Abstract
In this research article, I present evidence of the existence of visual templates in pattern generalization activity. Such templates initially emerged from a 3-week design-driven classroom teaching experiment on pattern generalization involving linear figural patterns and were assessed for existence in a clinical interview that was conducted four and a half months after the teaching experiment using three tasks (one ambiguous, two well defined). Drawing on the clinical interviews conducted with 11 seventh- and eighth-grade students, I discuss how their visual templates have spawned at least six types of algebraic generalizations. A visual template model is also presented that illustrates the distributed and a dynamically embedded nature of pattern generalization involving the following factors: pattern goodness effect; knowledge/action effects; and the triad of stage-driven grouping, structural unit, and analogy.
Similar content being viewed by others
Notes
I prefer to use the term figural pattern in order to convey what I assume to be the “simultaneously conceptual and figural” (Fischbein, 1993, p. 160) nature of mathematical patterns. The term “geometric patterns” is not appropriate due to a potential confusion with geometric sequences (as instances of exponential functions in discrete mathematics). Also, I was not keen in using the term “pictorial patterns” due to the (Peircean) fact that figural patterns are not mere pictures of objects but exhibit characteristics associated with diagrammatic representations. The term “ambiguous” shares Neisser’s (1976) general notion of ambiguous pictures as conveying the “possibility of alternative perceptions” (p. 50).
Especially in the case of patterning tasks that involve figural cues, among the most important perception types that matter involve visual perception. Visual perception involves the act of coming to see; it is further characterized to be of two types, namely, sensory perception and cognitive perception (Dretske, 1990). Sensory (or object) perception is when individuals see an object as being a mere object in itself. Cognitive perception goes beyond the sensory when individuals see or recognize a fact or a property in relation to the object. For example, students who see consecutive groups of figural stages in Fig. 7 as mere sets of objects exhibit sensory perception. However, when they recognize that the stages taken together actually form a pattern sequence of objects, they manifest cognitive perception. Cognitive perception necessitates the use of conceptual and other cognitive-related processes, enabling learners to articulate what they choose to recognize as being a fact or a property of a target object. It is mediated in some way through other types of visual knowledge that bear on the object, and such types could be either cognitive or sensory in nature.
I share Resnik’s (1997) view that the “primary subject-matter [of mathematics] is not the individual mathematical objects but rather the structures in which they are arranged” (p. 201). Pattern stages basically convey positions in some overall structural relationship and, so, do not have an identity or distinguishing features outside that relationship despite the fact that specializing on a particular stage, which is considered a useful generalizing strategy, may give the impression of a structure.
Intrinsic to classroom teaching experiments that employ design research are two objectives, that is, developing an instructional framework that allows specific types of learning to materialize and analyzing the nature and content of such learning types within the articulated framework. Thus, in every design study, theory and practice are viewed as being equally important, which includes rigorously developing and empirically justifying a domain-specific instructional theory relevant to a concept being investigated. Further, the content of the proposed instructional theory involves a well-investigated learning trajectory and appropriate instructional tools that enable student learning to take place in various phases of the trajectory. Finally, instruction in design studies is characterized as having the following elements: it is experientially real for students; it enables students to reinvent mathematics through, at least initially, their commonsense experiences; and it fosters the emergence, development, and progressive evolution of student-generated models.
I am aware of issues surrounding researcher-driven interviews. The X 3 interviews that I conducted with the 11 students were third in a series of clinical interviews conducted during the 10-month project. A colleague who was external to the project conducted both X 1 and X 2 interviews. Also, the X 3 interview would in fact be seventh in the case of the cohort 1 participants. Yanos and Hopper (2008) suggest the use of repeated interviews as a way of minimizing what Bourdieu (1999) refers to as “false, collusive objectification” whereby interviewees respond in ways that please their interviewers. Further, the findings that I have drawn from the X 3 clinical interviews and that are reported in this article were triangulated with two other longitudinal data sources (i.e., available student written work and earlier clinical interviews conducted by a colleague).
References
Arcavi, A. (2003). The role of visual representations in the learning of mathematics. Educational Studies in Mathematics, 52, 215–241.
Arnheim, R. (1971). Visual thinking. Berkeley: University of California Press.
Becker, J. R., & Rivera, F. (2005). Generalization schemes in algebra of beginning high school students. In H. Chick & J. Vincent (Eds.), Proceedings of the 29th Conference of the International Group for the Psychology of Mathematics Education (pp. 121–128), vol. 4. Melbourne, Australia: University of Melbourne.
