Undergraduate Students’ Conceptions of Variability in a Dynamic Computer-Based Environment

  • George EkolEmail author
  • Nathalie Sinclair


This chapter reports on portions of a larger study aimed at exploring how undergraduate introductory statistics students make sense of the central concept of statistical variability. We began by exploring their existing ways of understanding variability as expressed through spoken word, gestures, drawings, and inscriptions. We then invited the participants to interact with dynamic models that we designed in order to make more explicit the notion of variability and analysed their emerging understanding. Based on our analysis of the changes in their multimodal communication, we argue that the use of dynamic mathematics environments can help promote a more physical and temporal understanding of statistical variability.


Statistical variability Dynamic models Multimodal communication 


  1. Arzarello, F., Paolo, D., Robutti, O., & Sabena, C. (2009). Gestures are semiotic resources in the mathematics classroom. Educational Studies in Mathematics, 70(3), 97–109.CrossRefGoogle Scholar
  2. del Mas, R., & Liu, R. J. (2005). Exploring students’ conceptions of the standard deviation. Statistics Education Research Journal, 4(1), 55–82. Retrieved from Scholar
  3. Fischbein, E. (1987). Intuition in mathematics and science: An educational approach. Dordrecht, The Netherlands: D. Reidel.Google Scholar
  4. Garfield, J. B., & Ben-Zvi, D. (2008). Developing students’ statistical reasoning: Connecting research and teaching practice. New York: Springer.Google Scholar
  5. Makar, K., & Confrey, J. (2005). “Variation-talk”: Articulating meaning in statistics. Statistics Education Research Journal, 4(1), 27–54 [Online:].Google Scholar
  6. Presmeg, N. C. (1986). Visualization in high school mathematics. For the Learning of Mathematics, 6(3), 42–46.Google Scholar
  7. Radford, L. (2009). Why do gestures matter? Sensuous cognition and the palpability of mathematical meanings. Educational Studies in Mathematics, 70(3), 111–126.CrossRefGoogle Scholar
  8. Reading, C., & Shaughnessy, J. M. (2004). Reasoning about variation. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking (pp. 201–226). Dordrecht, The Netherlands: Kluwer Academic.CrossRefGoogle Scholar
  9. Rossman, A. J. (1996). Workshop statistics: Discovery with data. New York: Springer.Google Scholar
  10. Sinclair, N., & Schiralli, M. (2003). A constructive response to ‘Where mathematics comes from’. Educational Studies in Mathematics, 52(1), 79–91.CrossRefGoogle Scholar
  11. Sinclair, N., & Gol Tabaghi, S. (2010). Drawing space: Mathematicians’ kinetic conceptions of eigenvectors. Education Studies in Mathematics, 74(3), 223–240.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Simon Fraser UniversityBurnabyCanada

Personalised recommendations