Research in Science Education

, Volume 44, Issue 6, pp 859–883 | Cite as

Drawing on Experience: How Domain Knowledge Is Reflected in Sketches of Scientific Structures and Processes

  • Benjamin D. Jee
  • Dedre Gentner
  • David H. Uttal
  • Bradley Sageman
  • Kenneth Forbus
  • Cathryn A. Manduca
  • Carol J. Ormand
  • Thomas F. Shipley
  • Basil Tikoff
Article

Abstract

Capturing the nature of students’ mental representations and how they change with learning is a primary goal in science education research. This can be challenging in spatially intense domains, such as geoscience, architecture, and engineering. In this research, we test whether sketching can be used to gauge level of expertise in geoscience, using new technology designed to facilitate this process. We asked participants with differing levels of geoscience experience to copy two kinds of geoscience images—photographs of rock formations and causal diagrams. To permit studying the process of sketching as well as the structure and content of the sketches, we used the CogSketch system (Forbus et al. 2011, Topics in Cognitive Science 3:648–666) to record the time course of sketching and analyze the sketches themselves. Relative to novices, geoscience students included more geological structures and relational symbols in their sketches of geoscience materials and were more likely to construct their sketches in a sequence consistent with the order of causal events. These differences appear to stem from differences in domain knowledge, because they did not show up in participants’ sketches of materials from other fields. The findings and methods of this research suggest new ways to promote and assess science learning, which are well suited to the visual–spatial demands of many domains.

Keywords

Spatial knowledge Causal knowledge Science education Sketching Sketching software STEM 

