THE INFLUENCE OF VARIATION AND EXPECTATION ON
THE Developing Awareness of Distribution
Jane M. Watson
University of Tasmania
Jane.Watson@utas.edu.au
ABSTRACT
This study considers the evolving influence of variation and expectation on the development of school students’ appreciation of distribution as displayed in their construction of graphical representations of data sets. Three interview protocols are employed, presenting different contexts within which 109 students, ranging in age from 6 to 15 years, could display and interpret their understanding. Responses are analyzed within a hierarchical cognitive framework. It is hypothesized from the analysis that, contrary to the order in which expectation and variation are introduced in the school curriculum, the natural tendency for students is to acknowledge variation first and then expectation.
Keywords: Statistics
education research; Interviews; School students; Graphs
__________________________
Statistics Education Research
Journal, 8(1), 32-61, http://www.stat.auckland.ac.nz/serj
Ó International Association for Statistical Education (IASE/ISI), May, 2009
REFERENCES
Bakker, A.,
& Gravemeijer, K. P. E. (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: Kluwer Academic Publishers.
Ben-Zvi, D.,
& Amir, Y. (2005). How do primary students begin to reason about
distributions? In K. Makar (Ed.), Reasoning
about Distribution: A collection of studies. Proceedings of the Fourth International Research Forum on Statistical
Reasoning, Thinking and Literacy (SRTL-4). [CDROM, with video segments].
Brisbane, Australia: University of Queensland.
Biggs, J. B.
(1992). Modes of learning, forms of knowing, and ways of schooling. In A.
Demetriou, M. Shayer, & A. Efklides (Eds.), Neo-Piagetian theories of cognitive development: Implications and
applications for education (pp. 31-51). London: Routledge.
Biggs, J. B.,
& Collis, K. F. (1982). Evaluating
the quality of learning: The SOLO taxonomy. New York: Academic Press.
Burns, R. B.
(2000). Introduction to research methods
(4th ed.). London: Sage.
Chick, H. (2004).
Simple strategies for dealing with data. Australian
Mathematics Teacher, 60(3),
20-24.
Chick, H. L.,
& Watson, J. M. (2001). Data representation and interpretation by primary
school students working in groups. Mathematics
Education Research Journal, 13,
91-111.
Curcio, F. R.
(1987). Comprehension of mathematical relationships expressed in graphs. Journal for Research in Mathematics
Education, 18, 382-393.
Curcio, F. R.,
& Artzt, A. F. (1996). Assessing students’ ability to analyze data:
Reaching beyond computation. Mathematics
Teacher, 89(8), 668-673.
delMas, R. C.,
& Liu, Y. (2003). Exploring students’ understanding of statistical
variation. In C. Lee (Ed.), Reasoning
about variability: Proceedings of the Third International Research Forum on
Statistical Reasoning, Thinking, and Literacy [CD-ROM]. Mt. Pleasant, MI:
Central Michigan University.
Friel, S. N.,
Curcio, F. R., & Bright, G. W. (2001). Making sense of graphs: Critical
factors influencing comprehension and instructional implications. Journal for Research in Mathematics
Education, 32(2), 124-158.
Friel, S. N.,
O’Connor, W., & Mamer, J. D. (2006). More than “Meanmedianmode” and a bar
graph: What’s needed to have a statistical conversation. In G. F. Burrill
(Ed.), Thinking and reasoning with data
and chance (pp. 117-137). Reston, VA: National Council of Teachers of
Mathematics.
Green, D.
(1993). Data analysis: What research do we need? In L. Pereira-Mendoza (Ed.), Introducing data analysis in the schools:
Who should teach it? (pp. 219-239). Voorburg, The Netherlands:
International Statistical Institute.
Kelly, B. A.,
& Watson, J. M. (2002). Variation in a chance sampling setting: The lollies
task. In B. Barton, K. C. Irwin, M. Pfannkuch, & M. O. J. Thomas
(Eds.), Mathematics education in the
South Pacific: Proceedings of the twenty-sixth annual conference of the
Mathematics Education Research Group of Australasia (Vol. 2, pp. 366-373).
Sydney, NSW: MERGA.
Kerslake, D.
(1981). Graphs. In K. M. Hart (Ed.), Children’s
understanding of mathematics: 11-16 (pp. 120-136). London: John Murray.
