Abstract
Six 7th-grade students engaged with an instructional sequence involving the use of the TinkerPlots software to organize data sets in ways intended to help them construe two attributes: the location of quarters of the data within a sub-range of the entire set, and the spread of those portions. We present evidence of students’ thinking about these attributes drawn from their responses to questions posed in the instructional sequence. Comparison of findings from a pre-test and a culminating task suggest that the students enriched their ability to imagine and create hypothetical data from a given representative box plot, and that they became oriented to the spread of portions of a data set as indicated by the length of its quarters.
Résumé
Six élèves de 7e année ont participé à une séquence pédagogique impliquant l’utilisation du logiciel TinkerPlotsMC dans le but d’organiser des ensembles de données de façon à ce que cela les aide à interpréter deux attributs: l’emplacement des données organisées en quartiles à l’intérieur d’un sous-intervalle de variation et la répartition de ces portions. À partir des réponses données aux questions posées dans la séquence pédagogique, nous montrons comment les élèves conçoivent ces attributs. Quand on compare les conclusions tirées d’un prétest avec celles d’une tâche intégratrice, on constate que les élèves ont amélioré leur aptitude pour imaginer et concevoir des données hypothétiques résultant d’un diagramme de quartiles représentatif donné; et qu’ils se sont orientés vers la répartition des portions d’ensembles de données en fonction de l’étendue des quartiles.
Similar content being viewed by others
References
Bakker, A., Biehler, R., & Konold, C. (2004). Should young students learn about box plots. Curricular Development in Statistics Education, 163–173.
Bakker, A., & Gravemeijer, K. P. (2004). Learning to reason about distribution. In D. Ben-Zvi & J. Garfield (Eds.), The challenge of developing statistical literacy, reasoning and thinking (pp. 147–168). Springer. http://link.springer.com/content/pdf/https://doi.org/10.1007/1-4020-2278-6_7.pdf
Bright, G. W., Brewer, W., McClain, K., & Mooney, E. S. (2003). Navigating through data analysis in grades 6–8. National Council of Teachers of Mathematics.
Clement, J. (2000). Analysis of clinical interviews: Foundations and model viability. In A. E. Kelly & R. A. Lesh (Eds.), Handbook of research design in mathematics and science education (pp. 547–589). Earlbaum
Cobb, P. (1999). Individual and collective mathematical development: The case of statistical data analysis. Mathematical thinking and learning, 1(1), 5-43.
Cobb, P. (2007). Putting philosophy to work: Coping with multiple theoretical perspectives. In F. K. Lester, Jr. (Ed.), Second handbook of research on mathematics teaching and learning: A project of the National Council of Teachers of Mathematics (pp. 3-38). Charlotte, N. C.: Information Age Publishing.
Cobb, P. & Yackel, E. (1996). Constructivist, emergent, and sociocultural perspectives in the context of developmental research. Educational Psychologist, 31(3-4), 175-190.
Cobb, P., Confrey, J., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.
Corbin, J. M., & Strauss, A. L. (2008). Basics of qualitative research: Techniques and procedures for developing grounded theory (3rd ed). Sage Publications, Inc.
Dawson, R. (2011). How significant is a boxplot outlier? Journal of Statistics Education, 19(2).
Edwards, T. G., Özgün-Koca, A., & Barr, J. (2017). Interpretations of boxplots: Helping middle school students to think outside the box. Journal of Statistics Education, 25(1), 21–28. https://doi.org/10.1080/10691898.2017.1288556
Garfield, J. B., & Ben-Zvi, D. (2008). Developing students’ statistical reasoning: Connecting research and teaching practice. Springer.
Harel, G. (2008). DNR perspective on mathematics curriculum and instruction, Part II: with reference to teachers’ knowledge base. Zentralblatt fuer Didaktik der Mathematik, 40, 893–907.
Harel, G. (2007). The DNR system as a conceptual framework for curriculum development and instruction. In R. A. Lesh, E. Hamilton & J. J. Kaput (Eds.), Foundations for the future in mathematics education (1st ed.). Routledge.
Kaplan, J. J., Fisher, D. G., & Rogness, N. T. (2009). Lexical ambiguity in statistics: What do students know about the words association, average, confidence, random and spread. Journal of Statistics Education, 17(3).
Konold, C. (2007). Designing a data analysis tool for learners. In M. Lovett & P. Shah (Eds.), Thinking with Data (pp. 267–292). Lawrence Erlbaum Associates.
Konold, C., & Miller, C. D. (2005). TinkerPlots: Dynamic data exploration. Key Curriculum Press.
Konold, C., Robinson, A., Khalil, K., Pollatsek, A., Well, A., Wing, R., & Mayr, S. (2002). Students’ use of modal clumps to summarize data. In B. Philips (Ed.), Developing a Statistically Literate Society: Proceedings of the Sixth International Conference on Teaching Statistics (p. 6). International Statistical Institute.
Lane, D. M. (n.d.). Box plots. Online statistics education: An Interactive multimedia course of study. http://onlinestatbook.com/chapter2/boxplots.html
Lem, S., Kempen, G., Ceulemans, E., Onghena, P., Verschaffel, L., & Van Dooren, W. (2015). Combining multiple external representations and refutational text: An Intervention on learning to interpret box plots. International Journal of Science and Mathematics Education, 13(4), 909–926. https://doi.org/10.1007/s10763-014-9604-3
Lem, S., Onghena, P., Verschaffel, L., & Van Dooren, W. (2013). The heuristic interpretation of box plots. Learning and Instruction, 26, 22–35. https://doi.org/10.1016/j.learninstruc.2013.01.001
Lem, S., Onghena, P., Verschaffel, L., & Van Dooren, W. (2014). Experts’ misinterpretation of box plots – A Dual processing approach. Psychologica Belgica, 54(4), 395–405. https://doi.org/10.5334/pb.az
Lobato. J., Hohensee, C., & Rhodehamel, B. (2013). Students’ mathematical noticing. Journal For Research in Mathematics Education, 44(5), 809-850.
Pfannkuch, M. (2007). Year 11 students’ informal inferential reasoning: A Case study about the interpretation of box plots. International Electronic Journal of Mathematics Education, 2(3), 149–167.
Saldanha, L., & McAllister, M. (2016). Building up the box plot as a tool for representing and structuring data distributions: An instructional effort using TinkerPlots and evidence of students’ reasoning. In D. Ben-Zvi & K. Makar (Eds.), The Teaching and Learning of Statistics: International perspectives (pp. 235-245). Springer.
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
Watson, J. M., Fitzallen, N. E., Wilson, K. G., & Creed, J. F. (2008). The representational value of hats. Mathematics Teaching in the Middle School, 14(1), 4-10.
Disclaimer
Any opinions, findings, and conclusions or recommendations expressed in this report are those of the authors and do not necessarily reflect the views of the National Science Foundation. National Science Foundation.
Funding
This report is based on work supported by the National Science Foundation under Grant No. 0953987.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Saldanha, L., Hatfield, N. Students Conceptualizing the Box Plot as a Tool for Structuring Quantitative Data: a Design Experiment Using TinkerPlots. Can. J. Sci. Math. Techn. Educ. 21, 758–782 (2021). https://doi.org/10.1007/s42330-021-00184-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42330-021-00184-0