Berkeley, I. (2008). What the <0.70, 1.17, 0.99, 1.07> is a symbol? Minds and Machines, 18, 93–105.
Billings, E., Tiedt, T., & Slater, L. (2007). Algebraic thinking and pictorial growth patterns. Teaching Children Mathematics, 14(5), 302–308.
Bourdieu, P. (1999). Understanding. In P. Bourdieu (Ed.), The weight of the world: Social suffering in contemporary society (pp. 607–626). Cambridge: Polity Press.
Clark, F. & Kamii, C. (1996). Identification of multiplicative thinking in children in grades 1–5. Journal for Research in Mathematics Education, 27(1), 41–51.
Davis, R. (1984). Learning mathematics: The cognitive science approach to mathematics education. New Jersey: Ablex.
Davis, R. (1993). Visual theorems. Educational Studies in Mathematics, 24, 333–344.
Dörfler, W. (2008). En route from patterns to algebra: Comments and reflections. ZDM, 40(1), 143–160.
Dretske, F. (1990). Seeing, believing, and knowing. In D. Osherson, S. M. Kosslyn & J. Hollerback (Eds.), Visual cognition and action: An invitation to cognitive science (pp. 129–148). Cambridge: MIT Press.
Duval, R. (2006). A cognitive analysis of problems of comprehension in a learning of mathematics. Educational Studies in Mathematics, 61(1–2), 103–131.
Fischbein, E. (1993). The theory of figural concepts. Educational Studies in Mathematics, 24, 139–162.
Friel, S. & Markworth, K. (2009). A framework for analyzing geometric pattern tasks. Mathematics Teaching in the Middle School, 15(1), 24–33.
Gattis, M. & Holyoak, K. (1996). Mapping conceptual relations in visual reasoning. Journal of Experimental Psychology. Learning, Memory, and Cognition, 22(1), 231–239.
Giaquinto, M. (2007). Visual thinking in mathematics. Oxford: Oxford University Press.
Healy, L. & Hoyles, C. (1999). Visual and symbolic reasoning in mathematics: Making connections with computers? Mathematical Thinking and Learning, 1(1), 59–84.
Hutchins, E. (1995). Cognition in the wild. Cambridge: The MIT Press.
Koedinger, K. & Anderson, J. (1995). Abstract planning and perceptual chunks: elements of expertise in geometry. In B. Chandrasekaran, J. Glasgow & N. Hari Narayanan (Eds.), Diagrammatic reasoning: Cognitive and computational perspectives (pp. 577–626). Cambridge: The MIT Press.
Krutetskii, V. (1976). The psychology of mathematical abilities in school children. Chicago: University of Chicago Press.
Lannin, J. (2005). Generalization and justification: the challenge of introducing algebraic reasoning through patterning activities. Mathematical Thinking and Learning, 7(3), 231–258.
Lannin, J., Barker, D., & Townsend, B. (2006). Recursive and explicit rules: how can we build student algebraic understanding. Journal of Mathematical Behavior, 25, 299–317.
Lee, L. (1996). An initiation into algebra culture through generalization activities. In N. Bednarz, C. Kieran & L. Lee (Eds.), Approaches to algebra: Perspectives for research and teaching (pp. 87–106). Dordrecht: Kluwer.
Leslie, A., Xu, F., Tremoulet, P., & Scholl, B. (1998). Indexing and the object concept: Developing “what” and “where” systems. Trends in Cognitive Science, 2(1), 10–18.
Liang Chua, B., & Hoyles, C. (2009). Generalization and perceptual agility: how did teachers fare in a quadratic generalizing problem? In M. Joubert (Ed.), Proceedings of the British Society for Research into Learning Mathematics (pp. 13–18), vol. 29(2).
Lobato, J., Ellis, A., & Muñoz, R. (2003). How “focusing phenomena” in the instructional environment support individual students’ generalizations. Mathematical Thinking and Learning, 5(1), 1–36.
Mason, J., Graham, A., & Johnston-Wilder, S. (2005). Developing thinking in algebra. London: The Open University.
Metzger, W. (2006). Laws of seeing. Cambridge: MIT Press.
Mulligan, J. & Mitchelmore, M. (2009). Awareness of pattern and structure in early mathematical development. Mathematics Education Research Journal, 21(2), 33–49.
