Skip to main content
Log in

Statistical modelling and repeatable structures: purpose, process and prediction

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

Abstract

Children have limited exposure to statistical concepts and processes, yet researchers have highlighted multiple benefits of experiences in which they design and/or engage informally with statistical modelling. A study was conducted with a classroom in which students developed and utilised data-based models to respond to the inquiry question, Which origami animal jumps the furthest? The students used hat plots and box plots in Tinkerplots to make sense of variability in comparing distributions of their data and to support them to write justified conclusions of their findings. The study relied on classroom video and student artefacts to analyse aspects of the students’ modelling experiences which exposed them to powerful statistical ideas, such as key repeatable structures and dispositions in statistics. Three principles—purpose, process and prediction—are highlighted as ways in which the problem context, statistical structures and inquiry dispositions and cycle extended students’ opportunities to reason in sophisticated ways appropriate for their age. The research question under investigation was, How can an emphasis on purpose, process and prediction be implemented to support children’s statistical modelling? The principles illustrated in the study may provide a simple framework for teachers and researchers to develop statistical modelling practices and norms at the school level.

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

Similar content being viewed by others

References

  • Ainley, J., Pratt, D., & Hansen, A. (2006). Connecting engagement and focus in pedagogic task design. British Educational Research Journal, 32(1), 23–38. https://doi.org/10.1080/01411920500401971.

    Article  Google Scholar 

  • Allmond, S., & Makar, K. (2014). From hat plots to box plots in Tinkerplots: Supporting students to write conclusions which account for variability in data. In K. Makar, B. de Sousa & R. Gould (Eds.), Proceedings of the 9th International Conference on Teaching Statistics (pp. 1–6). International Association for Statistical Education.

  • Bakker, A., & Derry, J. (2011). Lessons from inferentialism for statistics education. Mathematical Thinking and Learning, 13(1–2), 5–26.

    Article  Google Scholar 

  • Barbosa, J. C. (2006). Mathematical modelling in classroom: A socio-critical and discursive perspective. ZDM, 38(3), 293–301. https://doi.org/10.1007/BF02652812.

    Article  Google Scholar 

  • Biehler, R., Frischemeier, D., & Podworny, S. (2017). Editorial: Reasoning about models and modelling in the context of informal statistical inference. Statistics Education Research Journal, 16(2), 8–12.

    Google Scholar 

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

    Google Scholar 

  • Doerr, H. M., & English, L. D. (2003). A modeling perspective on students’ mathematical reasoning about data. Journal for Research in Mathematics Education, 34(2), 110–136. https://doi.org/10.2307/30034902.

    Article  Google Scholar 

  • English, L. D. (2012). Data modeling with first-grade students. Educational Studies in Mathematics, 81(1), 15–30. https://doi.org/10.1007/s10649-011-9377-3.

    Article  Google Scholar 

  • Fielding-Wells, J. (2014). Developing argumentation in mathematics: The role of evidence and context. Unpublished doctoral dissertation, The University of Queensland.

  • Fielding-Wells, J., & Makar, K. (2015). Inferring to a model: Using inquiry-based argumentation to challenge young children’s expectations of equally likely outcomes. In A. Zieffler & E. Fry (Eds.), Reasoning about uncertainty: Learning and teaching informal inferential reasoning (pp. 1–27). Minneapolis: Catalyst Press.

    Google Scholar 

  • Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219–245.

    Article  Google Scholar 

  • Font, V., Godino, J. D., & D’Amore, B. (2007). An onto-semiotic approach to representations in mathematics education. For the Learning of Mathematics, 27(2), 2–7, 14.

    Google Scholar 

  • Gravemeijer, K., & Doorman, M. (1999). Context problems in realistic mathematics education: A calculus course as an example. Educational Studies in Mathematics, 39(1–3), 111–129.

    Article  Google Scholar 

  • Hancock, C., Kaput, J. J., & Goldsmith, L. T. (1992). Authentic inquiry with data: Critical barriers to classroom implementation. Educational Psychologist, 27(3), 337–364.

    Article  Google Scholar 

  • Hestenes, D. (2010). Modeling theory for math and science education. In R. Lesh, P. L. Galbraith, C. R. Haines & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies: ICTMA 13 (pp. 13–41). New York: Springer.

    Chapter  Google Scholar 

  • Konold, C. (2002). Hat plots? Unpublished manuscript. University of Massachusetts, Amherst.

    Google Scholar 

  • Konold, C., Finzer, W., & Kreetong, K. (2017). Modeling as a core component of structuring data. Statistics Education Research Journal, 16(2), 191–212.

