Lecturing graphing: What features of lectures contribute to student difficulties in learning to interpret graph?

  • G. Michael BowenEmail author
  • Wolff-Michael Roth
Teachers and Teaching


Some studies suggest that individuals having completed undergraduate science programs are often poorly prepared to use graphs in ways typical of their disciplines. Science and technology studies have identified competency in graphing as being of central importance to the practice of a scientific discipline. Given the centrality of graphing to the practice of science, an important aspect of becoming enculturated into the practices of a scientific discipline is being able to use and interpret graphs in ways that are typical to that discipline. For example, competency in this usage is important to reading, interpreting and understanding journal articles in a discipline. Undergraduate science students spend a considerable amount of time in lectures where graphical representations play a major role in the presentation of subject matter. To gain an understanding of the use of graphs in lectures and how this use contributes to student understanding, this paper provides a microanalysis of graph use in lectures drawn from artifacts compiled from videotaping all lectures and seminars in a thirteen week ecology course. This analysis focused on both the text and the geestural references made in the reading of a graph in an ecology lecture. We conclude that the common ground existing amongst scientists that help them reach an agreed upon interpretation of a graph is missing from the present lectures and then discuss the constraints this places on students, learning about graphs in lectures.


American Educational Research Association Undergraduate Science Caption Text Representational Practice Interpretive Flexibility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Anderson, J. R. (1985).Cognitive psychology and its implications. San Francisco, CA: Freeman.Google Scholar
  2. Bastide, F. (1990). The iconography of scientific texts: Principles of analysis. In M. Lynch, & S. Woolgar (Eds.),Representation in scientific practice (pp. 187–229). Cambridge, MA: MIT Press.Google Scholar
  3. Bourdieu, P. (1997).Méditations pascaliennes [Pascalian meditations]. Paris: Seuil.Google Scholar
  4. Bowen, G. M., & Roth, W.-M. (1997, October).Graphing from Grade 8 to professional practice. Paper presented at the annual meeting of the Society for the Social Studies of Science, Tucson, Arizona.Google Scholar
  5. Bowen, G. M., & Roth, W.-M. (1998, April).Are pre-service teachers prepared to teach inquiry? Paper presented at the annual conference of the American Educational Research Association, San Diego, CA.Google Scholar
  6. Bowen, G. M., Roth, W.-M., & McGinn, M. K. (1997, March).Learning to interpret graphs in small group interactions in a second-year university ecology course. Paper presented at the annual conference of the American Educational Research Association, Chicago, Ill.Google Scholar
  7. Gadamer, H. G. (1975).Truth and method. London: Sheed Ward.Google Scholar
  8. Garfinkel, H. (1991). Respecification: Evidence for locally produced naturally accountable phenomena of order*, logic, reason, meaning, method, etc. in an as of the essential haecceity of immortal ordinary society, (I)—an announcement of studies. In G. Button (Ed.),Ethnomethodology and the human sciences (pp. 10–19). Cambridge: Cambridge University Press.Google Scholar
  9. Geertz, C. (1973).The interpretation of cultures: Selected essays. New York: Basic Books.Google Scholar
  10. Goodwin, C. (1986). Gestures as a resource for the organization of mutual orientation.Semiotica, 62, 29–49.CrossRefGoogle Scholar
  11. Heidegger, M. (1977).Sein und zeit [Being and time]. Tübingen, Germany: Max Niemeyer.Google Scholar
  12. Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice.The Journal of the Learning Sciences, 4, 39–103.CrossRefGoogle Scholar
  13. Knorr-Cetina, K. D., & Amann, K. (1990). Image dissection in natural scientific inquiry.Science, Technology, & Human Values, 15, 259–283.Google Scholar
  14. Latour, B. (1987).Science in Action: How to follow scientists and engineers through society. Milton Keynes: Open University Press.Google Scholar
  15. Latour, B. (1993).La clef de Berlin et autres leçons d'un amateur de sciences [The key to Berlin and other lessons of a science lover]. Paris: Éditions la Découverte.Google Scholar