Kirkpatrick, E.
M. (Ed.). (1983). Chambers 20th
century dictionary. Edinburgh: W & R Chambers.
Konold, C.,
& Higgins, T. L. (2003). Reasoning about data. In J. Kilpatrick, W.
G. Martin, & D. 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.,
Higgins, T. L., Russell, S. J., & Khalil, K. (2003). Data seen through different lenses. Amherst, MA: University of
Massachusetts.
[Online: www.umass.edu/srri/serg/papers.html]
Langrall, C.,
Nisbet, S., & Mooney, E. (2006). The interplay between students’
statistical knowledge and context knowledge in analyzing data. In A. Rossman
& B. Chance (Eds.), Working Cooperatively
in Statistics Education. Proceedings of the Seventh International Conference on
Teaching Statistics,
[Online: http://www.stat.auckland.ac.nz/~iase/publications/17/2A3_LANG.pdf]
Lehrer, R.,
& Romberg, T. (1996). Exploring children’s data modeling. Cognition and Instruction, 14(1), 69-108.
Leinhardt, G.,
Zaslavsky, O., & Stein, M. K. (1990). Functions, graphs and graphing:
Tasks, learning and teaching. Review of
Educational Research, 60(1),
1-64.
Mevarech, Z.
R., & Kramarsky, B. (1997). From verbal descriptions to graphic
representations: Stability and change in students’ alternative conceptions. Educational Studies in Mathematics, 32, 229-263.
Miles, M. B.,
& Huberman, A. M. (1994). Qualitative
data analysis: An expanded sourcebook (2nd ed.). Thousand Oaks, CA: Sage.
Mooney, E.,
Langrall, C., & Nisbet, S. (2006). Developing a model to describe the use
of context knowledge in data explorations. In A. Rossman & B. Chance
(Eds.), Working Cooperatively in
Statistics Education. Proceedings of the Seventh International Conference on
Teaching Statistics,
[Online: http://www.stat.auckland.ac.nz/~iase/publications/17/2A4_MOON.pdf]
Moore, D. S.
(1990). Uncertainty. In L. S. Steen (Ed.), On
the shoulders of giants: New approaches to numeracy (pp. 95-137).
Washington, DC: National Academy Press.
Moore, D. S.
(1997). New pedagogy and new content: The case of statistics. International Statistical Review, 65(2), 123-165.
Moore, D. S.,
& McCabe, G. P. (1993). Introduction
to the practice of statistics (2nd ed.). New York: W. H. Freeman.
Moritz, J. B.
(2000). Graphical representations of statistical associations by upper primary
students. In J. Bana & A. Chapman (Eds.), Mathematics education beyond 2000: Proceedings of the twenty-third annual conference of the Mathematics
Education Research Group of Australasia (Vol. 2, pp. 440-447). Sydney:
MERGA.
Moritz, J. B.
(2002). Study times and test scores: What students’ graphs show. Australian Primary Mathematics Classroom,
7(1), 24-31.
National
Council of Teachers of Mathematics. (2000). Principles
and standards for school mathematics. Reston, VA: Author.
Pegg, J. E.
(2002a). Assessment in mathematics: A developmental approach. In J. M. Royer
(Ed.), Mathematical cognition (pp.
227-259). Greenwich, CT: Information Age Publishing.
Pegg, J. E.
(2002b). Fundamental cycles of cognitive growth. In A. Cockburn & E. Nardi
(Eds.), Proceedings of the 26th
conference of the International Group for the Psychology of Mathematics
Education (Vol. 4, pp. 41-48). Norwich, UK: University of East Anglia.
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.
Pfannkuch, M.,
Rubick, A., & Yoon, C. (2002). Statistical thinking and transnumeration. In
B. Barton, K. C. Irwin, M. Pfannkuch, & M. O. J. Thomas (Eds.), Mathematics education in the South Pacific:
Proceedings of the twenty-fifth annual conference of the Mathematics Education
Research Group of Australasia (Vol. 2, pp. 567-574). Sydney: MERGA.
Reading, C.,
& Shaughnessy, M. (2000). Student perceptions of variation in a sampling
situation. In T. Nakahara & M. Koyama (Eds.), Proceedings of the 24th annual conference of the International Group
for the Psychology of Mathematics Education (Vol. 4, pp. 89-96). Hiroshima,
Japan: Hiroshima University.
