Publicly-available datasets, though useful for education, are often constructed for purposes that are quite different from students’ own. To investigate and model phenomena, then, students must learn how to repurpose the data. This paper reports on an emerging line of research that builds on work in data modeling, exploratory data analysis, and storytelling to examine and support students’ data repurposing. We ask: What opportunities emerge for students to reason about the relationship between data, context, and uncertainty when they repurpose public data to explore questions about their local communities? And, How can these opportunities be supported in classroom instruction and activity design? In two exploratory studies, students were asked to pose questions about their communities, use publicly-available data to investigate those questions, and create visual displays and written stories about their findings. Across both enactments, opportunities for reasoning emerged especially when students worked to reconcile (1) their own knowledge and experiences of the context from which data were collected with details of the data provided; and (2) their different emerging stories about the data with one another. We review how these opportunities unfolded within each enactment at the level of group and classroom, with attention to facilitator support.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Ainley, J., Gould, R., & Pratt, D. (2015). Learning to reason from samples: Commentary from the perspectives of task design and the emergence of “big data”. Educational Studies in Mathematics, 88(3), 405–412. https://doi.org/10.1007/s10649-015-9592-4.
Bal, M. (1997). Narratology: Introduction to the theory of narrative. Narratology Introduction to the Theory of narrative. https://doi.org/10.2307/1772578.
Ben-Zvi, D. (2006). Scaffolding students’ informal inference and argumentation. In ICOTS-7: Proceedings of the seventh international conference on teaching statistics (pp. 1–6).
Ben-Zvi, D., & Aridor-Berger, K. (2015). Children’s wonder how to wander between data and context. In The teaching and learning of statistics: International perspectives (pp. 25–36). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-23470-0_3.
Ben-Zvi, D., Makar, K., & Garfield, J. (2018). International handbook of research in statistics education. Cham: Springer. https://doi.org/10.1007/978-3-319-66195-7.
Brown, A. L., & Campione, J. (1994). Guided discovery in a community of learners. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 229–270). Cambridge, MA, USA: MIT Press. https://doi.org/10.1037/000276.
Bruner, J. (1991). The narrative construction of reality. Critical Inquiry, 18(1), 1–21.
Chance, B., Ben-Zvi, D., Garfield, J. B., & Medina, E. (2007). The role of technology in improve student learning of statistics. Technology Innovations in Statistics Education, 1(1). http://repositories.cdlib.org/uclastat/cts/tise/vol1/iss1/art2.
Cobb, P., Confrey, J., diSessa, A. A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13. https://doi.org/10.3102/0013189X032001009.
Cobb, P., & McClain, K. (2004). Principles of instructional design for supporting the development of students’ statistical reasoning. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning, and thinking (pp. 375–395). Dordreht, The Netherlands: Springer. https://doi.org/10.1007/1-4020-2278-6_16.
Collins, A., & Ferguson, W. (1993). Epistemic forms and epistemic games: Structures and strategies to guide inquiry. Educational Psychologist, 28(1), 25–42.
Franklin, C., Kader, G., Mewborn, D., Moreno, J., Peck, R., Perry, M., & Scheaffer, R. (2007). Guidelines for assessment and instruction in statistics education (GAISE) report. Alexandria, VA.
Garfield, J. B., & Ben-Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International Statistical Review, 75(3), 372–396. https://doi.org/10.1111/j.1751-5823.2007.00029.x.
Gould, R. (2017). Data literacy is statistical literacy. Statistics Education Research Journal, 16(1), 22–25.
Hancock, C., Kaput, J. J., & Goldsmith, L. T. (1992). Authentic inquiry with data: Critical barriers to classroom implementation. Educational Psychologist, 27(3), 337–364. https://doi.org/10.1207/s15326985ep2703.
Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. Journal of the Learning Sciences, 4(1), 39–103. https://doi.org/10.1207/s15327809jls0401_2.
Konold, C., Higgins, T., Russell, S. J., & Khalil, K. (2015). Data seen through different lenses. Educational Studies in Mathematics, 88(3), 305–325. https://doi.org/10.1007/s10649-013-9529-8.
Lee, V. R., & Wilkerson, M. H. (2018). Data use by middle and secondary students in the digital age: A status report and future prospects. Commissioned paper for the National Academy of Sciences, Engineering, and Medicine, Board on Science Education, Committee on Science Investigations and Engineering Design for Grades 6–12. https://works.bepress.com/victor_lee/43/.
Lehrer, R., Kim, M. J., & 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(3), 195–216. https://doi.org/10.1007/s10758-007-9122-2.
Lehrer, R., & Romberg, T. (1996). Exploring children’s data modeling. Cognition and Instruction, 14(1), 69–108. https://doi.org/10.1207/s1532690xci1401.
Pfannkuch, M. (2011). The role of context in developing informal statistical inferential reasoning: A classroom study. Mathematical Thinking and Learning, 13(October), 27–46. https://doi.org/10.1080/10986065.2011.538302.
Pfannkuch, M., Regan, M., Wild, C., & Horton, N. (2010). Telling data stories: Essential dialogues for comparative reasoning. Journal of Statistics Education, 18(1), 1–38. https://doi.org/10.1080/00107530.1992.10746755.
Philip, T. M., Olivares-Pasillas, M. C., & Rocha, J. (2016). Becoming racially literate about data and data-literate about race: Data visualizations in the classroom as a site of racial-ideological micro-contestations. Cognition and Instruction, 34(4), 361–388. https://doi.org/10.1080/07370008.2016.1210418.
Rosebery, A. S., Ogonowski, M., DiSchino, M., & Warren, B. (2010). “The coat traps all your body heat”: Heterogeneity as fundamental to learning. Journal of the Learning Sciences, 19(3), 322–357. https://doi.org/10.1080/10508406.2010.491752.
Shaughnessy, J., & Pfannkuch, M. (2002). How faithful is old faithful? Mathematics Teacher, 95(4), 252–259. http://www.web.pdx.edu/~jfreder/M212/oldfaithful.pdf.
Wild, C., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–265. https://doi.org/10.1111/j.1751-5823.1999.tb00442.x.
Wilkerson, M., Lanouette, K. A., Shareff, R. L., Erickson, T., Bulalacao, N., Heller, J., St. Clair, N., Finzer, W., & Reichsman, F. (2018). Data transformations: Restructuring data for inquiry in a simulation and data analysis environment. In J. Kay & R. Luckin (Eds.), Rethinking learning in the digital age: Making the learning sciences count. Proceedings of the 13th international conference of the learning sciences (ICLS 2018) (pp. 1383–1384). London: ISLS.
Thanks to participating students, schools, teachers, and Jenna Conversano. This work was supported by a National Science Foundation Grant (IIS-1350282) and Tufts University Faculty Research Fund. Recommendations do not necessarily reflect the views of the NSF, UC-Berkeley, or Tufts. We are grateful for feedback from the CoRE writing group and members of the 10th Statistical Reasoning, Thinking, and Literacy Research Forum (SRTL-10).
About this article
Cite this article
Wilkerson, M.H., Laina, V. Middle school students’ reasoning about data and context through storytelling with repurposed local data. ZDM Mathematics Education 50, 1223–1235 (2018). https://doi.org/10.1007/s11858-018-0974-9
- Publicly-available Datasets
- Public School Enrollment
- Ward Geography
- Sighting Reports