    Google Scholar 

  • Lampert, M. (1990). When the problem is not the question and the solution is not the answer: Mathematical knowing and teaching. American Educational Research Journal, 27(1), 29–63.

    Article  Google Scholar 

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

    Chapter  Google Scholar 

  • Lehrer, R., Jones, R. S., & Kim, M. J. (2014). Model-based informal inference. In K. Makar, B. de Sousa, & R. Gould (Eds.), Proceedings of the Ninth International Conference on Teaching Statistics (ICOTS9). Dordrecht, the Netherlands: ISI.

  • Lehrer, R., Kim, M. J., & Jones, R. S. (2011). Developing conceptions of statistics by designing measures of distribution. ZDM – The International Journal on Mathematics Education, 43(5), 723–736. https://doi.org/10.1007/s11858-011-0347-0.

    Article  Google Scholar 

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

    Chapter  Google Scholar 

  • Lesh, R. A., & Doerr, H. M. (Eds.). (2003). Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Makar, K. (2014). Young children’s explorations of average through informal inferential reasoning. Educational Studies in Mathematics, 86(1), 61–78. https://doi.org/10.1007/s10649-013-9526-y.

    Article  Google Scholar 

  • Makar, K., & Rubin, A. (2009). A framework for thinking about informal statistical inference. Statistics Education Research Journal, 8(1), 82–105. Retrieved from http://iase-web.org/documents/SERJ/SERJ8(1)_Makar_Rubin.pdf.

  • 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. 261–297). New York: Springer.

    Chapter  Google Scholar 

  • Peters, S. (2014). Developing understanding of statistical variation: Secondary statistics teachers’ perceptions and recollections of learning factors. Journal of Mathematics Teacher Education, 17, 539–582. https://doi.org/10.1007/s10857-013-9242-7.

    Article  Google Scholar 

  • Pfannkuch, M., Budgett, S., & Arnold, P. (2015). Experiment-to-causation inference: Understanding causality in a probabilistic setting. In A. Zieffler & E. Fry (Eds.), Reasoning about uncertainty: Learning and teaching informal inferential reasoning (pp. 95–127). Minneapolis, MN: Catalyst Press.

    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, 405–435. https://doi.org/10.1016/j.jmathb.2003.09.002.

    Article  Google Scholar 

  • Pratt, D., & Ainley, J. (2014). Chance Re-encounters: ‘Computers in Probability Education’ revisited. In T. Wassong et al. (Eds.), MitWerkzeugen Mathematik und Stochastik lernen—Using tools for learning mathematics and statistics. New York: Springer.

    Google Scholar 

  • Pratt, D., Johnston-Wilder, P., Ainley, J., & Mason, J. (2008). Local and global thinking in statistical inference. Statistics Education Research Journal, 7(2), 107–129. Retrieved from http://iase-web.org/documents/SERJ/SERJ7(2)_Pratt.pdf.

  • Shaughnessy, J. M. (2007). Research on statistics learning and reasoning. In Second handbook of research on mathematics teaching and learning (pp. 957–1010). Reston, VA: NCTM.

    Google Scholar 

  • Shaughnessy, J. M., & Pfannkuch, M. (2002). How faithful is Old Faithful? Statistical thinking: A story of variation and prediction. Mathematics Teacher, 95(4), 252–259.

    Google Scholar 

  • Wells, J., Makar, K., & Allmond, S. (2012). Evidence triangle (Poster). Brisbane: The University of Queensland. Retrieved from http://www.mathsinquiry.com.

  • Wild, C. (2017). Modelling: Connecting the worlds. Keynote presentation at the 10th International Forum for Research on Statistical Reasoning, Thinking and Literacy (SRTL10), 2–8 July 2017, Rotorua, New Zealand.

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

    Article  Google Scholar 

  • Zapata-Cardona, L. (this issue). Students’ construction and use of models: A socio-critical perspective. ZDM Mathematics Education.

Download references

Acknowledgements

We gratefully acknowledge funding for this work from the Australian Research Council (ARC DP120100690, ARC DP170101993) and our research assistants Janine and Ali who transcribed these data and assisted with preliminary analysis.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Katie Makar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Makar, K., Allmond, S. Statistical modelling and repeatable structures: purpose, process and prediction. ZDM Mathematics Education 50, 1139–1150 (2018). https://doi.org/10.1007/s11858-018-0956-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11858-018-0956-y

Keywords

Navigation