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: Kluwer Academic Publishers.
Roth, W-H.,
& Bowen, G. M. (2003). When are graphs worth ten thousand words? An
expert-expert study. Cognition and
Instruction, 21(4), 429-473.
Russell, S. J.
(2006). What does it mean that “5 has a lot”? From the world to data and back.
In G. F. Burrill (Ed.), Thinking and
reasoning with data and chance (pp. 17-29). Reston, VA: National Council of
Teachers of Mathematics.
Shaughnessy, J.
M. (1997). Missed opportunities in research on the teaching and learning of
data and chance. In F. Biddulph & K. Carr (Eds.), People in mathematics education: Proceedings of the twentieth annual
conference of the Mathematics Education Research Group of Australasia (Vol.
1, pp. 6-22). Waikato, NZ: MERGA.
Shaughnessy, J.
M. (2006). Research on students’ understanding of some big concepts in statistics.
In G. F. Burrill (Ed.), Thinking and
reasoning with data and chance (pp. 77-98). Reston, VA: National Council of
Teachers of Mathematics.
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. 957-1009). Charlotte, NC:
Information Age Publishing.
Swan, M.
(1988). Learning the language of functions and graphs. In J. Pegg (Ed.), Mathematics Interfaces: Proceedings of the
twelfth biennial conference of The Australian Association of Mathematics
Teachers (pp. 76-80). Newcastle, NSW: The New England Mathematical
Association.
Watson, J. M.
(2001). Longitudinal development of inferential reasoning by school students. Educational Studies in Mathematics, 47, 337-372.
Watson, J. M.
(2002). Inferential reasoning and the influence of cognitive conflict. Educational Studies in Mathematics, 51, 225-256.
Watson, J. M.
(2005). Variation and expectation as foundations for the chance and data
curriculum. In P. Clarkson, A. Downton, D. Gronn, M. Horne, A. McDonough, R.
Pierce, & A. Roche (Eds.), Building
connections: Theory, research and practice (Proceedings of the 28th annual
conference of the Mathematics Education Research Group of Australasia,
Melbourne, pp. 35-42). Sydney: MERGA.
Watson, J. M.
(2006). Statistical literacy at school:
Growth and goals. Mahwah, NJ: Lawrence Erlbaum.
Watson, J. M.,
Callingham, R. A., & Kelly, B. A. (2007). Students’ appreciation of
expectation and variation as a foundation for statistical understanding. Mathematical Thinking and Learning, 9, 83-130.
Watson, J. M.,
& Kelly, B. A. (2002a). Can grade 3 students learn about variation? In B. Phillips (Ed.), Proceedings
of the Sixth International Conference on Teaching Statistics: Developing a
statistically literate society, Cape Town, South Africa. [CD-ROM].
Voorburg, The Netherlands: International Statistical Institute.
[Online:
http://www.stat.auckland.ac.nz/~iase/publications/1/2a1_wats.pdf]
Watson, J. M.,
& Kelly, B. A. (2002b). Emerging concepts in chance and data. Australian Journal of Early Childhood, 27(4), 24-28.
Watson, J. M.,
& Kelly, B. A. (2004a). Statistical variation in a chance setting: A
two-year study. Educational Studies in
Mathematics, 57, 121-144.
Watson,
J. M., & Kelly, B. A. (2004b). Expectation versus variation: Students’
decision making in a chance environment. Canadian
Journal of Science, Mathematics and Technology Education, 4, 371-396.
Watson,
J. M., & Kelly, B. A. (2005). The winds are variable: Student intuitions
about variation. School Science and
Mathematics, 105, 252-269.
Watson,
J. M., & Kelly, B. A. (2006). Expectation versus variation: Students’
decision making in a sampling environment. Canadian
Journal of Science, Mathematics and Technology Education, 6, 145-166.
Watson, J. M.,
& Moritz, J. B. (2000). Developing concepts of sampling. Journal for Research in Mathematics
Education, 31, 44-70.
Watson, J. M.,
& Moritz, J. B. (2001). Development of reasoning associated with
pictographs: representing, interpreting, and predicting. Educational Studies in Mathematics, 48, 47-81.
Jane M. Watson
Faculty of Education
University of Tasmania
Private Bag 66
Hobart, Tasmania 7001
Australia