Advertisement

ZDM

, Volume 49, Issue 4, pp 497–507 | Cite as

Using analogies to facilitate conceptual change in mathematics learning

  • Xenia Vamvakoussi
Original Article

Abstract

The problem of adverse effects of prior knowledge in mathematics learning has been amply documented and theorized by mathematics educators as well as cognitive/developmental psychologists. This problem emerges when students’ prior knowledge about a mathematical notion comes in contrast with new information coming from instruction, giving rise to systematic errors. Conceptual change perspectives on mathematics learning suggest that in such cases reorganization of students’ prior knowledge is necessary. Analogical reasoning, in particular cross-domain mapping, is considered an important mechanism for conceptual restructuring. However, the use of analogies in instruction is often found ineffective, mainly because the structural similarity between two domains is obscure for students. To deal with this problem, John Clement and his colleagues developed the bridging strategy that uses multiple analogies to bring students to pay attention to the structural similarity that often goes unnoticed. This paper focuses on the cross-domain mapping between number and the (geometrical) line that has been instrumental in the development of the number concept. I summarize findings of a series of studies that investigated students’ understandings of density in arithmetical and geometrical contexts from a conceptual change perspective; and I discuss how this research-based evidence was used to design an intervention study that used the analogy “numbers are points on the number line”, and a bridging analogy (“the number line is like an imaginary rubber band that never breaks, no matter how much it is stressed”) with the aim of bringing the notion of density within the grasp of secondary students.