Neisser, U. (1976). Cognitive psychology. New York: Meredith.
Orton, A. & Orton, J. (1999). Pattern and the approach to algebra. In A. Orton (Ed.), Patterns in the teaching and learning of mathematics (pp. 104–123). London: Cassell.
Piaget, J. (1987). Possibility and necessity. Minneapolis: University of Minnesota Press.
Resnik, M. (1997). Mathematics as a science of patterns. Oxford: Oxford University Press.
Rivera, F. (2007). Visualizing as a mathematical way of knowing: understanding figural generalization. Mathematics Teacher, 101(1), 69–75.
Rivera, F. (2008). On the pitfalls of abduction: Complicities and complexities in patterning activity. For the Learning of Mathematics, 28(1), 17–25.
Rivera, F. (2009). Visuoalphanumeric mechanisms in pattern generalization. Paper presented at the International Meeting on Patterns. Portugal: Viana do Castelo.
Rivera, F. & Becker, J. (2007). Abductive–inductive (generalization) strategies of preservice elementary majors on patterns in algebra. Journal of Mathematical Behavior, 26(2), 140–155.
Rivera, F., & Becker, J. (Eds.) (2008a). From patterns to algebra. ZDM, 40(1).
Rivera, F. & Becker, J. (2008b). Middle school children’s cognitive perceptions of constructive and deconstructive generalizations involving linear figural patterns. ZDM, 40(1), 65–82.
Rivera, F. & Becker, J. (2009a). Algebraic reasoning through patterns. Mathematics Teaching in the Middle School, 15(4), 212–221.
Rivera, F. & Becker, J. (2009b). Formation of generalization in patterning activity. In J. Cai & E. Knuth (Eds.), Advances in mathematics education. Netherlands: Springer. in press.
Sophian, C. (2007). The origins of mathematical knowledge in childhood. New York: Erlbaum.
Stacey, K. & MacGregor, M. (2001). Curriculum reform and approaches to algebra. In R. Sutherland, T. Rojano, A. Bell & R. Lins (Eds.), Perspectives on school algebra (pp. 141–154). Dordrecht: Kluwer.
Steele, D. & Johanning, D. (2004). A schematic-theoretic view of problem solving and development of algebraic thinking. Educational Studies in Mathematics, 57, 65–90.
Trudeau, J. & Dixon, J. (2007). Embodiment and abstraction: Actions create relational representations. Psychonomic Bulletin & Review, 14(5), 994–1000.
Warren, E. & Cooper, T. (2008). Generalizing the pattern rule for visual growth patterns: actions that support 8 year olds’ thinking. Educational Studies in Mathematics, 67, 171–185.
Yanos, P. & Hopper, K. (2008). On “false, collusive objectification:” Becoming attuned to self-censorhip, performance and interviewer biases in qualitative interviewing. International Journal Social Research Methodology, 11(3), 229–237.
Zazkis, R., Dubinsky, E., & Dautermann, J. (1996). Using visual and analytic strategies: a study of students’ understanding of permutation and symmetry groups. Journal for Research in Mathematics Education, 27(4), 435–457.
Author information
Authors and Affiliations
Corresponding author
Additional information
Support for this research was funded by the National Science Foundation awarded to the author under DRL 0448649. The opinions expressed are solely of the author and do not necessarily reflect the views of the foundation.
Electronic supplementary material
Below is the link to the electronic supplementary material.
ESM 2
Graphical illustration of Emma’s direct formula \( s = n + n - 1 \) in relation to Fig. 9 (DOC 118 kb)
ESM 3
Additional examples of linear figural patterns that yield a CSG (DOC 5217 kb)
ESM 8
Frank’s justification of the formula \( S = 2x - 1 \) in relation to the Fig. 9 pattern (DOC 68 kb)
ESM 9
Frank’s justification of the formula \( {\text{S}} = 2\left( {x - 1} \right) + 1 \) in relation to the Fig. 9 pattern (DOC 72 kb)
ESM 10
Cherrie’s justification of her formula \( x = 2n - 1 \) in relation to the Fig. 9 pattern (DOC 100 kb)
Rights and permissions
About this article
Cite this article
Rivera, F.D. Visual templates in pattern generalization activity. Educ Stud Math 73, 297–328 (2010). https://doi.org/10.1007/s10649-009-9222-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10649-009-9222-0