Skip to main content
Log in

Getting a Grip on Variability

  • Special Issue: Mathematical Biology Education
  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript

Abstract

Because science is a modeling enterprise, a key question for educators is: What kind of repertoire can initiate students into the practice of generating, revising, and critiquing models of the natural world? Based on our 20 years of work with teachers and students, we nominate variability as a set of connected key ideas that bridge mathematics and science and are fundamental for equipping youngsters for the posing and pursuit of questions about science. Accordingly, we describe a sequence for helping young students begin to reason productively about variability. Students first participate in random processes, such as repeated measure of a person’s outstretched arms, that generate variable outcomes. Importantly, these processes have readily discernable sources of variability, so that relations between alterations in processes and changes in the collection of outcomes can be easily established and interpreted by young students. Following these initial steps, students invent and critique ways of visualizing and measuring distributions of the outcomes of these processes. Visualization and measure of variability are then employed as conceptual supports for modeling chance variation in components of the processes. Ultimately, students reimagine samples and inference in ways that support reasoning about variability in natural systems.

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.

Institutional subscriptions

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

Similar content being viewed by others

References

  • Bakker A (2018) Design research in education. Routledge, New York

    Book  Google Scholar 

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

    Article  Google Scholar 

  • Ford MJ (2015) Educational implications of choosing “practice” to describe science in the next generation science standards. Sci Educ 99(6):1041–1048. https://doi.org/10.1002/sce.21188

    Article  Google Scholar 

  • Ford MJ, Forman EA (2006) Redefining disciplinary learning in classroom contexts. Rev Res Educ 30:1–32

    Article  Google Scholar 

  • Hestenes D (1982) Modeling games in the Newtonian world. Am J Phys I 60(8):732–748

    Article  Google Scholar 

  • Horvath JK, Lehrer R (1998) A model-based perspective on the development of children’s understanding of chance and uncertainty. In: Lajoie SP (ed) Reflections on statistics: learning, teaching, and assessment in grades K-12. Routledge, New York, pp 121–148

    Google Scholar 

  • Jones RS, Lehrer R, Kim M-J (2017) Critiquing statistics in student and professional worlds. Cogn Instr 35(4):317–336. https://doi.org/10.1080/07370008.2017.1358720

    Article  Google Scholar 

  • Konold C (1989) Informal conceptions of probability. Cogn Instr 6(1):59–98. https://doi.org/10.1207/s1532690xci0601_3

    Article  Google Scholar 

  • Konold C (2007) Designing a data tool for learners. In: Lovett MC, Shah P (eds) Thinking with data. Lawrence Erlbaum Associates, Taylor and Francis, New York, pp 267–292

    Google Scholar 

  • Konold C, Harradine A (2014) Contexts for highlighting signal and noise. In: Wassong T, Frischemeier D, Fischer PR, Hochmuth R, Bender P (eds) Mit Werkzeugen Mathematik und Stochastik lernen: using tools for learning mathematics and statistics. Springer, Wiesbaden, pp 237–250

    Chapter  Google Scholar 

  • Konold C, Lehrer R (2008) Technology and mathematics education: an essay in honor of Jim Kaput. In: English LD (ed) Handbook of international research in mathematics education, 2nd edn. Taylor & Francis, Philadelphia, pp 49–72

    Google Scholar 

  • Konold C, Miller CD (2011) TinkerPlots. Dynamic data exploration. Key Curriculum Press, Emeryville

    Google Scholar 

  • Konold C, Higgins T, Russell SJ, Khalil K (2015) Data seen through different lenses. Educ Stud Math 88(3):305–325. https://doi.org/10.1007/s10649-013-9529-8

    Article  Google Scholar 

  • Lehrer R (2017) Modeling signal-noise processes supports student construction of a hierarchical image of sample. Stat Educ Res J 16(2):64–85

    Google Scholar 

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

    Chapter  Google Scholar 

  • Lehrer R, Kim MJ (2009) Structuring variability by negotiating its measure. Math Educ Res J 21(2):116–133

    Article  Google Scholar 

  • Lehrer R, Romberg T (1996) Exploring children’s data modeling. Cogn Instr 14(1):69–108. https://doi.org/10.1207/s1532690xci1401_3

    Article  Google Scholar 

  • Lehrer R, Schauble L (2007) Contrasting emerging conceptions of distribution in contexts of error and natural variation. In: Lovett MC, Shah P (eds) Thinking with data. Lawrence Erlbaum Associates, Taylor and Francis, New York, pp 149–176

    Google Scholar 

  • Lehrer R, Schauble L (2017) Children’s conceptions of sampling in local ecosystems investigations. Sci Educ 101(6):968–984. https://doi.org/10.1002/sce.2129

