Abstract
This paper describes elementary students’ awareness and representation of the aggregate properties and variability of data sets when engaged in predictive reasoning. In a design study, 46 third-graders interpreted a table of historical temperature data to predict and represent future monthly maximum temperatures. The task enabled students to interpret numbers in context and apply their understanding of inherent natural variation to create a generalised data set. Student predictions, representations, and written and verbal descriptions were analysed using two frameworks—Awareness of Mathematical Pattern and Structure (AMPS), and Data Lenses. While 54% of students used the variability of the given data table to predict temperatures that were within the historical range for each month, only 20% described the table by focusing on aggregate properties. Student representations varied from highly structured line and bar graphs to idiosyncratic drawings on weather-related themes. In total, 83% of student representations were either idiosyncratic or direct copies of the data table. These findings suggest a progression in students’ predictive reasoning, with an awareness of range and seasonal patterns emerging before a multifaceted aggregate view.
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Notes
As part of the next cycle of the design study, the same students were given a similar task 12 months later. This “turning of the axes” was also observed in five students, although not the same individuals as in this study. In the third and final cycle when the students were about to start fifth grade, there were no examples of this type of representation.
Pseudonyms used for all students.
References
Abrahamson, D. (2012). Seeing chance: Perceptual reasoning as an epistemic resource for grounding compound event spaces. ZDM Mathematics Education, 44(7), 869–881. https://doi.org/10.1007/s11858-012-0454-6
Aridor, K., & Ben-Zvi, D. (2017). The co-emergence of aggregate and modelling reasoning. Statistics Education Research Journal, 16(2), 38–63 Retrieved from http://iase-web.org/Publications.php?p=SERJ
Australian Government. (2018). Bureau of Meterorology. Retrieved February 1, 2018, from climate data online monthly mean maximum temperatures Sydney (Observatory Hill): http://www.bom.gov.au/climate/data/
Bakker, A. (2018). Design research in education: A practical guide for early career researchers. London, UK: Routledge.
Bakker, A., & Derry, J. (2011). Lessons from inferentialism for statistics education. Mathematical Thinking and Learning, 13(1–2), 5–26. https://doi.org/10.1080/10986065.2011.538293
Ben-Zvi, D., & Aridor-Berger, K. (2016). Children’s wonder how to wander between data and context. In D. Ben-Zvi & K. Makar (Eds.), The teaching and learning of statistics: International perspectives (pp. 25–36). Cham, Switzerland: Springer.
Ben-Zvi, D., Gravemeijer, K., & Ainley, J. (2018). Design of statistics learning environments. In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), International handbook of research in statistics education (pp. 473–502). Cham, Switzerland: Springer.
Biehler, R., & Pratt, D. (2012). Research on the reasoning, teaching and learning of probability and uncertainty. ZDM Mathematics Education, 44(7), 819–823. https://doi.org/10.1007/s11858-012-0468-0
Burrill, G., & Biehler, R. (2011). Fundamental statistical ideas in the school curriculum and in training teachers. In C. Batanero, G. Burrill, & C. Reading (Eds.), Teaching statistics in school mathematics - challenges for teaching and teacher education (pp. 57–69). Dordrecht, the Netherlands: Springer.
Cobb, P. (1999). Individual and collective mathematical development: The case of statistical data analysis. Mathematical Thinking and Learning, 1(1), 5–43. https://doi.org/10.1207/s15327833mtl0101_1
Doerr, H., Delmas, R., & Makar, K. (2017). A modeling approch to the development of students’ informal inferential reasoning. Statistics Educational Research Journal, 16(2), 86–115 Retrieved from http://iase-web.org/Publications.php?p=SERJ
Eichler, A., & Vogel, M. (2012). Basic modelling of uncertainty: Young students’ mental models. ZDM Mathematics Education, 44(7), 841–854. https://doi.org/10.1007/s11858-012-0451-9
English, L. (2010). Modeling with complex data in the primary school. In R. Lesh, P. L. Galbraith, C. R. Haines, & A. Hurford (Eds.), Modeling students’ mathematical modeling competencies: ICTMA 13 (pp. 287–298). Boston, MA: Springer.
English, L. (2012). Data modelling with first-grade students. Educational Studies in Mathematics, 81(1), 15–30. https://doi.org/10.1007/s10649-011-9377-3
English, L. (2013). Reconceptualizing statistical learning in the early years. In L. English & J. Mulligan (Eds.), Reconceptualizing early mathematics learning (pp. 67–82). Dordrecht, the Netherlands: Springer.
Falk, R., Yudilevich-Assouline, P., & Elstein, A. (2012). Children's concept of probability. Educational Studies in Mathematics, 81, 207–233. https://doi.org/10.1007/s10649-012-9402-1.
