Educational Psychology Review

, Volume 14, Issue 1, pp 47–69 | Cite as

Review of Graph Comprehension Research: Implications for Instruction

  • Priti Shah
  • James Hoeffner
Article

Abstract

Graphs are commonly used in textbooks and educational software, and can help students understand science and social science data. However, students sometimes have difficulty comprehending information depicted in graphs. What makes a graph better or worse at communicating relevant quantitative information? How can students learn to interpret graphs more effectively? This article reviews the cognitive literature on how viewers comprehend graphs and the factors that influence viewers' interpretations. Three major factors are considered: the visual characteristics of a graph (e.g., format, animation, color, use of legend, size, etc.), a viewer's knowledge about graphs, and a viewer's knowledge and expectations about the content of the data in a graph. This article provides a set of guidelines for the presentation of graphs to students and considers the implications of graph comprehension research for the teaching of graphical literacy skills. Finally, this article discusses unresolved questions and directions for future research relevant to data presentation and the teaching of graphical literacy skills.

graphs graphical displays graph comprehension science education graphical literacy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

REFERENCES

  1. Armento, B. J., Nash, G. B., Salter, C. L., and Wixson, K. K. (1991). A More Perfect Union, Houghton Mifflin, Boston.Google Scholar
  2. Becker, R. A., Cleveland, W. S., and Wilks, A. R. (1988). Dynamic graphics for data analysis. In Cleveland, W. S., and McGill, M. E. (eds.), Dynamic Graphics for Statistics, Wadsworth, Belmont, CA, pp. 1-.Google Scholar
  3. Bell, A., and Janvier, C. (1981). The interpretation of graphs representing situations. Learn. Math. 2: 34–42.Google Scholar
  4. Bertin, J. (1983). In Berg, W. (trans.), Semiology of Graphics: Diagrams Networks Maps, The University ofWisconsin Press, Madison, WI.Google Scholar
  5. Brockmann, R. J. (1991). The unbearable distraction of color. IEEE Trans. Profession. Commun. 34: 153–159.Google Scholar
  6. Bryant, P. E., and Somerville, S. C. (1986). The spatial demands of graphs. Br. J. Psychol. 77: 187–197.Google Scholar
  7. Carmichael, L. C., Hogan, H. P., and Walters, A. A. (1932). An experimental study of the effect of language on the reproduction of visually perceived form. J. Exp. Psychol. 15: 73–86.Google Scholar
  8. Carpenter, P. A., and Shah, P. (1998). A model of the perceptual and conceptual processes in graph comprehension. J. Exp. Psychol. Appl. 4: 75–100.Google Scholar
  9. Carswell, C. M., Emery, C., and Lonon, A. M. (1993). Stimulus complexity and information integration in the spontaneous interpretation of line graphs. Appl. Cogn. Psychol. 7: 341–357.Google Scholar
  10. Carwell, C. M., Frankenberger, S., and Bernhard, D. (1991). Graphing in depth: Perspectives on the use of three-dimensional graphs to represent lower-dimensional data. Behav. Inform. Technol. 10: 459–474.Google Scholar
  11. Carswell, C. M., and Wickens, C. D. (1987). Information integration and the object display: An interaction of task demands and display superiority. Ergonomics 30: 511–527.Google Scholar
  12. Chernoff, H. (1973). Using faces to represent points in k-dimensional space graphically. J. Am. Stat. Assoc. 68: 361–368.Google Scholar
  13. Clement, J. (1985). Misconceptions in graphing. In Streetfland, L. (ed.), Proceedings of the Ninth International Conference of the International Group for the Psychology of Mathematics Education, IGPME, Utrecht, The Netherlands, Vol. 1, pp. 369–375.Google Scholar
  14. Cleveland, W. (1993). Visualizing Data, AT&T Bell Laboratories, Murray Hill, NJ.Google Scholar