    Article  Google Scholar 

  • Lehrer R, Schauble L (2019) Learning to play the modeling game. In: Upmeirer zu Belzen A, Kruger D, van Driel J (eds) Toward a competence-based view on models and modeling in science education. Springer, Cham, pp 221–236

    Chapter  Google Scholar 

  • Lehrer R, Kim M-J, Jones RS (2011) Developing conceptions of statistics by designing measures of distribution. ZDM Math Educ 43(5):723–736. https://doi.org/10.1007/s11858-011-0347-0

    Article  Google Scholar 

  • Lehrer R, Kim M-J, Ayers E, Wilson M (2014) Toward establishing a learning progression to support the development of statistical reasoning. In: Confrey J, Maloney AP, Nyuyen KH (eds) Learning over time: learning trajectories in mathematics education. Information Age Publishers, Charlotte, pp 31–60

    Google Scholar 

  • Makar K, Rubin A (2017) Learning about statistical inference. In: Ben-Zvi D, Makar K, Garfield J (eds) International handbook of research in statistics education. Springer International Publishing, Cham, pp 261–294

    Google Scholar 

  • Manor Braham H, Ben-Zvi D (2015) Students’ articulations of uncertainty in informally exploring sampling distribution. In: Zieffler A, Fry E (eds) Reasoning about uncertainty: learning and teaching informal inferential reasoning. Catalyst Press, Minneapolis, pp 57–94

    Google Scholar 

  • Metz KE (1998) Emergent understanding and attribution of randomness: comparative analysis of the reasoning of primary grade children and undergraduates. Cogn Instr 16(3):285–265. https://doi.org/10.1207/s1532690xci1603_3

    Article  Google Scholar 

  • National Research Council (2012) A framework for K-12 science education: practices, crosscutting concepts, and core ideas. The National Academy of the Sciences, Washington, DC

    Google Scholar 

  • Nersessian NJ (2008) Creating scientific concepts. The MIT Press, Cambridge

    Book  Google Scholar 

  • Petrosino AJ, Lehrer R, Schauble L (2003) Structuring error and experimental variation as distribution in the fourth grade. Math Think Learn 5(2–3):131–156. https://doi.org/10.1080/10986065.2003.9679997

    Article  Google Scholar 

  • Pfannkuch M, Wild CJ (2000) Statistical thinking and statistical practice: themes gleaned from professional statisticians. Stat Sci 15(2):132–152. https://doi.org/10.2307/2676728

    Article  Google Scholar 

  • Saldanha LA, Thompson PW (2002) Conceptions of sample and their relationship to statistical inference. Educ Stud Math 51(3):257–270

    Article  Google Scholar 

  • Saldanha LA, Thompson PW (2014) Conceptual issues in understanding the inner logic of statistical inference: insights from two teaching experiments. J Math Behav 35:1–30

    Article  Google Scholar 

  • Shinohara M, Lehrer R (2017, July) Narrating lines of practice: students’ views of their participation in statistical practice. Paper presented at the tenth international research forum on statistical reasoning, thinking, and literacy (SRTL 10). Rotorua, New Zealand

  • Tapee M, Cartmell T, Guthrie T, Kent LB (2019) Stop the silence. How to create a strategically social classroom. Math Teach Middle Sch 24(4):210–216

    Article  Google Scholar 

  • Thompson PW, Liu Y, Saldanha L (2007) Intricacies of statistical inference and teachers’ understanding of them. In: Lovett MC, Shah P (eds) Thinking with data. Lawrence Erlbaum Associates, Taylor and Francis, New York, pp 207–231

    Google Scholar 

  • Windschitl M, Thompson J, Braaten M (2008) Beyond the scientific method: model-based inquiry as a new paradigm of preference for school science investigations. Science Education 92(5):941–967. https://doi.org/10.1002/sce.20259

    Article  Google Scholar 

  • Wisittanawat P, Lehrer R (2018) Teacher assistance in modeling chance processes. In: Sorto MA (ed) Looking back, looking forward. (Proceedings of the 10th international conference on the teaching of statistics, Kyoto, Japan, July). Voorburg, The Netherlands: International Statistical Institute. Accessed on 26 Feb 2020 from https://iase-web.org/icots/10/proceedings/pdfs/ICOTS10_2A2.pdf

Download references

Acknowledgements

Funding was provided by Australian Research Council (Grant No. DP180102333) and Institute of Education Sciences (Grant No. R305A110685).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Richard Lehrer.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lehrer, R., Schauble, L. & Wisittanawat, P. Getting a Grip on Variability. Bull Math Biol 82, 106 (2020). https://doi.org/10.1007/s11538-020-00782-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11538-020-00782-3

Keywords

Navigation