Franklin, C., Kader, G., Mewborn, D., Moreno, J., Peck, R., Perry, M., & Scheaffer, R. (2007). Assessment and instruction in statistics education (GAISE) Report A Pre-K-12 curriculum framework. American Statistical Association. https://www.amstat.org/asa/files/pdfs/GAISE/GAISEPreK-12_Full.pdf
Hourigan, M., & Leavy, A. (2015). What do the stats tell us? Engaging elementary children in probabilistic reasoning based on data analysis. Teaching Statistics, 38(1), 8–15. https://doi.org/10.1111/test.12084
Jones, G., Thornton, C., Langrall, C., Mooney, E., Perry, B., & Putt, I. (2000). A framework for characterizing children’s statistical thinking. Mathematical Thinking and Learning, 2(4), 269–307. https://doi.org/10.1207/S15327833MTL0204_3
Kazak, S., Wegerif, R., & Fujita, T. (2015). Combining scaffolding for content and scaffolding for dialogue to support conceptual breakthroughs in understanding probability. ZDM Mathematics Education, 47(7), 1269–1283. https://doi.org/10.1007/s11858-015-0720-5
Kinnear, V., & Clark, J. (2014). Probabilistic reasoning and prediction with young children. In I. Anderson, M. Cavanagh, & A. Prescott (Ed.), Curriculum in focus: Research guided practice proceedings of the 37th annual conference of the mathematics education research group (pp. 335–342). Sydney, Australia: MERGA.
Konold, C., Higgins, T., Russell, S. J., & Khalil, K. (2015). Data seen through different lenses. Educational Studies in Mathematics, 88, 305–325. https://doi.org/10.1007/s10649-013-9529-8
Konold, C., & Pollatsek, A. (2002). Data analysis as the search for signals in noisy processes. Journal for Research in Mathematics Education, 33(4), 259–289. https://doi.org/10.2307/749741
Lakoff, G., & Nunez, R. E. (2000). Where mathematics comes from: How the embodied mind bring mathematics into being. New York, NY: Basic Books.
Leavy, A. (2008). An examination of the role of statistical investigation in supporting the development of young children's statistical reasoning. In O. N. Saracho & B. Spodek (Eds.), Contemporary perspectives on mathematics in early childhood education (pp. 215–232). Charlotte, NC: Information Age Publishing.
Lehrer, R., & English, L. (2018). Introducing children to modelling variability. In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), The international handbook of research in statistics education (pp. 229–260). Cham, Switzerland: Springer.
Lehrer, R., & Schauble, L. (2017). Children’s conceptions of sampling in local ecosystems investigations. Science Education, 101, 968–984. https://doi.org/10.1002/sce.21297
Makar, K. (2014). Young children’s explorations of average through informal inferential reasoning. Educational Studies in Mathematics, 86(1), 61–78. https://doi.org/10.1007/s10649-013-9526-y
Makar, K. (2016). Developing young children’s emergent inferential practices in statistics. Mathematical Thinking and Learning, 18(1), 1–24. https://doi.org/10.1080/10986065.2016.1107820
Makar, K., & Rubin, A. (2009). A framework for thinking about informal statistical inference. Statistics Education Research Journal, 8(1), 82–105.
Makar, K., & Rubin, A. (2017). Learning about statistical inference. In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), The international handbook of research in statistics education (pp. 261–294). Cham, Switzerland: Springer.
Mulligan, J. (2015). Moving beyond basic numeracy: Data modeling in the early years of schooling. ZDM Mathematics Education, 47(4), 653–663. https://doi.org/10.1007/s11858-015-0687-2
Mulligan, J., & Mitchelmore, M. (2009). Awareness of pattern and structure in early mathematical development. Mathematics Education Research Journal, 21(2), 33–49. https://doi.org/10.1007/BF03217544
Mulligan, J., Oslington, G., & English, L. (2020). Supporting early mathematical development through a ‘pattern and structure’ intervention program. ZDM Mathematics Education. https://doi.org/10.1007/s11858-020-01147-9
Oslington, G. (2018). Second-graders’ predictive reasoning strategies. In E. Bergqvist, M. Osterholm, C. Granberg, & L. Sumpter (Ed.), Proceeding of the 42nd Conference of the International Group for the Psychology of Mathematics Education. 3, pp. 435–442. Umea, Sweden: PME.
Oslington, G., Mulligan, J., & Van Bergen, P. (2018). Young children’s reasoning through data exploration. In V. Kinnear, M. Y. Lai, & T. Muir (Eds.), Forging connections in early mathematics teaching and learning (pp. 191–212). Singapore: Springer.
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. https://doi.org/10.1080/10986065.2003.9679997
Watson, J. (2006). Statistical literacy at school: Growth and goals. Mahwah, NJ: Lawrence Erlbaum.
Watson, J., Callingham, R., & English, L. (2017). Students’ development of statistical literacy in the upper primary years. In A. Downton, S. Livy, & J. Hall (Eds.), 40 years on: We are still learning! Proceedings of the 40th annual conference of the mathematics education research Group of Australasia (pp. 538–545). Melbourne, Australia: MERGA.
Watson, J., Collis, K., Callingham, R., & Moritz, J. (1995). A model for assessing higher order thinking in statistics. Educational Research and Evaluation, 1(3), 247–275. https://doi.org/10.1080/1380361950010303
Watson, J., & Kelly, B. (2005). The winds are variable: Student intuitions about variation. School Science and Mathematics, 105(5), 252–269.
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The research was completed in a manner consistent with the principles of the research ethics of the American Psychological Association and approved through Macquarie University Ethics (approval number 5201600461).
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Oslington, G., Mulligan, J. & Van Bergen, P. Third-graders’ predictive reasoning strategies. Educ Stud Math 104, 5–24 (2020). https://doi.org/10.1007/s10649-020-09949-0
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DOI: https://doi.org/10.1007/s10649-020-09949-0