Keywords

Conceptual change Rational numbers Analogies Bridging analogies 

References

  1. Alcock, L., Ansari, B., Batchelor, S., Bissona, M. J., De Smedt, B., Gilmore C. ,..., & Webe, K (2016). Challenges in mathematical cognition: a collaboratively-derived research agenda. Journal of Numerical Cognition, 2, 20–41. doi: 10.5964/jnc.v2i1.10.CrossRefGoogle Scholar
  2. Anderson, J.R., Reder, L.M., & Simon, H.A. (2000). Applications and misapplications of cognitive psychology to mathematics education. Texas Educational Review. Retrieved on 2016 August 15 from http://act-r.psy.cmu.edu/?post_type=publications&p=13741.
  3. Berch, D. B. (2016). Disciplinary differences between cognitive psychology and mathematics education: A developmental disconnection syndrome. Journal of Numerical Cognition, 2(1), 42–47. doi: 10.5964/jnc.v2i1.23.CrossRefGoogle Scholar
  4. Bowdle, B. F., & Gentner, D. (2005). The career of metaphor. Psychological Review, 112(1), 193–216. doi: 10.1037/0033-295X.112.1.193.CrossRefGoogle Scholar
  5. Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.Google Scholar
  6. Brousseau, G. (2002). Theory of didactical situations in mathematics (N. Balacheff, M. Cooper, R. Sutherland, & V. Warfield, Eds. and Trans.). New York: Kluwer Academic Publishers.Google Scholar
  7. Brown, D. E., & Clement, J. (1989). Overcoming misconceptions via analogical reasoning: Abstract transfer versus explanatory model construction. Instructional Science, 18, 237–261. doi: 10.1007/BF00118013.CrossRefGoogle Scholar
  8. Bryce, T., & Macmillan, K. (2005). Encouraging conceptual change: the use of bridging analogies in the teaching of action–reaction forces and the ‘at rest’condition in physics. International Journal of Science Education, 27, 737–763. doi: 10.1080/09500690500038132.CrossRefGoogle Scholar
  9. Carey, S. (2004). Bootstrapping and the origin of concepts. Daedalus, 133, 59–68. doi: 10.1162/001152604772746701.CrossRefGoogle Scholar
  10. Clement, J. (1993). Using bridging analogies and anchoring intuitions to deal with students’preconceptions in physics. Journal of Research in Science Teaching, 30, 1241–1257.CrossRefGoogle Scholar
  11. Clement, J. (2008). The role of explanatory models in teaching for conceptual change. In S. Vosniadou (Ed.), International handbook of research on conceptual change (1st ed., pp. 417–452). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  12. Dantzig, T. (2005). Number: The language of science (4th edn.). New York: Pi Press.Google Scholar
  13. De Smedt, B., & Verschaffel, L. (2010). Travelling down the road: From cognitive neuroscience to mathematics education …and back. ZDM—The International Journal on Mathematics Education, 42, 649–654. doi: 10.1007/s11858-010-0282-5.CrossRefGoogle Scholar
  14. Donovan, M. S., & Bransford, J. D. (2005). How students learn: History, mathematics, and science in the classroom. Washington, DC: The National Academies Press.Google Scholar
  15. Doritou, M., & Gray, E. (2009). Teachers’ subject knowledge: the number line representation. Paper presented at 6th Conference of the European society for Research in Mathematics Education (CERME 6), Lyon, France.Google Scholar
  16. Duit, R. (1991). On the role of analogies and metaphors in learning science. Science Education, 75, 649–672. doi: 10.1002/sce.3730750606.CrossRefGoogle Scholar
  17. Dunbar, K. (2001). The analogical paradox: Why analogy is so easy in naturalistic settings yet so difficult in the psychological laboratory. In D. Gentner, K. J. Holyoak & B. N. Kokinov (Eds.), The analogical mind: Perspectives from cognitive science (pp. 313–3340). Cambridge: The MIT Press.Google Scholar
  18. English, L. D. (1997). Analogies, metaphors, and images: Vehicles for mathematical reasoning. In L. D. English (Ed.), Mathematical reasoning: Analogies, metaphors, and images (pp. 3–18). Mahwah, NJ: Erlbaum.Google Scholar
  19. Fischbein, E. (1987). Intuition in science and mathematics. Dordrecht: Kluwer Academic Publishers.Google Scholar
  20. Gelman, R. (1990). First principles organize attention to and learning about relevant data: Number and animate-inanimate distinction as examples. Cognitive Science, 14, 79–106. doi: 10.1207/s15516709cog1401_5.CrossRefGoogle Scholar
  21. Gelman, R. (2000). The epigenesis of mathematical thinking. Journal of Applied Developmental Psychology, 21, 27–37. doi: 10.1016/S0193-3973(99)00048-9.CrossRefGoogle Scholar
  22. Gentner, D., Brem, S., Ferguson, R. W., Markman, A. B., Levidow, B. B., Wolff, P., & Forbus, K. D. (1997). Analogical reasoning and conceptual change: A case study of Johannes Kepler. Journal of the Learning Sciences, 6(1), 3–40. doi: 10.1207/s15327809jls0601_2.CrossRefGoogle Scholar
