The role of perceptual similarity, context, and situation when selecting attributes: considerations made by 5–6-year-olds in data modeling environments
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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.
KeywordsData modeling Attribute selection Statistical inquiry Young children Teaching mathematics Statistics Elementary education
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.
- Brousseau, G. (1997). Theory of didactical situations in mathematics. Dordrecht: Kluwer.Google Scholar
- DiSessa, A., Hammer, D., Sherin, B., & Kolpakowski, T. (1991). Inventing graphing: Metarepresentational expertise in children. Journal of Mathematical Behavior, 10(1), 117–160.Google Scholar
- 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
- Gelman, S. A. (2006). Early conceptual development. In K. McCartney & D. Phillips (Eds.), Blackwell handbook of early childhood development (pp. 149–166). Malden: Blackwell.Google Scholar
- 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.Google Scholar
- Gelman, S. A., & Markman, E. M. (1986). Categories and induction in young children. Cognition, 23, 183–209.Google Scholar
- 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.Google Scholar
- Hourigan, M., & Leavy, A. M. (2016). Practical problems: Introducing statistics to kindergarteners. Teaching Children Mathematics, 22(5), 283–291.Google Scholar
- Kinnear, V. (2013). Young children’s statistical reasoning: A tale of two contexts (Unpublished doctoral dissertation). Queensland University of Technology, Brisbane, Australia.Google Scholar
- 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., & 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., & 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.Google Scholar
- 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..Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Paparistodemou, E., & Meletiou-Mavrotheris, M. (2008). Enhancing reasoning about statistical inference in 8 year-old students. Statistics Education Research Journal, 7(2), 83–106.Google Scholar
- Uzzell, B. P., Ponton, M., & Ardila, A. (Eds.). (2013). International handbook of cross-cultural neuropsychology. Mahwah: Lawrence Erlbaum.Google Scholar