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A cross-disciplinary approach to teaching data literacy and proportionality

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Abstract

The Thinking with Data project (TWD) expands on current notions of data literacy by (1) focusing on proportional reasoning as key to data literacy and (2) leveraging the non-mathematics disciplines to engage students in deep thinking about the context of data and the application of proportionality. A set of four 2-week, sequential modules for cross-disciplinary implementation in seventh-grade classrooms was designed and evaluated. Using a quasi-experimental approach, we found that student data literacy was increased through the focused integration of social studies, mathematics, science, and English language arts. In this article, we describe our theoretical approach to designing and implementing the modules, report on student learning gains in mathematics, and describe teacher reactions to the materials. In sum, our study provides evidence that the TWD approach has the potential to build data literacy while also allowing students to learn core discipline-based content standards.

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References

  • Adijage, R., & Pluvinage, F. (2007). An experiment in teaching ratio and proportion. Educational Studies in Mathematics, 65(2), 149–175.

    Article  Google Scholar 

  • Ben-Zvi, D., & Arcavi, A. (2001). Junior high school students’ construction of global views of data and data representations. Educational Studies in Mathematics, 45, 35–65.

    Article  Google Scholar 

  • Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with interesting implications. In A. Iran-Nejad & P. D. Pearson (Eds.), Review of research in education (Vol. 24, pp. 61–101). Washington DC: American Educational Research Association.

    Google Scholar 

  • Briggs, W. (2002). Quantitative literacy and SIAM. SIAM News 35(2). Retrieved from. http://www-math.cudenver.edu/∼wbriggs/qr/siam_news.html.

  • Clements, D. H. (2007). Curriculum research: Toward a framework for “research-based curricula. Journal for Research in Mathematics Education, 38(1), 35–70.

    Google Scholar 

  • Cobb, G., & Moore, D. (1997). Mathematics, statistics, and teaching. The American Mathematical Monthly, 104, 801–823.

    Article  Google Scholar 

  • Cohen, D. K., & Hill, H. (2001). Learning policy: When state education reform works. New Haven: Yale University Press.

    Google Scholar 

  • Corbin, J., & Strauss, A. (1990). Grounded theory method: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13, 3–21.

    Article  Google Scholar 

  • English, L. D., & Sriraman, B. (2010). Problem solving for the 21st century. In B. Sriraman & L. D. English (Eds.), Theories of mathematics education: Seeking new frontiers (Advances in Mathematics Education, Series, pp. 263–285). Berlin: Springer.

    Chapter  Google Scholar 

  • Fogerty, R. (2009). How to integrate the curricula. Thousand Oaks: Corwin.

    Google Scholar 

  • Freudenthal, H. (1983). Didactical phenomenology of mathematical structures. Boston: D. Reidel Publishing Company.

    Google Scholar 

  • Gleick, P. (2001). Where’s Waldo? A review of the skeptical environmentalist. Retrieved from http://pacinst.org/topics/integrity_of_science/blog/?p=37.

  • Gravemeijer, K. P. E. (1999). How emergent models may foster the constitution of formal mathematics. Mathematical Thinking and Learning, 1, 155–177.

    Article  Google Scholar 

  • Gravemeijer, M., & Doorman, M. (1999). Context problems in realistic mathematics education: A calculus course as an example. Educational Studies in Mathematics, 39, 103–129.

    Article  Google Scholar 

  • Hancock, C., Kaput, J., & Goldsmith, L. (1992). Authentic inquiry with data: Critical barriers to classroom implementation. Educational Psychologist, 27(3), 337–364.

    Article  Google Scholar 

  • Jones, K. (2000). Providing a foundation for deductive reasoning: Students’ interpretations when using dynamic geometry software and their evolving mathematical explanations. Educational Studies in Mathematics, 44(1/2), 55–85.

    Article  Google Scholar 

  • Kaput, J. (1999). Teaching and learning a new algebra. In E. Fennema & T. A. Romberg (Eds.), Mathematics classrooms that promote understanding (pp. 133–155). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15(10), 661–667.

    Article  Google Scholar 

  • Lajoie, S., Jacobs, V., & Lavigne, N. (1995). Empowering students in the use of statistics. The Journal of Mathematical Behavior, 14, 401–425.

    Article  Google Scholar 

  • Lamon, S. J. (1994). Ratio and proportion: Cognitive foundations in unitizing and norming. In G. Harel & J. Confrey (Eds.), The development of multiplicative reasoning in the learning of mathematics (pp. 89–120). Albany: State University of New York Press.

    Google Scholar 

  • Lehrer, R., & Schauble, L. (2003). Origins and evolution of model-based reasoning in mathematics and science. In R. Lesh & H. M. Doerr (Eds.), Beyond constructivism: A models and modeling perspective on mathematics problem-solving, learning, and teaching (pp. 59–70). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Lesh, R. A., Middleton, J. A., Caylor, E., & Gupta, S. (2008). A science of need: Designing tasks to engage students in modeling complex data. Educational Studies in Mathematics, 68, 113–130.