  23. Gentner, D., & Wolff, P. (2000). Metaphor and knowledge change. In E. Dietrich & A. Markman (Eds.), Conceptual change in humans and machines (pp. 295–342). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  24. Giannakoulias, E., Souyoul, A., & Zachariades, T. (2007). Students’ thinking about fundamental real numbers properties. In D. Pitta-Pantazi & G. Philippou (Eds.), Proceedings of the Fifth Congress of the European Society for Research in Mathematics Education (pp. 416–425). Cyprus: ERME, Department of Education, University of Cyprus.Google Scholar
  25. Hartnett, P., & Gelman, R. (1998). Early understandings of numbers: Paths or barriers to the construction of new understandings? Learning and Instruction, 8 ,341–374. doi: 10.1016/S0959-4752(97)00026-1.CrossRefGoogle Scholar
  26. Jacob, N. J., Vallentin, D., & Nieder, A. (2012). Relating magnitudes: The brain’s code for proportions. Trends in Cognitive Science, 16, 157–166. doi: 10.1016/j.tics.2012.02.002.CrossRefGoogle Scholar
  27. Kilpatrick, J. (2014). History of research in mathematics education. In S. Lehrman (Ed.), Encyclopedia of mathematics education (pp. 267–271). London: Springer.Google Scholar
  28. Kilpatrick, J., Swafford, J., & Findell, B. (2001). Adding + it up. Helping children learn mathematics. Washington, DC: National Academy Press.Google Scholar
  29. Lakoff, G., & Núñez, R. (2000). Where mathematics comes from: How the embodied mind brings mathematics into being. New York: Basic Books.Google Scholar
  30. Limón, M. (2001). On the cognitive conflict as an instructional strategy for conceptual change: A critical appraisal. Learning and Instruction, 11, 357–380. doi: 10.1016/S0959-4752(00)00037-2.CrossRefGoogle Scholar
  31. McMullen, J., Laakkonen, E., Hannula-Sormunen, M., & Lehtinen, E. (2015). Modeling the developmental trajectories of rational number concept (s). Learning and Instruction, 37, 14–20. doi: 10.1016/j.learninstruc.2013.12.004.CrossRefGoogle Scholar
  32. Merenluoto, K., & Lehtinen, E. (2002). Conceptual change in mathematics: Understanding the real numbers. In M. Limon & L. Mason (Eds.), Reconsidering conceptual change: Issues in theory and practice (pp. 233–258). Dordrecht: Kluwer.Google Scholar
  33. Merenluoto, K., & Lehtinen, E. (2004). Number concept and conceptual change: Towards a systemic model of the processes of change. Learning and Instruction, 14, 519–534. doi: 10.1016/j.learninstruc.2004.06.016.CrossRefGoogle Scholar
  34. Moss, J. (2005). Pipes, tubes, and beakers: New approaches to teaching the rational number system. In M. S. Donovan & J. D. Bransford (Eds.), How students learn: Mathematics in the classroom (pp. 121–162). Washington, DC: National Academic Press.Google Scholar
  35. Ni, Y., & Zhou, Y.-D. (2005). Teaching and learning fraction and rational numbers: The origins and implications of whole number bias. Educational Psychologist, 40(1), 27–52. doi: 10.1207/s15326985ep4001_3.CrossRefGoogle Scholar
  36. Núñez, R., & Lakoff, G. (2005). The cognitive foundations of mathematics: The role of conceptual metaphor. In J. I. D. Campbell (Ed.), Handbook of mathematical cognition (pp. 109–124). New York, NY: Psychology Press.Google Scholar
  37. Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Towards a theory of conceptual change. Science Education, 66, 211–227. doi: 10.1002/sce.3730660207.CrossRefGoogle Scholar
  38. Resnick, L., & Singer, J. (1993). Protoquantitative origins of ratio reasoning. In T. Carpenter, E. Fennema & T. Romberg (Eds.), Rational numbers: An integration of research (pp. 107–130). Hillsdale, NJ: Erlbaum.Google Scholar
  39. Resnick, L. B. (2006). The dilemma of mathematical intuition in learning. In J. Novotná, H. Moraová, M. Krátká & N. Stehliková (Eds.), Proceedings of the 30th Conference of the International Group for the Psychology of Mathematics Education (Vol. 1, pp. 173–175). Prague: PME.Google Scholar
  40. Richland, L. E., Zur, O., & Holyoak, K. J. (2007). Cognitive supports for analogies in the mathematics classroom. Science, 316, 1128–1129. doi: 10.1126/science.1142103.CrossRefGoogle Scholar
  41. Savinainen, A., Scott, P., & Viiri, J. (2005). Using a bridging representation and social interactions to foster conceptual change: Designing and evaluating an instructional sequence for Newton’s third law. Science Education, 89 (2), 175–195. doi: 10.1002/sce.20037.CrossRefGoogle Scholar
  42. Sbaragli, S. (2006). Primary school teachers’ beliefs and change of beliefs on mathematical infinity. Mediterranean Journal for Research in Mathematics Education, 5(2), 49–76.Google Scholar
  43. Schneider, M., Vamvakoussi, X., & Van Dooren, W. (2012). Conceptual change. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 735–738). New York: Springer.Google Scholar
  44. Schoenfeld, A. H. (Ed.). (1987). Cognitive science and mathematics education. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  45. Siegler, R. S. (2016). Magnitude knowledge: The common core of numerical development. Developmental Science, 19, 341–361. doi: 10.1111/desc.12395.CrossRefGoogle Scholar
  46. Smith, C. L., Solomon, G. E. A., & Carey, S. (2005). Never getting to zero: Elementary school students’ understanding of the infinite divisibility of number and matter. Cognitive Psychology, 51, 101–140. doi: 10.1016/j.cogpsych.2005.03.001.CrossRefGoogle Scholar