  15. Cleveland, W. S., Diaconis, P., and McGill, R. (1982). Variables on scatterplots look more highly correlated when the scales are increased. Science 216: 1138–1141.Google Scholar
  16. Cleveland, W. S., and McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. J. Am. Stat. Assoc. 77: 541–547.Google Scholar
  17. Cleveland, W. S., and McGill, R. (1985). Graphical perception and graphical methods for analyzing scientific data. Science 229: 828–833.Google Scholar
  18. Fisher, H. T. (1982). Mapping Information, Abt Books, Cambridge, MA.Google Scholar
  19. Fischer, M. H. (2000). Do irrelevant depth cues affect the comprehension of bar graphs? Appl. Cogn. Psychol. 14: 151–162.Google Scholar
  20. Freedman, E.G., and Smith, L.D. (1996). The role of data and theory in covariation assesesment: Implications for the theory-ladenness of observation.J. Mind Behav. 17: 321–343.Google Scholar
  21. Gattis, M., and Holyoak, K. (1995). Mapping conceptual to spatial relations in visual reasoning. J. Exp. Psychol. Learn. Mem. Cogn. 22: 1–9.Google Scholar
  22. Guthrie, J. T., Weber, S., and Kimmerly, N. (1993). Searching documents: Cognitive process and deficits in understanding graphs, tables, and illustrations. Contemp. Educ. Psychol. 18: 186–221.Google Scholar
  23. Halford, G. S., Wilson, W. H., and Phillips, S. (1998). Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology. Behav. Brain Sci. 21: 803–864.Google Scholar
  24. Hegarty, M., Quillici, J., Narayanan, N. H., Holmquist, S., and Moreno, R. (1999). Multimedia instruction: Lessons from evaluation of a theory-based design. J. Educ. Multimed. Hypermed. 8: 119–150.Google Scholar
  25. Hoffman, R. R., Deitweiler, M., and Lipton, K. S. (1993). General guidance for establishing color standards for meteorological displays. Weather Forecast.Google Scholar
  26. Howell, W. C. (1993). Engineering psychology in a changing world. Ann. Rev. Psychol. 231-263.Google Scholar
  27. Huber, P. J. (1987). Experiences with three-dimensional scatterplots. J. Am. Stat. Assoc. 82: 448–453.Google Scholar
  28. Hunter, B., Crismore, A., and Pearson, P. D. (1987). Visual displays in basal readers and social science textbooks. In Willows, D., and Houghton, H. A. (eds.), The Psychology of Illustration, Vol. 2: Instructional Issues, Springer, New York, pp. 116–135.Google Scholar
  29. Janvier, C. (1981). Use of situations in mathematics education. Educ. Stud. Math. 12: 113–122.Google Scholar
  30. Jennings, D. L., Amabile, T., and Ross, L. (1982). Informal covariation assessment: Data-based versus theory-based judgements. In Kahneman, D., Slovic, P., and Tversky, A. (eds.), Judgement Under Uncertainty: Heuristics and Biases, Cambridge University Press, Cambridge, England, pp. 211–230.Google Scholar
  31. Kaput, J. J. (1987). Representation and mathematics. In Janvier, C. (ed.), Problems of Representation in Mathematics Learning and Problem Solving, Erlbaum, Hillsdale, NJ, pp. 19–26.Google Scholar
  32. Kosslyn, S. (1985). Graphics and human information processing: A review of five books. J. Am. Stat. Assoc. 80: 499–512.Google Scholar
  33. Kosslyn, S. (1989). Understanding charts and graphs. Appl. Cogn. Psychol. 3: 185–225.Google Scholar
  34. Kosslyn, S. M. (1994). Elements of Graph Design, Freeman, New York.Google Scholar
  35. Kubovy, M. (1981). Concurrent pitch segregation and the theory of indispensable attributes. In Kubovy, M., and Pomerantz, J. (eds.), Perceptual Organization, Erlbaum, Hillsdale, NJ.Google Scholar
  36. Larkin, J. H., and Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cogn. Sci. 11: 65–99.Google Scholar
  37. Lauer, T. W., and Post, G. V. (1989). Density in scatterplots and the estimation of correlation. Behav. Inform. Technol. 8: 235–244.Google Scholar
  38. Legge, G. E., Gu, Y., and Luebker, A. (1989). Efficiency of graphical perception. Percept. Psychophys. 46: 365–374.Google Scholar
  39. Lehrer, R., and Romberg, T. (1996). Exploring children's data modeling. Cogn. Instr. 14: 69–108.Google Scholar
