Classroom data modeling involves posing questions, identifying attributes of phenomena, measuring and structuring these attributes, and then composing, revising, and communicating the outcomes. Selecting attributes is a fundamental component of data modeling, and the considerations made when selecting attributes is the focus of this paper. A teaching experiment involving 2 teacher educators and 25 pre-service teachers (PSTs) was carried out with 24 young children (5–6-year-olds) as part of a 4-day data modeling investigation. Although perceptual features of the data influenced initial approaches to attribute selection, considerations of the problem situation influenced a shift from the perceptual and towards consideration of attributes such as taxonomy, habitat, behavior, and diet. Expertise in the data context (animal kingdom) and ability to collaborate and negotiate within groups supported children in their ability to switch attributes, attend to multiple situations presented by the problem, and modify and extend their categorizations of data.
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The children appeared unaware that polar bears are arctic animals and penguins reside in a number of regions (typically the southern hemisphere, sometimes Antarctic, and sometimes residing in tropical islands). They are not all cold weather birds.
Blair, M., & Somerville, S. C. (2009). The importance of differentiation in young children’s acquisition of expertise. Cognition, 112, 259–280.
Brousseau, G. (1997). Theory of didactical situations in mathematics. Dordrecht: Kluwer.
Chi, M. T. H., & Koeske, R. D. (1983). Network representation of a child’s dinosaur knowledge. Developmental Psychology, 19, 29–39.
Cobb, P., McClain, K., & Gravemeijer, K. (2003). Learning about statistical covariation. Cognition and Instruction, 21(1), 1–78.
Cobb, P., Wood, T., Yackel, E., & McNeal, B. (1992). Characteristics of classroom mathematics traditions: An interactional analysis. American Educational Research Journal, 29, 573–604.
Darmody, M., & Smyth, E. (2012). Exploring school and classroom environments in Irish primary schools. Children, Youth and Environments, 22(1), 178–197.
DiSessa, A., Hammer, D., Sherin, B., & Kolpakowski, T. (1991). Inventing graphing: Metarepresentational expertise in children. Journal of Mathematical Behavior, 10(1), 117–160.
English, L. D. (2010). Young children’s early modelling with data. Mathematics Education Research Journal, 22(2), 24–47.
English, L. D. (2012). Data modelling with first-grade students. Educational Studies in Mathematics, 81, 15–30.
Ertle, B., Chokshi, S., & Fernandez, C. (2001). Lesson planning tool. Retrieved November 28 2016, from www.tc.columbia.edu/centers/lessonstudy/doc/Lesson_Planning_Tool.pdf
Fletcher-Janzen, E., Strickland, T. L., & Reynolds, C. (2000). Handbook of cross-cultural neuro-psychology. New York: Springer Science & Business Media. https://doi.org/10.1007/978-1-4615- 4219-3
Gelman, S. A. (2006). Early conceptual development. In K. McCartney & D. Phillips (Eds.), Blackwell handbook of early childhood development (pp. 149–166). Malden: Blackwell.
Gelman, S. A., Chesnick, R., & Waxman, S. R. (2005). Mother-child conversations about pictures and objects: Referring to categories and individuals. Child Development, 76, 1129–1143.
Gelman, S. A., & Markman, E. M. (1986). Categories and induction in young children. Cognition, 23, 183–209.
Gentner, D., & Namy, L. L. (1999). Comparison in the development of categories. Cognitive Development, 14, 487–513.
Hancock, C., Kaput, J. T., & Goldsmith, L. T. (1992). Authentic inquiry with data: Critical barriers to classroom implementation. Educational Psychologist, 27(3), 337–364.
Hanner, S., James, E., & Rohlfing, M. (2002). Classification models across grades. In R. Lehrer & L. Schauble (Eds.), Investigating real data in the classroom (pp. 99–117). New York: Teachers College.
Hourigan, M., & Leavy, A. M. (2016). Practical problems: Introducing statistics to kindergarteners. Teaching Children Mathematics, 22(5), 283–291.
