Reasoning about
Variability
in Comparing Distributions
Dani Ben-Zvi
University of Haifa, Faculty of Education
dbenzvi@univ.haifa.ac.il
SUMMARY
Variability stands in the heart of statistics theory and practice. Concepts and judgments involved in comparing groups have been found to be a productive vehicle for motivating learners to reason statistically and are critical for building the intuitive foundation for inferential reasoning. The focus in this paper is on the emergence of beginners’ reasoning about variation in a comparing distributions situation during their extended encounters with an Exploratory Data Analysis (EDA) curriculum in a technological environment. The current case study is offered as a contribution to understanding the process of constructing meanings and appreciation for variability within a distribution and between distributions and the mechanisms involved therein. It concentrates on the detailed qualitative analysis of the ways by which two seventh grade students started to develop views (and tools to support them) of variability in comparing groups using various statistical representations. Learning statistics is conceived as cognitive development and socialization processes into the culture and values of “doing statistics” (enculturation). In the light of the analysis, a description of what it may mean to begin reasoning about variability in comparing distributions of equal size is proposed, and implications are drawn.
Keywords: Variability;
Comparing distributions; Statistical reasoning; Exploratory data analysis;
Enculturation; Appropriation
__________________________
Statistics Education Research Journal,
3(2), 42-63, http://www.stat.auckland.ac.nz/serj
Ó International Association for Statistical Education (IASE/ISI), November, 2004
References
Bakker, A., & Gravemeijer, K. (2004). Learning to reason about distribution. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning, and thinking (pp. 147–168). Dordrecht, The Netherlands: Kluwer Academic Publishers.
Ben-Zvi, D. (2000). Toward understanding of the role of technological tools in statistical learning. Mathematical Thinking and Learning, 2(1&2), 127–155.
Ben-Zvi, D. (2002). Seventh grade students’ sense making of data and data representations. In B. Phillips (Ed.), Proceedings of the Sixth International Conference on Teaching of Statistics, Cape Town, South Africa. [CD-ROM] Voorburg, The Netherlands: International Statistical Institute.
Ben-Zvi, D. (2004). Reasoning about Data Analysis. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning, and thinking (pp. 121–146). Dordrecht, The Netherlands: Kluwer Academic Publishers.
Ben-Zvi, D., & Arcavi, A. (1998). Toward a characterization and understanding of students’ learning in an interactive statistics environment. In L. Pereira-Mendoza (Ed.), Proceedings of the Fifth International Conference on Teaching Statistics (Vol. 2, pp. 647–653). Voorburg, The Netherlands: International Statistical Institute.
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.
Ben-Zvi, D., & Friedlander, A. (1997a). Statistical investigations with spreadsheets—Student’s workbook (In Hebrew). Rehovot, Israel: Weizmann Institute of Science.
Ben-Zvi, D., & Friedlander, A. (1997b). Statistical thinking in a technological environment. In J. B. Garfield & G. Burrill (Eds.), Research on the role of technology in teaching and learning statistics (pp. 45–55). Voorburg, The Netherlands: International Statistical Institute.
Ben-Zvi, D., & Ozruso, G. (2001). Statistical investigations with spreadsheets—Teacher’s guide (In Hebrew). Rehovot, Israel: Weizmann Institute of Science.
Biehler, R. (1993). Software tools and mathematics education: The case of statistics. In C. Keitel & K. Ruthven (Eds.), Learning from computers: Mathematics education and technology (pp. 68–100). Berlin: Springer-Verlag.
Biehler, R. (1997). Software for learning and for doing statistics. International Statistical Review, 65(2), 167–189.
Biehler, R. (2001, August). Developing and assessing students’ reasoning in comparing statistical distributions in computer supported statistics courses. Paper presented at the Second International Research Forum on Statistical Reasoning, Thinking, and Literacy (SRTL-2), Armidale, Australia.
Bright, G. W. & Friel, S. N. (1998). Graphical representations: Helping students interpret data. In S. P. Lajoie (Ed.), Reflections on statistics: Learning, teaching, and assessment in grades K-12 (pp. 63–88). Mahwah, NJ: Lawrence Erlbaum.
