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Emerging perspectives in mathematical cognition: contextualizing, complementizing, and complexifying

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Abstract

This article describes emerging perspectives on contextualizing, complementizing, and complexifying—three processes involved when individuals ascribe meaning to mathematical objects of their thinking. The article is oriented toward a dialectic between theory and empirical research and is structured in two parts. The first part focuses on an evolving theoretical framing that acknowledges the significance of these three processes in mathematical cognition. In the second part, the evolving theoretical framing is used to analyze one student’s knowing of the limit concept of a sequence. This analysis directs one’s attention to the emergence and function of this student’s knowledge resource, which was generic in usage and complex in structure, allowing the activation of productive ideas and contextual meaning-making as needed. Through this analysis, theoretical and interpretative possibilities were generated that inform research on mathematical cognition and elucidate the emerging theoretical perspectives of contextualizing, complementizing, and complexifying.

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Notes

  1. The term “knowledge resource” is used in the sense of Smith et al. (1993), designating “any feature of the learner’s present cognitive state that can serve as significant input to the process of conceptual growth” (p. 124).

  2. Duval (2006) revealed students’ confusion of a representation with the object that is being represented, substantiated with what he called a “cognitive paradox”: “how can they [individuals] distinguish the represented object from the semiotic representation used if they cannot get access to the mathematical object apart from the semiotic representation?” (p. 107).

  3. The subscript F indicates that the terms senseF, referenceF, thoughtF, and ideaF refer to Frege (1892b).

  4. For a discussion of generative and convergent approaches, see Clement (2000).

  5. The case study presented here has been subject of previous reports including Pinto and Tall (2002), Gray et al. (1999), and Tall et al. (2001). In those reports, the focus was on describing, exploring, and elaborating the strategy of giving meaning, often contrasted to extracting meaning. Further, those reports remained on a descriptive level when speaking about the student’s “mental actions with a mental object” (see Pinto & Tall, 2002): “Chris interprets the definition in terms of his old knowledge, explores the concept through thought experiment and reconstructs his understanding of the concept definition” (p. 5). The present article outstrips these reports by providing an explanatory account of the nature and function of the student’s knowledge resource as discussed here—an account that goes well beyond the descriptive approaches of previous reports.

  6. For a recent discussion on the ‘generic use’, see Yopp and Ely (2016).

  7. This is similar to Mason’s (1989) proposal that the essence of abstraction is coming to look at something differently, but differs as it is not so much a shift of attention but an expansion of attention.

  8. We use the notion of “conceptual unity” to stress the idea of coordinating diversity rather than looking for similarity. It shares Barnard and Tall’s (1997) view of a “cognitive unit” as “a piece of cognitive structure that can be held in the focus of attention all at one time” (p. 41), but extends their formulation as it suggests the emergence of a transcendent unity when two or more diverse ideasF are coordinated.

  9. For a detailed account of conceptual blending, see Fauconnier and Turner (2002).

References

  • Arnon, I., Cottrill, J., Dubinsky, E., Oktaç, A., Roa Fuentes, S., Trigueros, M., & Weller, K. (2014). APOS theory: A framework for research and curriculum development in mathematics education. New York, NY: Springer.

    Book  Google Scholar 

  • Arzarello, F., Bazzini, L., & Chiappini, G. (2001). A model for analysing algebraic processes of thinking. In R. Sutherland, T. Rojano, A. Bell, & R. Lins (Eds.), Perspectives on school algebra (pp. 61–81). Dordrecht, the Netherlands: Kluwer.

    Google Scholar 

  • Bass, H. (2017). Designing opportunities to learn mathematics theory-building practices. Educational Studies in Mathematics, 95(3), 229–244.

    Article  Google Scholar 

  • Barnard, A., & Tall, D. (1997). Cognitive units, connections and mathematical proof. In E. Pehkonen (Ed.), Proceedings of the 21 st International Conference for the Psychology of Mathematics Education (Vol. 2, pp. 41–48). Lahti, Finland: PME.

    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. 547–590). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Cornu, B. (1991). Limits. In D. O. Tall (Ed.), Advanced mathematical thinking (pp. 153–166). Dordrecht, the Netherlands: Kluwer.

