Skip to main content
Log in

Dot plots and hat plots: supporting young students emerging understandings of distribution, center and variability through modeling

  • Original Article
  • Published:
ZDM Aims and scope Submit manuscript

Abstract

An important use of statistical models and modeling in education stems from the potential to involve students more deeply with conceptions of distribution, variation and center. As models are key to statistical thinking, introducing students to modeling early in their schooling will likely support the statistical thinking that underpins later, more advanced work with increasingly sophisticated statistical models. In this case study, a class of 10–11 year-old students are engaged in an authentic task designed to elicit modeling. Multiple data sources were used to develop insights into student learning: lesson videotape, work samples and field notes. Through the use of dot plots and hat plots as data models, students made comparisons of the data sets, articulated the sources of variability in the data, sought to minimize the variability, and then used their models to both address the initial problem and to justify the effectiveness of their attempts to reduce induced variation. This research has implications for statistics curriculum in the early formal years of schooling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Ainley, J., & Pratt, D. (2017). Computational modelling and children’s expressions of signal and noise. Statistics Education Research Journal, 16(2), 15–37.

    Google Scholar 

  • Australian Curriculum, Assessment and Reporting Authority (2017). Australian Curriculum: mathematics v8.3. https://www.australiancurriculum.edu.au/f-10-curriculum/mathematics. Accessed 23 Nov 2017.

  • Ben-Zvi, D., & Amir, Y. (2005). How do primary school students begin to reason about distributions? In K. Makar (Ed.), Reasoning about distribution: a collection of current research studies. Proceedings of the fourth international research forum on statistical reasoning, thinking, and literacy (SRTL-4), University of Auckland, New Zealand, 2–7 July. Brisbane: University of Queensland.

    Google Scholar 

  • Ben-Zvi, D., & Arcavi, A. (2001). Junior high school students’ construction of global views of data and data representations. Educational Studies in Mathematics, 45, 35–65.

    Article  Google Scholar 

  • Ben-Zvi, D., Aridor, K., Makar, K., & Bakker, A. (2012). Students’ emergent articulations of uncertainty while making informal statistical inferences. ZDM, 44(7), 913–925.

    Article  Google Scholar 

  • Cobb, G. W. (2007). The introductory statistics course: a Ptolemaic curriculum? Technology Innovations in Statistics Education, 1(1), 1–15.

    Google Scholar 

  • Common Core State Standards Initiative (2010). Common Core State Standards for mathematics. http://www.corestandards.org/Math. Accessed 30 Nov 2017.

  • Crouch, R. M., & Haines, C. R. (2004). Mathematical modeling: transitions between the real world and the mathematical model. International Journal of Mathematics Education in Science and Technology, 35(2), 197–206.

    Article  Google Scholar 

  • Doerr, H. M., DelMas, B., & Makar, K. (2017). A modeling approach to the development of students’ informal inferential reasoning. Statistics Education Research Journal, 16(2), 86–115.

    Google Scholar 

  • English, L. D. (2010). Young children’s early modeling with data. Mathematics Education Research Journal, 22(2), 24–47.

    Article  Google Scholar 

  • English, L. D. (2012). Data modeling with first-grade students. Educational Studies in Mathematics, 81(1), 15–30.

    Article  Google Scholar 

  • English, L. D. (2013). Modeling with complex data in the primary school. In R. Lesh, P. L. Galbraith, C. R. Haines & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies: ICTMA 13 (pp. 287–299). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Garfield, J., & Ben-Zvi, D. (2007). How students learn statistics revisited: a current review of research on teaching and learning statistics. International Statistical Review, 75(3), 372–396.

    Article  Google Scholar 

  • Garfield, J., & Ben-Zvi, D. (2008). Developing students’ statistical reasoning: connecting research and teaching practice. Dordrecht: Springer.

    Google Scholar 

  • Graham, A. (2006). Developing thinking in statistics. London: Paul Chapman.

    Google Scholar 

  • Konold, C., Higgins, T., Russell, J., & Khalil, K. (2015). Data seen through different lenses. Educational Studies in Mathematics, 88(3), 305–325.

    Article  Google Scholar 

  • Konold, C., & Kazak, S. (2008). Reconnecting data and chance. Technology Innovations in Statistics Education, 2(1). http://escholarship.org/uc/item/38p7c94v.

  • Konold, C., & Miller, C. D. (2005). TinkerPlots: dynamic data exploration. Emeryville: Key Curriculum Press.

    Google Scholar 

  • Konold, C., & Pollatsek, A. (2002). Data analysis as the search for signals in noisy processes. Journal for Research in Mathematics Education, 33(4), 259–289.