Cobb, P. (1999). Individual and collective mathematical development: The case of statistical data analysis. Mathematical Thinking and Learning, 1(1), 5–43.
Gal, I., & Garfield, J. B. (Eds.). (1997). The assessment challenge in statistics education. Amsterdam, Netherlands: IOS Press.
Gal, I., Rothschild, K., & Wagner, D. A. (1990, April). Statistical concepts and statistical reasoning in school children: Convergence or divergence? Paper presented at the annual meeting of the American Educational Research Association, Boston.
Garfield, J. (1995). How students learn statistics. International Statistical Review 63(1), 25–34.
Hancock, C., Kaput, J. J., & Goldsmith, L. T. (1992). Authentic inquiry with data: Critical barriers to classroom implementation. Educational Psychologist, 27(3), 337–364.
Hershkowitz, R. (1999). Where in shared knowledge is the individual knowledge hidden? In O. Zaslavsky (Ed.) Proceedings of the 23rd Conference of the International Group for the Psychology of Mathematics Education, I, (pp. 9–24). Haifa, Israel: The Technion.
Hershkowitz, R., Dreyfus, T., Schwarz, B., Ben-Zvi, D., Friedlander, A., Hadas, N., Resnick, T., & Tabach, M. (2002). Mathematics curriculum development for computerized environments: A designer-researcher-teacher-learner activity. In L. D. English (Ed.), Handbook of international research in mathematics education (pp. 657–694). London: Erlbaum.
Hunt, D. N. (1995). Teaching statistical concepts using spreadsheets. In the Proceedings of the 1995 Conference of the Association of Statistics Lecturers in Universities. Nottingham, UK: The Teaching Statistics Trust. [Online: http://www.mis.coventry.ac.uk/~nhunt/aslu.htm]
Konold, C (2002). Teaching concepts rather than conventions. New England Journal of Mathematics, 34(2), 69–81.
Konold, C., & Higgins, T. (2003). Reasoning about data. In J. Kilpatrick, W. G. Martin & D. E. Schifter (Eds.), A research companion to principles and standards for school mathematics, (pp. 193–215). Reston, VA: National Council of Teachers of Mathematics.
Konold, C., Pollatsek, A., Well, A., & Gagnon, A. (1997). Students analyzing data: Research of critical barriers. In J. B. Garfield & G. Burrill (Eds.), Research on the role of technology in teaching and learning statistics, (pp. 151–167). Voorburg, The Netherlands: International Statistical Institute.
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, 29–63.
Magidson, S. (1992, April). From the laboratory to the classroom: A technology-intensive curriculum for functions and graphs. Paper presented at the Annual Meeting of the American Educational Research Association, San Francisco, CA.
Makar, K., & Confrey, J. (2004). Secondary teachers’ statistical reasoning in comparing two groups. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning, and thinking (pp. 353–374). Dordrecht, The Netherlands: Kluwer Academic Publishers.
Meira, L. R. (1991). Explorations of mathematical sense-making: An activity-oriented view of children’s use and design of material displays. An unpublished Ph.D. dissertation, Berkeley, CA: University of California.
Meletiou, M. (2002). Conceptions of variation: A literature review. Statistics Education Research Journal, 1(1), 46–52.
Meletiou, M., & Lee, C. (2002). Student understanding of histograms: A stumbling stone to the development of intuitions about variation. In B. Phillips (Ed.), Proceedings of the Sixth International Conference on Teaching Statistics, Cape Town, South Africa. [CDROM] Voorburg, The Netherlands: International Statistical Institute.
Mokros, J., & Russell, S. J. (1995). Children’s Concepts of Average and Representativeness. Journal for Research in Mathematics Education, 26(1), 20–39.
Moschkovich, J. D. (1989). Constructing a problem space through appropriation: A case study of guided computer exploration of linear functions. An unpublished manuscript available from the author.
Moschkovich, J. D., Schoenfeld, A. H., & Arcavi, A. A. (1993). Aspects of understanding: On multiple perspectives and representations of linear relations, and connections among them. In T. Romberg, E. Fennema & T. Carpenter (Eds.), Integrating Research on the Graphical Representation of Function, (pp. 69–100). Hillsdale, NJ: Lawrence Erlbaum Associates.