    Google Scholar 

  • Davis, R., & Vinner, S. (1986). The notion of limit: Some seemingly unavoidable misconception stages. Journal of Mathematical Behavior, 5, 281–303.

    Google Scholar 

  • diSessa, A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 105–225.

    Article  Google Scholar 

  • diSessa, A., & Cobb, P. (2004). Ontological innovation and the role of theory in design experiments. The Journal of the Learning Sciences, 13(1), 77–103.

    Article  Google Scholar 

  • diSessa, A., Sherin, B., & Levin, M. (2016). Knowledge analysis: An introduction. In A. A. diSessa, M. Levin, & N. J. S. Brown (Eds.), Knowledge and interaction: A synthetic agenda for the learning sciences (pp. 30–71). New York, NY: Routledge.

    Google Scholar 

  • Dörfler, W. (2002). Formation of mathematical objects as decision making. Mathematical Thinking and Learning, 4(4), 337–350.

    Article  Google Scholar 

  • Dubinsky, E. (1991). Reflective abstraction in advanced mathematical thinking. In D. O. Tall (Ed.), Advanced mathematical thinking (pp. 95–123). Dordrecht, the Netherlands: Kluwer.

    Google Scholar 

  • Duval, R. (2006). A cognitive analysis of problems of comprehension in a learning of mathematics. Educational Studies in Mathematics, 61(1–2), 103–131.

    Article  Google Scholar 

  • Fauconnier, G., & Turner, M. (2002). The way we think: Conceptual blending and the mind’s hidden complexities. New York, NY: Basic Books.

    Google Scholar 

  • Frege, G. (1892a). Über Begriff und Gegenstand (on concept and object). Vierteljahresschrift für wissenschaftliche Philosophie, 16, 192–205.

    Google Scholar 

  • Frege, G. (1892b). Über Sinn und Bedeutung (On sense and reference). Zeitschrift für Philosophie und philosophische Kritik, 100, 25–50.

    Google Scholar 

  • Frege, G. (1918/1919). Der Gedanke: Eine logische Untersuchung. Beiträge zur Philosophie des deutschen Idealismus, 2, 58–77.

    Google Scholar 

  • Freudenthal, H. (1978). Weeding and sowing: Preface to a science of mathematical education. Dordrecht, the Netherlands: Reidel.

    Google Scholar 

  • Gray, E., Pinto, M., Pitta, D., & Tall, D. (1999). Knowledge construction and diverging thinking in elementary & advanced mathematics. Educational Studies in Mathematics, 38(1–3), 111–133.

    Article  Google Scholar 

  • Gray, E., & Tall, D. (1994). Duality, ambiguity and flexibility: A proceptual view of simple arithmetic. Journal for Research in Mathematics Education, 26(2), 115–141.

    Google Scholar 

  • Harel, G., & Tall, D. (1991). The general, the abstract, and the generic in advanced mathematics. For the Learning of Mathematics, 11(1), 38–42.

    Google Scholar 

  • Mason, J. (1989). Mathematical abstraction as the result of a delicate shift of attention. For the Learning of Mathematics, 9(2), 2–8.

    Google Scholar 

  • Mason, J., & Pimm, D. (1984). Generic examples: Seeing the general in the particular. Educational Studies in Mathematics, 15(3), 277–289.

    Article  Google Scholar 

  • Mole, C. (2011). Attention is cognitive unison: An essay in philosophical psychology. Oxford: Oxford University Press.

    Google Scholar 

  • Noss, R., & Hoyles, C. (1996). Windows on mathematical meanings: Learning cultures and computers. Dordrecht: Kluwer.

    Book  Google Scholar 

  • Peirce, C. (1931). The collected papers of Charles S. Peirce. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Piaget, J. [and collaborators] (1977/2001). Studies in reflecting abstraction (Recherches sur l’ abstraction réfléchissante) (translated by R. Campbell). Philadelphia, PA: Psychology Press.

  • Pinto, M. (1998). Students’ understanding of real analysis. Coventry, UK: University of Warwick.

    Google Scholar 

  • Pinto, M., & Tall, D. (2002). Building formal mathematics on visual imagery: A case study and a theory. For the Learning of Mathematics, 22(1), 2–10.

