Advertisement

Read It This Way: Scaffolding Comprehension for Unconventional Statistical Graphs

  • Amy Rae FoxEmail author
  • James Hollan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10871)

Abstract

How do you make sense of a graph that you have never seen before? In this work, we outline the types of prior knowledge relevant when making sense of an unconventional statistical graph. After observing students reading a deceptively simple graph for time intervals, we designed four instructional scaffolds for evaluation. In a laboratory study, we found that only one scaffold (an interactive image) supported accurate interpretation for most students. Subsequent analysis of differences between two sets of materials revealed that task structure–specifically the extent to which a problem poses a mental impasse–may function as a powerful aid for comprehension. We find that prior knowledge of conventional graph types is extraordinarily difficult to overcome.

Keywords

Graph comprehension Scaffold Unconventional graph 

Notes

Acknowledgements

Sincerest thanks are offered to research assistants Evan Barosay, Kai-Yu Chang and Hazel Leung, as well as Drs. Seana Coulson, David Kirsh, Rafael Núñez and Caren Walker.

References

  1. 1.
    Larkin, J., Simon, H.: Why a diagram is (sometimes) worth ten thousand words. Cognit. Sci. 99, 65–99 (1987)CrossRefGoogle Scholar
  2. 2.
    Shah, P., Hoeffner, J.: Review of graph comprehension research: implications for instruction. Educ. Psychol. Rev. 14(1), 47–69 (2002)CrossRefGoogle Scholar
  3. 3.
    Acartürk, C.: Points, lines and arrows in statistical graphs. In: Cox, P., Plimmer, B., Rodgers, P. (eds.) Diagrams 2012. LNCS (LNAI), vol. 7352, pp. 95–101. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-31223-6_13CrossRefGoogle Scholar
  4. 4.
    Acartürk, C.: Towards a systematic understanding of graphical cues in communication through statistical graphs. J. Vis. Lang. Comput. 25(2), 76–88 (2014)CrossRefGoogle Scholar
  5. 5.
    Kong, N., Agrawala, M.: Graphical overlays: using layered elements to aid chart reading. IEEE Trans. Vis. Comput. Graph. 18(12), 2631–2638 (2012)CrossRefGoogle Scholar
  6. 6.
    Mautone, P.D., Mayer, R.E.: Cognitive aids for guiding graph comprehension. J. Educ. Psychol. 99(3), 640–652 (2007)CrossRefGoogle Scholar
  7. 7.
    Pinker, S.: Theory of graph comprehension. In: Freedle, R. (ed.) Artificial Intelligence and the Future of Testing, pp. 73–126. Erlbaum, Hillsdale (1990)Google Scholar
  8. 8.
    Shah, P., Freedman, E.G., Vekiri, I.: The comprehension of quantitative information in graphical displays. In: Shah, P., Miyake, A. (eds.) Cambridge Handbook of Visuospatial Thinking (2005)Google Scholar
  9. 9.
    Kulpa, Z.: A diagrammatic approach to investigate interval relations. J. Vis. Lang. Comput. 17(5), 466–502 (2006)CrossRefGoogle Scholar
  10. 10.
    Qiang, Y., Delafontaine, M., Versichele, M., De Maeyer, P., Van de Weghe, N.: Interactive analysis of time intervals in a two-dimensional space. Inf. Vis. 11(4), 255–272 (2012)CrossRefGoogle Scholar
  11. 11.
    Qiang, Y., Valcke, M., De Maeyer, P., Van de Weghe, N.: Representing time intervals in a two-dimensional space: an empirical study. J. Vis. Lang. Comput. 25(4), 466–480 (2014)CrossRefGoogle Scholar
  12. 12.
    Hsieh, H.-F., Shannon, S.E.: Three approaches to qualitative content analysis. Qual. Health Res. 15(9), 1277–1288 (2005)CrossRefGoogle Scholar
  13. 13.
    Ohlsson, S.: Information-processing explanations of insight and related phenomena. In: Advances in the Psychology of Thinking, pp. 1–44 (1992)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of California – San DiegoSan DiegoUSA

Personalised recommendations