  40. Leinhardt, G., Zaslavsky, O., and Stein, M. K. (1990). Functions, graphs, and graphing: Tasks, learning, and teaching. Rev. Educ. Res. 60: 1–64.Google Scholar
  41. Levy, E., Zacks, J., Tversky, B., and Schiano, D. (1996). Gratuitous graphics: Putting preferences in perspective. Human Factors in Computing Systems: Conference Proceedings, ACM, New York, pp. 42–49.Google Scholar
  42. Lewandowsky, S., and Behrens, J. T. (1999). Statistical graphs and maps. In Durso, F., Dumais, S., Nickerson, R., Schvaneveldt, R., Chi, M., and Lindsay, S. (eds.), The Handbook of Applied Cognitive Psychology,Wiley, Chichester, England, pp. 513–549.Google Scholar
  43. Lewandowsky, S., and Spence, I. (1989). Discriminating strata in scatterplots. J. Am. Stat. Assoc. 84: 682–688.Google Scholar
  44. Lohse, G. L. (1993). A cognitive model of understanding graphical perception. Hum. Comp. Interact. 8: 353–388.Google Scholar
  45. Lord, C. G., Ross, L., and Lepper, M. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequent evidence. J. Pers. Soc. Psychol. 37: 2098–2110.Google Scholar
  46. MacDonald-Ross, M. (1977). Graphics in texts. Rev. Res. Educ. 5: 49–85.Google Scholar
  47. Maichle, U. (1994). Cognitive processes in understanding line graphs. In Schnotz, W., and Kulhavy, R.W. (eds.), Comprehension of Graphics, Elsevier, Amsterdam.Google Scholar
  48. Marchak, F. M., and Marchak, L. C. (1991). Interactive versus passive dynamics and the exploratory analysis of multivariate data. Behav. Res. Methods Instrum. Comput. 23: 296–300.Google Scholar
  49. McDermott, L., Rosenquist, M., and vanZee, E. (1987). Students difficulties in connecting graphs and physics: Example from kinematics. Am. J. Phys. 55: 503–513.Google Scholar
  50. McKenzie, D. L., and Padilla, M. J. (1986). The construction and validation of the Test of Graphing in Science (TOGS). J. Res. Sci. Teach. 23: 571–579.Google Scholar
  51. Merwin, D. H., Vincow, M., and Wickens, C. D. (1994). Visual analysis of scientific data; Comparison of 3d-topographic, color and gray scale displays in a feature detection task. In Proceedings of the 38th Annual Meeting of the Human Factors and Ergonomics Society, Human Factors and Ergonomics Society, Santa Monica, CA.Google Scholar
  52. Morrison, J. B., Tversky, B., and Betrancourt, M. (2000). Animation: Does it facilitate learning? Paper presented at the AAAI Spring Symposium on Smart Graphics, March, 2000, AAAI Press.Google Scholar
  53. Nachmias, R., and Linn, M. C. (1987). Evaluations of science laboratory data: The role of computer-presented information. J. Res. Sci. Teach. 24: 491–505.Google Scholar
  54. Phillips, R. J. (1982). An experimental investigation of layer tints for relief maps in school atlases. Ergonomics 25: 1143–1154.Google Scholar
  55. Pinker, S. (1990). A theory of graph comprehension. In Freedle, R. (ed.), Artificial Intelligence and the Future of Testing, Erlbaum, Hillsdale, NJ, pp. 73–126.Google Scholar
  56. Preece, J. (1990). Some HCI issues concerned with displaying quantitative information graphically. In Gorny, P., and Tauber, M. J. (eds.), Visualization in Human-Computer Interaction, Springer, New York.Google Scholar
  57. Quintana, C., Eng, J., Carra, A., Wu, H., and Soloway, E. (1999). Symphony: A Case Study in Extending Learner-Centered Design Through Process Space Analysis, Paper presented at CHI 99: Conference on Human Factors in Computing Systems, May 19-21, 1999, Pittsburgh, Pennsylvania.Google Scholar
  58. Reiser, B. J., Tabak, I., Sandoval, W. A., Smith, B., Steinmuller, F., and Leone, T. J. (in press). BGuILE: Stategic and conceptual scaffolds for scientific inquiry in biology classrooms. In Carver, S. M., and Klahr, D. (eds.), Cognition and Instruction: Twenty Five Years of Progress, Erlbaum, Mahvah, NJ.Google Scholar
  59. Scardamalia, N., Bereiter, C., and Lamon, M. (1994). The CSILE Project: Trying to bring the classroom into the world. In McGilly, K. (ed.), Classroom Lessons: Integrating Cognitive Theory and Classroom Practice, MIT Press, Cambridge, MA.Google Scholar