  47. Smith, J. P., diSessa, A. A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis of knowledge in transition. The Journal of the Learning Sciences, 3(2), 115–163. doi: 10.1207/s15327809jls0302_1.CrossRefGoogle Scholar
  48. Steffe, L. P., & Olive, J. (2010). Children’s fractional knowledge. New York, NY: Springer.CrossRefGoogle Scholar
  49. Vamvakoussi, X. (2010). The ‘numbers are points οn the line’ analogy: Does it have an instructional value? In L. Verschaffel, E. De Corte, T. de Jong & J. Elen (Eds.), Use of external representations in reasoning and problem solving: Analysis and improvement. New Perspectives on Learning and Instruction Series (pp. 209–224). New York, NY: Routlege.Google Scholar
  50. Vamvakoussi, X. (2015). The development of rational number knowledge: Old topic, new insights. Learning and Instruction, 37, 50–55. doi: 10.1016/j.learninstruc.2015.01.002.CrossRefGoogle Scholar
  51. Vamvakoussi, X., Christou, K. P., & Van Dooren, W. (2011). What fills the gap between the discrete and the dense? Greek and Flemish students’ understanding of density. Learning & Instruction, 21, 676–685. doi: 10.1016/j.learninstruc.2011.03.005.CrossRefGoogle Scholar
  52. Vamvakoussi, X., Kargiotakis, G., Kollias, Mamalougos, N. G., & Vosniadou, S. (2003). Collaborative modelling of rational numbers. In B. Wasson, S. Ludvigsen & U. Hoppe (Eds.), Designing for change in networked learning environments—Proceedings of the International Conference on Computer Support for Collaborative Learning (pp. 103–107). Dordrecht: Kluwer.Google Scholar
  53. Vamvakoussi, X., Van Dooren, W., & Verschaffel, L. (2012). Naturally biased? In search for reaction time evidence for a natural number bias in adults. The Journal of Mathematical Behavior, 31, 344–355. doi: 10.1016/j.jmathb.2012.02.001.CrossRefGoogle Scholar
  54. Vamvakoussi, X., & Vosniadou, S. (2004). Understanding the structure of the set of rational numbers: A conceptual change approach. Learning and Instruction, 14, 453–467. doi: 10.1016/j.learninstruc.2004.06.013.CrossRefGoogle Scholar
  55. Vamvakoussi, X., & Vosniadou, S. (2007). How many numbers in an interval? Presuppositions, synthetic models and the effect of the number line. Ιn S. Vosniadou. In A. Baltas & X. Vamvakoussi (Eds.), Reframing the conceptual change approach in learning and instruction (pp. 267–283). Oxford: Elsevier.Google Scholar
  56. Vamvakoussi, X., & Vosniadou, S. (2010). How many decimals are there between two fractions? Aspects of secondary school students’ understanding of rational numbers and their notation. Cognition and Instruction, 28(2), 181–209. doi: 10.1080/07370001003676603.CrossRefGoogle Scholar
  57. Vamvakoussi, X., & Vosniadou, S. (2012). Bridging the gap between the dense and the discrete: the number line and the “rubber line” bridging analogy. Mathematical Thinking and Learning, 14, 265–284. doi: 10.1080/10986065.2012.717378.CrossRefGoogle Scholar
  58. Vamvakoussi, X., Vosniadou, S., & Van Dooren, W. (2013). The framework theory approach applied to mathematics learning. In S. Vosniadou (Ed.), International handbook of research on conceptual change (2nd Ed.) (pp. 305–321). New York, NY: Routledge.Google Scholar
  59. Van Dooren, W., Vamvakoussi, X., & Verschaffel, L. (2013). Mind the gap–Task design principles to achieve conceptual change in rational number understanding. In C. Margolinas (Ed.), Task design in mathematics education: Proceedings of ICMI Study 22 (pp. 519–527). Oxford: International Commission on Mathematical Instruction.Google Scholar
  60. Verschaffel, L., & Vosniadou, S. (Guest Eds.). (2004). The conceptual change approach to mathematics learning and teaching [Special Issue]. Learning and Instruction14(5).Google Scholar
  61. Vosniadou, S. (1989). Analogical reasoning as a mechanism in knowledge acquisition: A developmental perspective. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 413–436). Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  62. Vosniadou, S., Ioannides, C., Dimitrakopoulou, A., & Papademetriou, E. (2001). Designing learning environments to promote conceptual change in science. Learning and Instruction, 11, 381–419. doi: 10.1016/S0959-4752(00)00038-4.CrossRefGoogle Scholar
  63. Vosniadou, S., Vamvakoussi, X., & Skopeliti, I. (2008). Τhe framework theory approach to conceptual change. In S. Vosniadou (Ed.), International handbook of research on conceptual change (1st ed., pp. 3–34). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  64. Yilmaz, S., Eryilmaz, A., & Geban, O. (2006). Assessing the impact of bridging analogies in mechanics. School Science and Mathematics, 106(6), 220–230. doi: 10.1111/j.1949-8594.2006.tb17911.x.CrossRefGoogle Scholar

Copyright information

© FIZ Karlsruhe 2017

Authors and Affiliations

  1. 1.Department of Early Childhood EducationUniversity of IoanninaIoanninaGreece

Personalised recommendations