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Developing conceptions of statistics by designing measures of distribution

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Abstract

Students often learn procedures for measuring, but rarely do they grapple with the foundational conceptual problem of generating and validating coordination between a measure and the phenomenon being measured. Coordinating measures with phenomenon involves developing an appreciation of the objects and relations in each as well as establishing their mutual correspondence. We supported students’ developing conceptions of statistics by positioning them to design measures of center and of variability for distributions that they had generated through repeated measure of a length. After students invented and explored the viability of their measures individually, they participated in a public (whole-class conversation) forum featuring justification and reflection about the viability of their designed measures. We illustrate how individual invention enticed students to attend to, and to make explicit, characteristics of distribution not initially noticed or known only tacitly. Conceptions of statistics and of relevant characteristics of distribution were further expanded as students justified and argued about the utility and prospective generalization of particular inventions. Teachers supported student learning by highlighting prospective relations between characteristics of measures and characteristics of distribution as they emerged during the course of activity in each setting.

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References

  • Bakker, A., Wijers, M., Joinker, V., & Akkerman, S. (2011). The use, nature and purposes of measurement in intermediate-level occupations. ZDM.

  • Berlinski, D. (2000). The advent of the algorithm. New York: Harcourt.

  • Chang, H. (2004). Inventing temperature. Measurement and scientific progress. Oxford: Oxford University Press.

  • Clements, D. H., & Bright, G. (2003). Learning and teaching measurement. 2003 Yearbook. Washington, D.C.: National Council of Teachers of Mathematics.

    Google Scholar 

  • Cobb, P. (1999). Individual and collective mathematical learning: The case of statistical data analysis. Mathematical Thinking and Learning, 1, 5–44.

    Article  Google Scholar 

  • Cobb, G. W., & Moore, D. S. (1997). Mathematics, statistics, and teaching. American Mathematical Monthly, 104(9), 801–823.

    Article  Google Scholar 

  • Crosby, A. (1997). The measure of reality: Quantification and western society 1250–1600. New York: Cambridge University Press.

    Google Scholar 

  • diSessa, A. (2004). Metarepresentation. Native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.

    Google Scholar 

  • Ford, M. J. (2010). Critique in academic disciplines and active learning of academic content. Cambridge Journal of Education, 40(3), 265–280.

    Article  Google Scholar 

  • Gooding, D. (1990). Experiment and the making of meaning. Human agency in scientific observation and experiment. Dordrecht: Kluwer Academic Publishers.

  • Holland, D., Lachicotte, W., Jr., Skinner, D., & Cain, C. (1998). Agency and identity in cultural worlds. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Jefferson, G. (1984). Transcription notation. In J. Maxwell & J. Heritage (Eds.), Structures of social action (pp. ix–xvi). New York: Cambridge University Press.

  • Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.

  • Konold, C. (2007). Designing a data analysis tool for learners. In M. C. Lovett & P. Shah (Eds.), Thinking with data (pp. 267–291). New York: Lawrence Erlbaum Associates.

    Google Scholar 

  • Konold, C., & Lehrer, R. (2008). Technology and mathematics education. In L. D. English (Ed.), Handbook of international research in mathematics education (pp. 49–69). New York: Routledge.

    Google Scholar 

  • Konold, C., & Miller, C. D. (2005). TinkerPlots: Dynamic data exploration [Computer software]. Emeryville, CA: Key Curriculum Press.

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

    Article  Google Scholar 

  • Lee, K., & Smith III, J. P. (2011). What’s different across an ocean? How Singapore and U.S. elementary mathematics curricula introduce and develop length measurement (in press).

  • Lehrer, R., Jacobson, C., Thoyre, G., Kemeny, V., Strom, D., Horvath, J., et al. (1998). Developing understanding of geometry and space. In R. Lehrer & D. Chazan (Eds.), Designing learning environments for developing understanding of geometry and space (pp. 169–200). Lawrence Erlbaum Associates: Mahwah, NJ.

    Google Scholar 

  • Lehrer, R., & Kim, M.-J. (2009). Structuring variability by negotiating its measure. Mathematics Education Research Journal, 21(2), 116–133.

    Article  Google Scholar 

  • Lehrer, R., Kim, M.-J., & Schauble, L. (2007). Supporting the development of conceptions of statistics by engaging students in measuring and modeling variability. International Journal of Computers for Mathematical Learning, 12(3), 195–216.

    Article  Google Scholar 

  • Lehrer, R., & Romberg, T. (1996). Exploring children’s data modeling. Cognition & Instruction, 14(1), 69–108.

    Article  Google Scholar 

  • Lehrer, R., & Schauble, L. (2004). Modeling natural variation through distribution. American Educational Research Journal, 41(3), 635–679.

    Article  Google Scholar 

  • Petrosino, A. J., Lehrer, R., & Schauble, L. (2003). Structuring error and experimental variation as distribution in the fourth grade. Mathematical Thinking and Learning, 5(2&3), 131–156.

    Google Scholar 

  • Rotman, B. (2000). Mathematics as sign. Stanford: Stanford University Press.

    Google Scholar 

  • Thompson, P. W., Liu, Y., & Saldanha, L. A. (2007). Intricacies of statistical inference and teachers’ understanding of them. In M. C. Lovett & P. Shah (Eds.), Thinking with data (pp. 207–231). New York: Lawrence Erlbaum.

    Google Scholar 

  • Van Fraassen, B. C. (2008). Scientific representation: Paradoxes of perspective. Oxford: Oxford University Press.

    Google Scholar 

  • Zawojewski, J. S., & Shaughnessy, J. M. (2000). Mean and median: Are they really so easy? Mathematics Teaching in the Middle School, 5, 436–440.

    Google Scholar 

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Acknowledgments

The research reported here was supported by the U.S. National Science Foundation, REC-0337675 and by the Institute of Education Sciences, U.S. Department of Education, through Grant R305K060091 to Vanderbilt University.

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Correspondence to Richard Lehrer.

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The opinions expressed are those of the authors and do not represent views of the U.S. Department of Education or of the U.S. National Science Foundation.

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

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