  60. Schiano, J. D., and Tversky, B. (1992). Structure and strategy in encoding simplified graphs. Mem. Cogn. 20: 12–20.Google Scholar
  61. Schmid, C. (1983). Statistical Graphics: Design Principles and Practices, Wiley, New York.Google Scholar
  62. Schunn, C. D., and Anderson, J. R. (1999). The generality/specificity of expertise in scientific reasoning. Cogn. Sci. 23: 337–370.Google Scholar
  63. Shah, P., and Carpenter, P. A. (1995). Conceptual limitations in comprehending line graphs. J. Exp. Psychol. Gen. 124: 43–61.Google Scholar
  64. Shah, P., Mayer, R. E., and Hegarty, M. (1999). Graphs as aids to knowledge construction: Signaling techniques for guiding the process of graph comprehension. J. Educ. Psychol. 91: 690–702.Google Scholar
  65. Shah, P. (in press). Graph comprehension: The role of format, content, and individual differences. In Anderson, M., Meyer, B., and Olivier, P. (eds.), Diagrammatic Representation and Reasoning, Springer, New York.Google Scholar
  66. Shah, P. (1995). Cognitive Processes in Graph Comprehension, Unpublished doctoral dissertation.Google Scholar
  67. Shah, P., and Shellhammer, D. (1999). The Role of Domain Knowledge and Graph Reading Skills in Graph Comprehension, Paper presented at the 1999 Meeting of the Society for Applied Research in Memory and Cognition, Boulder, CO.Google Scholar
  68. Simkin, D. K., and Hastie, R. (1986). An information processing analysis of graph perception. J. Am. Stat. Assoc. 82: 454–465.Google Scholar
  69. Somerville, S. C., and Bryant, P. E. (1985). Young children's use of spatial coordinates. Child Dev. 56: 604–613.Google Scholar
  70. Spence, I. (1990). Visual psychophysics of simple graphical elements, J. Exp. Psychol. Hum. Percept. Perform. 16: 683–692.Google Scholar
  71. Stenning, K., and Oberlander, J. (1995). Acognitive theory of graphical and linguistic reasoning: Logic and implementation. Cogn. Sci. 19: 97–140.Google Scholar
  72. Stuetzle, W. (1987). Plot windows. J. Am. Stat. Assoc. 82: 466–475.Google Scholar
  73. Tufte, E. R. (1983). The Visual Display of Quantitative Information, Graphics, Cheshire, CT.Google Scholar
  74. Tversky, B. (in press). Spatial schemas in depictions. In Gattis, M. (ed.), Spatial Schemas and Abstract Thought, MIT Press, Cambridge.Google Scholar
  75. Tversky, B., Kugelmass, S., and Winter, A. (1991). Cross-cultural and developmental trends in graphic productions. Cogn. Psychol. 23: 515–557.Google Scholar
  76. Tversky, B., and Schiano, D. J. (1989). Perceptual and conceptual factors in distortions inmemory for graphs and maps. J. Exp. Psychol. Gen. 118: 387–398.Google Scholar
  77. Vernon, M. D. (1950). The visual presentation of factual information. Br. J. Educ. Psychol. 20: 174–185.Google Scholar
  78. Wickens, C.D., Merwin, D. H., and Lin, E. L. (1994). Implications of graphics enhancements for the visualization of scientific data: Dimensional integrality, stereopsis, motion, and mesh. Hum. Fact. 36: 44–61.Google Scholar
  79. Wilkinson, L. (1999). Graphs for research in counseling psychology. Counsel. Psychol. 27: 384–407.Google Scholar
  80. Winn, B. (1987). Charts, graphs, and diagrams in educational materials. In Willows, D., and Houghton, H. A. (eds.), The Psychology of Illustration, Springer, New York.Google Scholar
  81. Zacks, J., and Tversky, B. (1999). Bars and lines: A study of graphic communication. Mem. Cogn. 27: 1073–1079.Google Scholar
  82. Zacks, J., Levy, E., Tversky, B., and Schiano, D. J. (1998). Reading bar graphs: Effects of depth cues and graphical context. J. Exp. Psychol. Appl. 4: 119–138.Google Scholar

Copyright information

© Plenum Publishing Corporation 2002

Authors and Affiliations

  • Priti Shah
    • 1
  • James Hoeffner
    • 1
  1. 1.Department of PsychologyUniversity of MichiganAnn Arbor

Personalised recommendations