation in Science and Mathematics Education, 28(1), 1–15.
Perlis, A. (1962). The computer in the university. In M. Greenberger (Ed.), Computers and the world of the future (pp. 180–219). MIT Press.
Rabardel, P. (1993a). Representations dans des situations d’activites instrumentees. In A. Weill-Fassina, P. Rabardel, & D. Dubois (Eds.), Representations pour l’action (pp. 97–111). Octares.
Rabardel, P. (1993). Les hommes et les technologies. Une approche cognitive des instruments contemporains. Armand Colin.
Rasmussen, C., Dunmyre, J., Fortune, N., & Keene, K. (2019). Modeling as a means to develop new ideas: The case of reinventing a bifurcation diagram. Primus, 29(6), 509–526.
Roorda, G., Vos, P., Drijvers, P., & Goedhart, M. (2016). Solving rate of change tasks with a graphing calculator: A case study on Instrumental Genesis. Digital Experiences in Mathematics Education, 2(3), 228–252.
Selden, J., & Selden, A. (1995). Unpacking the logic of mathematical statements. Educational Studies in Mathematics, 29(2), 123–151.
Sentance, S., Waite, J., & Kallia, M. (2019). Teaching computer programming with PRIMM: A sociocultural perspective. Computer Science Education, 29(2–3), 136–176.
Sentance, S., & Waite, J. (2017). PRIMM: Exploring pedagogical approaches for teaching text-based programming in school. In E. Barendsen & P. Hubwieser (Eds.), Proceedings of the 12th Workshop on Primary and Secondary Computing Education (pp. 113–114). WiPSCE.
Seymour, E., & Hunter, A.-B. (Eds.). (2019). Talking about leaving revisited: Persistence, relocation, and loss in undergraduate STEM education. Springer.
Seymour, E., & Hewitt, N. (1997). Talking about leaving. Westview Press.
Talbert, R. (2015). Inverting the transition-to-proof classroom. Primus, 25(8), 614–626.
Treffers, A. (1987). Three dimensions: A model of goal and theory description in mathematics instruction – the Wiskobas project. Springer.
Trouche, L. (2004). Managing complexity of human/machine interactions in computerized learning environments: Guiding students’ command process through instrumental orchestrations. International Journal of Computers for Mathematical Learning, 9(3), 281–307.
van den Heuvel-Panhuizen, M. (2003). The didactical use of models in realistic mathematics education: An example from a longitudinal trajectory on percentage. Educational Studies in Mathematics, 54(1), 9–35.
Vergnaud, G. (2009). The theory of conceptual fields. Human Development, 52(2), 83–94.
Vérillon, P., & Rabardel, P. (1995). Cognition and artifacts: A contribution to the study of thought in relation to instrumented activity. European Journal of Psychology of Education, 10(1), 77–101.
Vygotsky, L. (1978). Mind in society: Development of higher psychological processes (M. Cole, V. Jolm-Steiner, S. Scribner, & E. Souberman, Eds.). Harvard University Press.
World Economic Forum (2016). The future of jobs: Employment, skills and workforce strategy for the fourth industrial revolution. (Global challenge insight report.) World Economic Forum. https://www.weforum.org/docs/WEF_Future_of_Jobs.pdf
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Martinez, A.E. Using Python to Reason About Logic and Set Theory: Three Instrumented Action Schemes. Digit Exp Math Educ 10, 1–28 (2024). https://doi.org/10.1007/s40751-023-00130-9
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DOI: https://doi.org/10.1007/s40751-023-00130-9