    Article  Google Scholar 

  • Larson, C., Harel, G., Oehrtman, M., Zandieh, M., Rasmussen, C., Speiser, R., & Walter, C. (2013). Modeling perspectives in math education research. In R. Lesh, P. L. Galbraith, C. R. Haines & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies: ICTMA 13 (pp. 61–71). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Lehrer, R., & English, L. (2018). Introducing children to modeling variability. In D. Ben-Zvi, K. Makar & J. Garfield (Eds.), The international handbook of research in statistics education (pp. 229–260). Switzerland: Springer International.

    Chapter  Google Scholar 

  • Lehrer, R., & Schauble, L. (2010). What kind of explanation is a model? In M. Stein & L. Kucan (Eds.), Instructional explanations in the disciplines (pp. 9–22). Boston: Springer.

    Chapter  Google Scholar 

  • Lesh, R., & Harel, G. (2003). Problem solving, modeling, and local conceptual development. Mathematical Thinking and Learning, 5(2), 157–189.

    Article  Google Scholar 

  • Makar, K., Bakker, A., & Ben-Zvi, D. (2015). Scaffolding norms of argumentation-based inquiry in a primary mathematics classroom. ZDM, 47(7), 1107–1120.

    Article  Google Scholar 

  • Makar, K., & Rubin, A. (2018). Learning about statistical inference. In D. Ben-Zvi, K. Makar & J. Garfield (Eds.), International handbook of research in statistics education (pp. 261–294). Switzerland: Springer International.

    Chapter  Google Scholar 

  • McPhee, D., & Makar, K. (2014). Exposing young children to activities that develop emergent inferential practices in statistics. In K. Makar, B. de Sousa, & R. Gould (Eds.), International Conference on Teaching Statistics (ICOTS9), Flagstaff, Arizona, USA. Voorburg: International Statistical Institute.

    Google Scholar 

  • Mokros, J., & Russell, S. J. (1995). Children’s concepts of average and representativeness. Journal for Research in Mathematics Education, 26(1), 20–39.

    Article  Google Scholar 

  • Noll, J., & Kirin, D. (2017). TinkerPlots model construction approaches for comparing two groups: student perspectives. Statistics Education Research Journal, 16(2), 213–243.

    Google Scholar 

  • Pfannkuch, M., & Reading, C. (2006). Reasoning about distribution: a complex process. Statistics Education Research Journal, 5(2), 4–9.

    Google Scholar 

  • Powell, A. B., Francisco, J. M., & Maher, C. A. (2003). An analytical model for studying the development of learners’ mathematical ideas and reasoning using videotape data. Journal of Mathematical Behavior, 22(4), 405–435.

    Article  Google Scholar 

  • Pratt, D. (2011). Re-connecting probability and reasoning about data in secondary school teaching. In Proceedings of the 58th World Statistics Conference, Dublin. http://2011.isiproceedings.org/papers/450478.pdf. Accessed on 21 June 2018.

  • Rubin, A., Hammerman, J. K. L., & Konold, C. (2006). Exploring informal inference with interactive visualization software. In Proceedings of the Seventh International Conference on Teaching Statistics, Salvador, Brazil. Voorburg, The Netherlands: International Statistical Institute.

    Google Scholar 

  • Shaughnessy, J. M. (2007). Research on statistics learning and reasoning. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 957–1010). Charlotte: Information Age.

    Google Scholar 

  • Stake, R. E. (2006). Multiple case study analysis. New York: Guilford.

    Google Scholar 

  • Watson, J., & Moritz, J. (2000). The longitudinal development of understanding of average. Mathematical Thinking and Learning, 2(1–2), 11–50.

    Article  Google Scholar 

  • Watson, J. M. (2006). Statistical literacy at school: growth and goals. New Jersey: Lawrence Erlbaum Associates.

    Google Scholar 

  • Wild, C. J. (2006). The concept of distribution. Statistics Education Research Journal, 5(2), 10–26.

    Google Scholar 

  • Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–265.

    Article  Google Scholar 

  • Yin, R. (2014). Case study research: design and methods (5th edn.). Beverly Hills: Sage.

    Google Scholar 

Download references

Acknowledgements

THIS work was supported by funding from the Australian Research Council under DP170101993. The author wishes to gratefully acknowledge the contributions of the teacher and students engaged in this research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jill Fielding-Wells.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fielding-Wells, J. Dot plots and hat plots: supporting young students emerging understandings of distribution, center and variability through modeling. ZDM Mathematics Education 50, 1125–1138 (2018). https://doi.org/10.1007/s11858-018-0961-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11858-018-0961-1

Keywords

Navigation