Advertisement

ZDM

, Volume 50, Issue 7, pp 1223–1235 | Cite as

Middle school students’ reasoning about data and context through storytelling with repurposed local data

  • Michelle Hoda WilkersonEmail author
  • Vasiliki Laina
Original Article

Abstract

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.

Notes

Acknowledgements

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).

References

  1. 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.CrossRefGoogle Scholar
  2. Bal, M. (1997). Narratology: Introduction to the theory of narrative. Narratology Introduction to the Theory of narrative.  https://doi.org/10.2307/1772578.CrossRefGoogle Scholar
  3. 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).Google Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. Bruner, J. (1991). The narrative construction of reality. Critical Inquiry, 18(1), 1–21.CrossRefGoogle Scholar
  8. 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.
  9. 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.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. Collins, A., & Ferguson, W. (1993). Epistemic forms and epistemic games: Structures and strategies to guide inquiry. Educational Psychologist, 28(1), 25–42.CrossRefGoogle Scholar
  12. 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.Google Scholar
  13. 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.CrossRefGoogle Scholar
  14. Gould, R. (2017). Data literacy is statistical literacy. Statistics Education Research Journal, 16(1), 22–25.Google Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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/.
  19. 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.CrossRefGoogle Scholar
  20. Lehrer, R., & Romberg, T. (1996). Exploring children’s data modeling. Cognition and Instruction, 14(1), 69–108.  https://doi.org/10.1207/s1532690xci1401.CrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. 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.
  26. 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.CrossRefGoogle Scholar
  27. 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.Google Scholar

Copyright information

© FIZ Karlsruhe 2018

Authors and Affiliations

  1. 1.University of California, BerkeleyBerkeleyUSA

Personalised recommendations