  16. Leinhardt, G., Zaslavsky, O., & Stein, M. K. (1990). Functions, graphs, and graphing: Tasks, learning, and teaching.Review of Educational Research, 60, 1–64.CrossRefGoogle Scholar
  17. Lemke, J. L. (1998). Multiplying meaning: Visual and verbal semiotics in scientific text. In J. R. Martin, & R. Veel (Eds.),Reading science (pp. 87–113). London: Routledge.Google Scholar
  18. Lowe, R. K. (1993). Constructing a mental representation from an abstract technical diagram.Learning and Instruction, 3, 157–179.CrossRefGoogle Scholar
  19. Lynch, M. (1991). Method: Measurement-ordinary and scientific measurement as ethnomethodological phenomena. In G. Button (Ed.),Ethnomethodology and the human sciences (pp. 77–108. Cambridge: Cambridge University Press.Google Scholar
  20. Lynch, M. (1995). Laboratory space and the technological complex: An investigation of topical contextures. In S. L. Star (Ed.),Ecologies of knowledge: Work and politics in science and technology (pp. 226–256). Albany, NY: State University of New York Press.Google Scholar
  21. Pea, R. (1993). Practices of distributed intelligence and designs for education. In G. Salomon (Ed.),Distributed cognitions: Psychological and educational considerations (pp. 47–87). Cambridge: Cambridge University Press.Google Scholar
  22. Ricklefs, R. E. (1990).Ecology (3rd ed.). New York: Freeman.Google Scholar
  23. Ricœur, P. (1991).From text to action: Essays in hermeneutics, II. Evanston, IL: Northwestern University Press.Google Scholar
  24. Roth, W.-M. (1996). Where is the context in contextual word problems?: Mathematical practices and products in Grade 8 students' answers to story problems.Cognition and Instruction, 14, 487–527.CrossRefGoogle Scholar
  25. Roth, W.-M. & Bowen, G. M. (1998, April).cognitive complexities of graphical representations during ecology lectures: A phenomenological hermeneutics approach. Paper presented at the annual meeting of the American Educational Research Association, San Diego, California.Google Scholar
  26. Roth, W.-M., & Masciotra, D. (in preparation). From thing to sign: Toward a genetic phenomenology of graph interpretation.Google Scholar
  27. Roth, W.-M., & McGinn, M. K. (1997). Graphing: Cognitive ability or practice?Science Education, 81, 91–106.CrossRefGoogle Scholar
  28. Roth, W.-M., McGinn, M. K., & Bowen, G. M. (1998). How prepared are preservice teachers to teach scientific inquiry? Levels of performance in scientific representation practices.Journal of Science Teacher Education, 9(1), 25–48.CrossRefGoogle Scholar
  29. Roth, W.-M., McGinn, M. K., & Bowen, G. M. (1997, April). Towards an anthropology of graphing. Paper presented at the annual meeting of the American Educational Research Association, Chicago, Illinois.Google Scholar
  30. Roth, W.-M., McRobbie, C., Lucas, K. B., & Boutonné, S. (1997). Why do students fail to learn from demonstrations? A social practice perspective on learning in physics.Journal of Research in Science Teaching, 34, 509–533.CrossRefGoogle Scholar
  31. Roth, W.-M., & Tobin, K. (1996). Staging Aristotle and natural observation against Galileo and (stacked) scientific experiment or physics lectures as rhetorical events.Journal of Research in Science Teaching, 33, 135–157.CrossRefGoogle Scholar
  32. Roth, W.-M., Tobin, K., & Shaw, K. (1997). How numbers, tables, graphs, and money come to represent a rolling ball: A microanalysis of “difficult” physics lectures.International Journal of Science Education, 19, 1075–1091.Google Scholar
  33. Schnotz, W. (1993). Introduction.Learning and Instruction, 3, 151–155.CrossRefGoogle Scholar
  34. Suchman, L. A., & Trigg, R. H. (1993). Artificial intelligence as craftwork. In S. Chaiklin, & J. Lave (Eds.),Understanding practice: Perspectives on activity and context (pp. 144–178). Cambridge: Cambridge University Press.Google Scholar
  35. Winograd, T., & Flores, F. (1987).Understanding computers: A new foundation for design. New York: Addison-Wesley.Google Scholar
  36. Woolgar, S. (1990). Time and documents in researcher interaction: Some ways of making out what is happening in experimental science. In M. Lynch, & S. Woolgar (Eds.),Representation in scientific practice (pp. 123–152). Cambridge, MA: MIT.Google Scholar

Copyright information

© Australian Science Research Association 1998

Authors and Affiliations

  1. 1.Faculty of EducationUniversity of VictoriaVictoriaCanada

Personalised recommendations