Pfannkuch, M., & Wild, C. (2004). Towards an understanding of statistical thinking. In D. Ben-Zvi, & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning, and thinking (pp. 17–46). Dordrecht, The Netherlands: Kluwer Academic Publishers.
Reading, C., & Shaughnessy, 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 Publishers.
Resnick, L. (1988). Treating mathematics as an ill-structured discipline. In R. Charles & E. Silver (Eds.), The teaching and assessing of mathematical problem solving (pp. 32–60). Reston, VA: National Council of Teachers of Mathematics.
Resnick, T., & Tabach, M. (1999). Touring the land of Oz - algebra with computers for Grade Seven (in Hebrew). Rehovot, Israel: Weizmann Institute of Science.
Schoenfeld, A. H. (1992). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics. In D. Grouws (Ed.), Handbook of research on mathematics teaching and learning (pp. 334–370). New York: Macmillan.
Schoenfeld, A. H. (1994). Some notes on the enterprise (research in collegiate mathematics education, that is). Conference Board of the Mathematical Sciences Issues in Mathematics Education, 4, 1–19.
Shaughnessy, J. M., & Ciancetta, M. (2002). Students’ understanding of variability in a probability environment. In B. Phillips (Ed.), Proceedings of the Sixth International Conference on Teaching Statistics, Cape Town, South Africa. [CDROM] Voorburg, The Netherlands: International Statistical Institute.
Shaughnessy, J. M., Garfield, J., & Greer, B. (1996). Data handling. In A. J. Bishop, K. Clements, C. Keitel, J. Kilpatrick, & C. Laborde (Eds.), International handbook of mathematics education (Vol. I, pp. 205–237). Dordrecht, The Netherlands: Kluwer Academic Publishers.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes, (Edited by M. Cole, V. John-Steiner, S. Scribner, & E. Souberman). Cambridge, MA: Harvard University Press.
Voigt, J. (1995). Thematic patterns of interaction and sociomathematical norms. In P. Cobb & H. Bauersfeld (Eds.), Emergence of mathematical meaning: Interaction in classroom cultures, (pp. 163–201). Hillsdale, NJ: Erlbaum.
Watson, J. M. (2001). Longitudinal development of inferential reasoning by school students. Educational Studies in Mathematics, 47, 337–372.
Watson, J. M., & Kelly, B. A. (2002). Can grade 3 students learn about variation? In B. Phillips (Ed.), Proceedings of the Sixth International Conference on Teaching Statistics, Cape Town, South Africa. [CDROM] Voorburg, The Netherlands: International Statistical Institute.
Watson, J. M., Kelly, B. A., Callingham, R. A., & Shaughnessy, J. M. (2003). The measurement of school students’ understanding of statistical variation. International Journal of Mathematical Education in Science and Technology, 34(1), 1–29.
Watson, J. M., & Moritz, J. B. (1999). The beginning of statistical inference: Comparing two data sets. Educational Studies in Mathematics, 37(2), 145–168.
Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–265.
Yackel, E., & Cobb, P. (1996). Socio-mathematical norms, argumentation and autonomy in mathematics. Journal for Research in Mathematics Education, 27(4), 458–477.
Software
Excel, Microsoft Corporation, http://www.microsoft.com/office/excel/.
Fathom (Fathom Dynamic Statistics Software), B. Finzer, Key Curriculum Press, 1150 65th Street, Emeryville, CA 94608, USA. http://www.keypress.com/fathom/.
Mini-Tools, Peabody College, Vanderbilt University, principal investigator: P. Cobb, http://peabody.vanderbilt.edu/depts/tandl/mted/Proj6_CMT/6MiniTools.html.
Tinkerplots, the Statistics Education Research Group at the University of Massachusetts, Amherst, principal investigator: C. Konold, http://www.umass.edu/srri/serg/projects/tp/tpmain.html.
DANI Ben-zvi
Faculty of Education
University of Haifa
Mount Carmel
Haifa 31905
Israel