Johnson, K. E., & Mervis, C. B. (1994). Microgenetic analysis of first steps in children’s acquisition of expertise on shorebirds. Developmental Psychology, 30, 418–435.
Kinnear, V. (2013). Young children’s statistical reasoning: A tale of two contexts (Unpublished doctoral dissertation). Queensland University of Technology, Brisbane, Australia.
Kinnear, V., & Clarke, J. (2016). Young children’s abductive reasoning about data. In Proceedings of the 13th International Congress on Mathematical Education. Hamburg, July 24–31. Retrieved from http://icme13.org/files/ICME13-Programme356_low36.pdf
Leavy, A. M. (2015). Looking at practice: Revealing the knowledge demands of teaching data handling in the primary classroom. Mathematics Education Research Journal, 27(3), 283–309. https://doi.org/10.1007/s13394-014-0138-3
Leavy, A. M., & Hourigan, M. (2015). Motivating inquiry in statistics and probability in the primary classroom. Teaching Statistics, 37(2), 41–47. https://doi.org/10.1111/test.12062
Leavy, A. M., & Hourigan, M. (2016a). Crime scenes and mystery players! Using interesting contexts and driving questions to support the development of statistical literacy. Teaching Statistics, 38(1), 29–35. https://doi.org/10.1111/test.12088
Leavy, A. M., & Hourigan, M. (2016b). Using lesson study to support knowledge development in initial teacher education: Insights from early number classrooms. Teaching and Teacher Education, 57, 161–175. https://doi.org/10.1016/j.tate.2016.04.002
Lehrer, R., Kim, M., & 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, 195–216.
Lehrer, R., & Schauble, L. (2000). Inventing data structures for representational purposes: Elementary grade students’ classification models. Mathematical Thinking and Learning, 21(1&2), 51–74.
Lehrer, R., & Schauble, L. (2007). Contrasting emerging conceptions of distribution in contexts of error and natural variation. In M. C. Lovett & P. Shah (Eds.), Thinking with data (pp. 149–176). New York: Taylor & Francis.
Lesh, R., & Doerr, H. M. (Eds.). (2003). Beyond constructivism: Models and modeling perspectives on mathematics teaching, learning, and problem solving. Mahwah: Lawrence Erlbaum Associates, Inc..
Lesh, R., & Kelly, A. (2000). Multitiered teaching experiments. In A. Kelly & R. Lesh (Eds.), Research design in mathematics and science education (pp. 197–230). Mahwah: Lawrence Erlbaum Associates.
Lesh, R., & Lehrer, R. (2003). Mathematical learning. In W. Reynolds & G. Miller (Eds.), Comprehensive handbook of psychology (Vol. 7, pp. 357–390). New York: John Wiley.
Paparistodemou, E., & Meletiou-Mavrotheris, M. (2008). Enhancing reasoning about statistical inference in 8 year-old students. Statistics Education Research Journal, 7(2), 83–106.
Petersen, L. A., & McNeil, N. M. (2013). Effects of perceptually rich manipulatives on preschoolers’ counting performance: Established knowledge counts. Child Development, 84(3), 1020–1033.
Samuelson, L. K., & Smith, L. B. (2005). They call it like they see it: Spontaneous naming and attention to shape. Developmental Science, 8, 182–198.
Uzzell, B. P., Ponton, M., & Ardila, A. (Eds.). (2013). International handbook of cross-cultural neuropsychology. Mahwah: Lawrence Erlbaum.
Wild, C., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry (with discussion). International Statistical Review, 67(3), 223–265.
We gratefully acknowledge the work of all reviewers and editors of this manuscript who invested their time and energy in providing insightful and constructive feedback on earlier versions of this paper.
The research was supported by a faculty seed-funding grant [SF16-101] from Mary Immaculate College.
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Leavy, A., Hourigan, M. The role of perceptual similarity, context, and situation when selecting attributes: considerations made by 5–6-year-olds in data modeling environments. Educ Stud Math 97, 163–183 (2018). https://doi.org/10.1007/s10649-017-9791-2
- Data modeling
- Attribute selection
- Statistical inquiry
- Young children
- Teaching mathematics
- Elementary education