    Google Scholar 

  • Pirie, S., & Kieren, T. (1994). Growth in mathematical understanding: How can we characterise it and how can we represent it? Educational Studies in Mathematics, 26(2–3), 165–190.

    Article  Google Scholar 

  • Przenioslo, M. (2004). Images of the limit of function formed in the course of mathematical studies at the university. Educational Studies in Mathematics, 55(1–3), 103–132.

    Article  Google Scholar 

  • Radford, L. (2002). The seen, the spoken and the written: A semiotic approach to the problem of objectification of mathematical knowledge. For the Learning of Mathematics, 22(2), 14–23.

    Google Scholar 

  • Radford, L. (2013). Three key concepts of the theory of objectification: Knowledge, knowing, and learning. Journal of Research in Mathematics Education, 2(1), 7–44.

    Google Scholar 

  • Scheiner, T. (2016). New light on old horizon: Constructing mathematical concepts, underlying abstraction processes, and sense making strategies. Educational Studies in Mathematics, 91(2), 165–183.

    Article  Google Scholar 

  • Schoenfeld, A., Smith, J., & Arcavi, A. (1993). Learning: The microgenetic analysis of one student’s evolving understanding of a complex subject matter domain. In R. Glaser (Ed.), Advances in instructional psychology (Vol. 4, pp. 55–175). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Schwarz, B., Dreyfus, T., & Hershkowitz, R. (2009). The nested epistemic actions model for abstraction in context. In B. B. Schwarz, T. Dreyfus, & R. Hershkowitz (Eds.), Transformation of knowledge through classroom interaction (pp. 11–41). New York, NY: Routledge.

    Chapter  Google Scholar 

  • Sfard, A. (1991). On the dual nature of mathematical conceptions: Reflections on processes and objects as different sides of the same coin. Educational Studies in Mathematics, 22(1), 1–36.

    Article  Google Scholar 

  • Skemp, R. (1986). The psychology of learning mathematics. (2nd ed., first published 1971). London, UK: Penguin Group.

  • Smith, J., diSessa, A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis of knowledge in transition. Journal of the Learning Sciences, 3(2), 115–163.

    Article  Google Scholar 

  • Tall, D. (2013). How humans learn to think mathematically. Exploring the three worlds of mathematics. Cambridge, UK: Cambridge University Press.

    Book  Google Scholar 

  • Tall, D., Gray, E., Ali, M., Crowley, L., DeMarois, P., McGowen, M., … Yusof, Y. (2001). Symbols and the bifurcation between procedural and conceptual thinking. Canadian Journal of Science, Mathematics and Technology Education, 1(1), 81–104.

    Article  Google Scholar 

  • Tall, D., & Schwarzenberger, R. (1978). Conflicts in the learning of real numbers and limits. Mathematics Teaching, 82, 44–49.

    Google Scholar 

  • Tall, D., & Vinner, S. (1981). Concept image and concept definition in mathematics with particular reference to limit and continuity. Educational Studies in Mathematics, 12(2), 151–169.

    Article  Google Scholar 

  • van Oers, B. (1998). From context to contextualizing. Learning and Instruction, 8(6), 473–488.

    Article  Google Scholar 

  • Williams, S. (1991). Models of limit held by college calculus students. Journal for Research in Mathematics Education, 22(3), 219–236.

    Article  Google Scholar 

  • Yopp, D., & Ely, R. (2016). When does an argument use a generic example? Educational Studies in Mathematics, 91(1), 37–53.

    Article  Google Scholar 

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Acknowledgments

We are grateful to Annie Selden for her thoughtful comments and helpful suggestions given throughout the development of this paper. The first author wants to thank for support of this work both the Foundation of German Business through the Klaus Murmann Fellowship and Macquarie University through the Research Excellence Scholarship.

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Correspondence to Thorsten Scheiner.

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Scheiner, T., Pinto, M.M. Emerging perspectives in mathematical cognition: contextualizing, complementizing, and complexifying. Educ Stud Math 101, 357–372 (2019). https://doi.org/10.1007/s10649-019-9879-y

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