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Drawing Inference from Data Visualisations

Received: 8 July 2014     Accepted: 28 July 2014     Published: 20 August 2014
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Abstract

This article investigates how 14- to 16- year-old students interpret representations of multivariate data generated by data visualisation tools and how they then seek to construct their own meaningful data visualizations that highlight emerging important aspects of data. Students were asked a single question—about where they would like to live—that involved reasoning about a complex data set with many different variables that they were able to explore using a dynamic visualization tool that allowed them to easily generate multiple visualizations of the relevant data set. Findings show the diverse inferences that students articulated to reason about covariation between multiple variables while using the cycle of inquiry and visual analysis. Students revisited their specific kinds of inferences while using complex data visualisation tools, inventing and revising their visual representations of data. Once they obtained some necessary insight, they readily made an informed decision.

Published in International Journal of Secondary Education (Volume 2, Issue 4)
DOI 10.11648/j.ijsedu.20140204.12
Page(s) 66-72
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Inference, Big Data, Multivariate Data, Covariation, Data Visualisations, Visualisation Tools

References
[1] IBM. (2002). What is big data? — Bringing big data to the enterprise. Retrieved August 26, 2013, from http://www.ibm.com
[2] National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston, VA: National Council of teachers of Mathematics.
[3] Australian Curriculum, Assessment and Reporting Authority. (2011). Australian Curriculum: Mathematics. Version 1.2. Retrieved March 15, 2011, from http://www.acara.edu.au
[4] Ridgway, J., Nicholson, J., & McCusker, S. (2013). Reasoning with Multivariate Evidence. Technology innovations in Statistics, 7 (2), 1933-4214.
[5] Prodromou, T. (2013). Data Visualisation and Statistics from the Future. Proceedings of the 59th ISI World Statistics Congress (Data visualization for youth appeal Sponsoring Association(s)) (Paper 3), p. 1-6. Hong Kong, China: International Statistical Institute (ISI). Online: http://www.statistics.gov.hk/wsc/IPS049-P3-S.pdf
[6] Gapminder Online: http://www.Gapminder.org/downloads/
[7] Nisbett R., & Ross, L. (1980). Human inference: Strategies and shortcomings of social judgment. New Jersey: prentice Hall.
[8] Engel, J., & Sedlmeier, P. (2011). Correlation and Regression in the training of teachers. In C. Batanero, G. Burrill, & C. Reading (Eds.), Teaching statistics in school mathematics-challenges for teaching and teacher education (pp. 97-107). New York: Springer Science+Business Media B.V. 2011.
[9] Vallee-Tourangeau, F., Hollingsworth, L., Murphy , R. (1998). Attentional bias in correlation judgments? Smedslund (1963) revisited. Scandinavian Journal of Psychology 39, 221-233.
[10] Jennings, D. L., Ammabile, T. M., & Ross, L., (1982). Informal covariation assessment: Data-based versus theory-based judgments. In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgments under uncertainty: Heuristics and biases (pp. 221-230). New York: Cambridge University Press.
[11] Lane, D.M., Anderson, C. A., & Kellam, K. L. (1985). Judging the relatedness of variables: The psychophysics or cavariation detection. Journal of experimental Psychology, 11 (5), 640-649.
[12] Erlick, D. E., & Mills, R. G. (1967). Perceptual quantification of conditional dependency. Journal of experimental Psychology, 73 (1), 9-14.
[13] Morton, K., Bunker, R., Mackinlay, J., Morton, R., & Stolte, C. (2012). Dynamic workload driven data integration in Tableau. In proceedings of the Special interest Group on Management of Data Conference (pp. 807−816).
[14] Stake, R. E. (2000). Case studies. In N. K. Denzin, & Y. S. Lincoln (Eds.). Handbook of qualitative research (2nd ed., pp. 435-354). Thousand Oaks, CA: Sage.
[15] Stake, R. E. (1995). The Art of case Study Research. Thousands Oaks, CA: Cage Publications.
[16] Robson, C. (1993). Real World Research. Oxford: Blackwell.
[17] Glaser, B. G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Mill Valley, CA: Sociology Press.
Cite This Article
  • APA Style

    Theodosia Prodromou. (2014). Drawing Inference from Data Visualisations. International Journal of Secondary Education, 2(4), 66-72. https://doi.org/10.11648/j.ijsedu.20140204.12

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    ACS Style

    Theodosia Prodromou. Drawing Inference from Data Visualisations. Int. J. Second. Educ. 2014, 2(4), 66-72. doi: 10.11648/j.ijsedu.20140204.12

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    AMA Style

    Theodosia Prodromou. Drawing Inference from Data Visualisations. Int J Second Educ. 2014;2(4):66-72. doi: 10.11648/j.ijsedu.20140204.12

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  • @article{10.11648/j.ijsedu.20140204.12,
      author = {Theodosia Prodromou},
      title = {Drawing Inference from Data Visualisations},
      journal = {International Journal of Secondary Education},
      volume = {2},
      number = {4},
      pages = {66-72},
      doi = {10.11648/j.ijsedu.20140204.12},
      url = {https://doi.org/10.11648/j.ijsedu.20140204.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsedu.20140204.12},
      abstract = {This article investigates how 14- to 16- year-old students interpret representations of multivariate data generated by data visualisation tools and how they then seek to construct their own meaningful data visualizations that highlight emerging important aspects of data. Students were asked a single question—about where they would like to live—that involved reasoning about a complex data set with many different variables that they were able to explore using a dynamic visualization tool that allowed them to easily generate multiple visualizations of the relevant data set. Findings show the diverse inferences that students articulated to reason about covariation between multiple variables while using the cycle of inquiry and visual analysis. Students revisited their specific kinds of inferences while using complex data visualisation tools, inventing and revising their visual representations of data. Once they obtained some necessary insight, they readily made an informed decision.},
     year = {2014}
    }
    

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    AB  - This article investigates how 14- to 16- year-old students interpret representations of multivariate data generated by data visualisation tools and how they then seek to construct their own meaningful data visualizations that highlight emerging important aspects of data. Students were asked a single question—about where they would like to live—that involved reasoning about a complex data set with many different variables that they were able to explore using a dynamic visualization tool that allowed them to easily generate multiple visualizations of the relevant data set. Findings show the diverse inferences that students articulated to reason about covariation between multiple variables while using the cycle of inquiry and visual analysis. Students revisited their specific kinds of inferences while using complex data visualisation tools, inventing and revising their visual representations of data. Once they obtained some necessary insight, they readily made an informed decision.
    VL  - 2
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Author Information
  • School of Education, University of New England, Armidale NSW 2351, AUSTRALIA

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