    Article  Google Scholar 

  • Madison, B. L. (2002, February). Educating for numeracy: A challenging responsibility. Notices of the American Mathematical Society 49(2). Retrieved from www.ams.org/noticesomment/200202/commentary.pdf.

  • Mariotti, M. A. (2000). Introduction to proof: The mediation of a dynamic software environment. Educational Studies in Mathematics, 44(1/2), 25–53.

    Article  Google Scholar 

  • McClain, K., & Cobb, P. (2001). Supporting students’ ability to reason about data. Educational Studies in Mathematics, 45, 111–129.

    Article  Google Scholar 

  • Miyakawa, T., & Winslow, C. (2009). Didactical designs for students’ proportional reasoning: An “open approach” lesson and a “fundamental situation. Educational Studies in Mathematics, 72, 199–218.

    Article  Google Scholar 

  • Moore, D. (1985). Statistics: Concepts and controversies. San Francisco: W.H. Freeman.

    Google Scholar 

  • Moreno-Armella, L., Hegedus, S., & Kaput, J. (2008). Static to dynamic mathematics: Historical and representational perspectives. Special issue of Educational Studies in Mathematics: Democratizing Access to Mathematics Through Technology Issues of Design and Implementation, 68(2), 99–111.

    Google Scholar 

  • National Council of Teachers of Mathematics (NCTM). (1995). Connecting mathematics across the curriculum. Reston: Author.

    Google Scholar 

  • Penuel, W. R., Roschelle, J., & Shechtman, N. (2007). Designing formative assessment software with teachers: An analysis of the co-design process. Research and Practice in Technology Enhanced Learning, 2(1), 51–74.

    Article  Google Scholar 

  • Roschelle, J., Shechtman, N., Tatar, D., Hegedus, S., Hopkins, B., Empson, S., Knudsen, J., & Gallagher, L. (2010). Integration of technology, curriculum, and professional development for advancing middle school mathematics: Three large-scale studies. American Educational Research Journal, 47(4), 833–878.

    Article  Google Scholar 

  • Rubin, A. (2005). Math that matters. Hands On: A Journal for Mathematics and Science Educators, 28(1), 3–7.

    Google Scholar 

  • Scheaffer, R. L. (2001). Quantitative literacy and statistics. Amstat News. Retrieved from www.amstat.org/publications/amsn/index.cfm?fuseaction=pres112001.

  • Schwartz, J. L. (1988). Intensive quantity and referent-transforming operations. In J. Hiebert & M. Behr (Eds.), Number concepts and operations in the middle grades (pp. 41–52). Reston: National Council of Teachers of Mathematics.

    Google Scholar 

  • Steen, L. A. (2001). Mathematics and democracy: The case for quantitative literacy. N.p.: National Council on Education and the Disciplines.

  • Stein, M. K., Smith, M. S., Henningsen, M., & Silver, E. A. (2000). Implementing standards-based mathematics instruction: A casebook for professional development. New York: Teachers College Press.

    Google Scholar 

  • Thompson, P. W., & Thompson, A. G. (1992). Images of rate. Paper presented at the Annual Meeting of the American Educational Research Association, San Francisco, CA.

  • Tourniaire, F., & Pulos, S. (1985). Proportional reasoning: A review of the literature. Educational Studies in Mathematics, 16, 181–204.

    Article  Google Scholar 

  • United Nations. (1997). UN Convention on the Law of the Non-navigational Uses of International Watercourses (Resolution 51/229). New York: General Assembly of the United Nations.

    Google Scholar 

  • Vahey, P., Enyedy, N., & Gifford, B. (2000). Learning probability through the use of a collaborative, inquiry-based simulation environment. Journal of Interactive Learning Research, 11(1), 51–84.

    Google Scholar 

  • Vahey, P., Yarnall, L., Patton, C., Zalles, D., & Swan, K. (2006). Mathematizing middle school: Results from a cross-disciplinary study of data literacy. San Francisco: Paper presented at the Annual Conference of the American Educational Research Association.

    Google Scholar 

  • Wheeler, D. (1982). Mathematization matters. For the Learning of Mathematics, 3(1), 45–47.

    Google Scholar 

Download references

Acknowledgments

The authors thank the teachers and students who were so enthusiastic about the TWD project. We also thank the editor and anonymous reviewers for all their effort in providing constructive feedback on early versions of this article. The research reported on in this paper was funded by the National Science Foundation under Grant No. NSF ESI-0628122. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Correspondence to Philip Vahey.

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Vahey, P., Rafanan, K., Patton, C. et al. A cross-disciplinary approach to teaching data literacy and proportionality. Educ Stud Math 81, 179–205 (2012). https://doi.org/10.1007/s10649-012-9392-z

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