References

  1. Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333, 1096–1097.CrossRefGoogle Scholar
  2. Black, A. A. (2005). Spatial ability and earth science conceptual understanding. Journal of Geoscience Education, 53(4), 402–414.Google Scholar
  3. Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.CrossRefGoogle Scholar
  4. Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.CrossRefGoogle Scholar
  5. Chi, M. T. H., Glaser, R., & Farr, M. (Eds.). (1988). The nature of expertise. Hillsdale, NJ: Erlbaum.Google Scholar
  6. de Silva, R., Bischel, T. D., Lee, W., Peterson, E. J., Calfee, R. C. & Stahovich, T., 2007. Kirchhoff’s pen: A pen-based circuit analysis tutor. In Proceedings of the 4th Eurographics workshop on Sketch-based interfaces and modeling. Google Scholar
  7. Dihoff, R. E., Brosvic, G. M., & Epstein, M. (2004). Provision of feedback during preparation for academic testing: learning is enhanced by immediate but not delayed feedback. Psychological Record, 54, 207–231.Google Scholar
  8. Do, E. Y. (2005). Design sketches and sketch design tools. Knowledge Based Systems, 18, 383–405.CrossRefGoogle Scholar
  9. Duley, J. F., Wilkins, J., Hamby, S., Hopkins, D., Burwell, R., & Barry, N. (1993). Explicit scoring criteria for the Rey–Osterrieth and Taylor complex figures. The Clinical Neuropsychologist, 7, 29–38.CrossRefGoogle Scholar
  10. Forbus, K. (2011). Qualitative modeling. Wiley Interdisciplinary Reviews: Cognitive Science, 2(4), 374–391.Google Scholar
  11. Forbus, K., Usher, J., Lovett, A., Lockwood, K., & Wetzel, J. (2008). CogSketch: open-domain sketch understanding for cognitive science research and for education. In the Proceedings of the Fifth Eurographics Workshop on Sketch-Based Interfaces and Modeling. Annecy, FranceGoogle Scholar
  12. Forbus, K., Usher, J., Lovett, A., Lockwood, K., & Wetzel, J. (2011). CogSketch: sketch understanding for cognitive science research and for education. Topics in Cognitive Science, 3, 648–666.CrossRefGoogle Scholar
  13. Gentner, D., & Ratterman, M. J. (1991). Language and the career of similarity. In S. A. Gelman & J. P. Byrnes (Eds.), Perspectives on thought and language: interrelations in development (pp. 257–277). London: Cambridge University Press.Google Scholar
  14. Gentner, D., & Stevens, A. L. (Eds.). (1983). Mental models. Hillsdale, NJ: Erlbaum.Google Scholar
  15. Gobert, J. (2000). A typology of models for plate tectonics: inferential power and barriers to understanding. International Journal of Science Education, 22, 937–977.CrossRefGoogle Scholar
  16. Gobert, J., & Clement, J. J. (1999). Effects of student-generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching, 36, 39–53.CrossRefGoogle Scholar
  17. Gobet, F., Lane, P. C. R., Croker, S., Cheng, P. C. H., Jones, G., Oliver, I., et al. (2001). Chunking mechanisms in human learning. Trends in Cognitive Science, 5, 236–243.CrossRefGoogle Scholar
  18. Goel, V. (1995). Sketches of thought. Cambridge: MA: MIT Press.Google Scholar
  19. Hay, D. B., Williams, D., Stahl, D., & Wingate, R. J. (2013). Using drawings of the brain cell to exhibit expertise in neuroscience: exploring the boundaries of experimental culture. Science Education, 97(3), 468–491.CrossRefGoogle Scholar
  20. Heiser, J., & Tversky, B. (2006). Arrows in comprehending and producing mechanical diagrams. Cognitive Science, 30, 581–592.CrossRefGoogle Scholar
  21. Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 1, 127–138.CrossRefGoogle Scholar
  22. Holden, M., Curby, K., Newcombe, N., & Shipley, T. F. (2010). Spatial memory: hierarchical encoding of location in natural scenes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36(3), 590–604.Google Scholar
  23. Jee, B. D., & Wiley, J. (2007). How goals affect the organization and use of domain knowledge. Memory and Cognition, 35, 837–851.CrossRefGoogle Scholar
  24. Jee, B. D., Uttal, D. H., Gentner, D., Manduca, C., Shipley, T., Sageman, B., et al. (2010). Analogical thinking in geoscience education. Journal of Geoscience Education, 58, 2–13.CrossRefGoogle Scholar
  25. Jee, B. D., Uttal, D. H., Gentner, D., Manduca, C., & Shipley, T. (2013). Finding faults: analogical comparison supports spatial concept learning in geoscience. Cognitive Processing, 14(2), 175–187.CrossRefGoogle Scholar
  26. Kali, Y., & Orion, N. (1996). Spatial abilities of high school students in the perception of geologic structures. Journal of Research in Science Teaching, 33, 369–391.CrossRefGoogle Scholar
  27. Kastens, K. A., Agrawal, S., & Liben, L. (2009). How students and field geologists reason in integrating spatial observations from outcrops to visualize a 3-D geological structure. International Journal of Science Education, 31, 365–394.CrossRefGoogle Scholar
  28. Kindfield, A. (1992). Expert diagrammatic reasoning in biology. Paper presented at the AAAI Symposium on Reasoning with Diagrammatic Representations I, Stanford University.Google Scholar
  29. Kulik, J. A., & Kulik, C. C. (1988). Timing of feedback and verbal learning. Review of Educational Research, 58, 79–97.CrossRefGoogle Scholar
  30. Landy, D., & Goldstone, R. L. (2007). Formal notations are diagrams: evidence from a production task. Memory and Cognition, 35, 2033–2040.CrossRefGoogle Scholar
  31. Larkin, J., & Simon, H. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65–99.CrossRefGoogle Scholar
  32. Libarkin, J. C., & Brick, C. (2002). Research methodologies in science education: visualization and the geosciences. Journal of Geoscience Education, 50, 449–455.Google Scholar
  33. Liben, L. S., & Titus, S. J. (2012). The importance of spatial thinking for geoscience education: Insights from the crossroads of geoscience and cognitive science. Geological Society of America Special Papers, 86, 51–70.Google Scholar
  34. Lowe, R. K. (1993). Constructing a mental representation from an abstract technical diagram. Learning and Instruction, 3, 157–179.CrossRefGoogle Scholar
  35. Marshak, S. (2005). Earth: portrait of a planet. New York: W.W. Norton.Google Scholar
  36. Miller, B. W., Cromley, J. G., Newcombe, N. S., Chang, M. D., & Forbus, K. D. (2014). Supporting student science learning through sketching with on-demand feedback. Manuscript in preparation.Google Scholar
  37. Moss, J., Kotovsky, K., & Cagan, J. (2006). The role of functionality in the mental representations of engineering students: some differences in the early stages of expertise. Cognitive Science, 30(1), 65–93.CrossRefGoogle Scholar
  38. Pargas, R., Cooper, M., Williams, C., and Bryfczynski, S. (2007). Organicpad: A tablet pc based interactivity tool for organic chemistry. First International International Workshop on Pen-based Learning Technologies. (pp. 1-6). IEEE. Google Scholar
  39. Piburn, M.D., van der Hoeven Kraft, K., and Pacheco, H. (2011). A new century for geoscience education research. Paper presented at the Second Committee Meeting on the Status, Contributions, and Future Directions of Discipline-Based Education Research. Available: http://www.nationalacademies.org/bose/DBER_Piburn_October_Paper.pdf.
  40. Plimmer, B., & Freeman, I. (2007). A toolkit approach to sketched diagram recognition. Proceedings of the 21st British HCI Group Annual Conference on People and Computers: HCI…but not as we know it, (Vol. 1, 205–213). Swinton, UK: Computer Society.Google Scholar
  41. Plimmer, B., & Hammond, T. (2008). Getting started with sketch tools: a tutorial on sketch recognition tools. Lecture Notes in Computer Science, 5223, 9–12.CrossRefGoogle Scholar
  42. Proffitt, J. B., Coley, J. D., & Medin, D. L. (2000). Expertise and category-based induction. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 811–828.Google Scholar
  43. Schunn, C. D., & Anderson, J. R. (1999). The generality/specificity of expertise in scientific reasoning. Cognitive Science, 23, 337–370.CrossRefGoogle Scholar
  44. Schwamborn, A., Mayer, R. E., Thillmann, H., Leopold, C., & Leutner, D. (2010). Drawing as a generative activity and drawing as a prognostic activity. Journal of Educational Psychology, 102(4), 872–879.Google Scholar
  45. Shea, D. L., Lubinski, D., & Benbow, C. P. (2001). Importance of assessing spatial ability in intellectually talented young adolescents: a 20-year longitudinal study. Journal of Educational Psychology, 93(3), 604–614.CrossRefGoogle Scholar
  46. Shin, M. S., Park, S. Y., Park, S. R., Seol, S. H., & Kwon, J. S. (2006). Clinical and empirical applications of the Rey–Osterrieth Complex Figure Test. Nature Protocols, 1, 892–899.CrossRefGoogle Scholar
  47. Suwa, M., & Tversky, B. (1997). What architects and students perceive in their sketches: a protocol analysis. Design Studies, 18, 385–403.CrossRefGoogle Scholar
  48. Taylor, H. A., & Tversky, B. (1992). Descriptions and depictions of environments. Memory and Cognition, 20, 483–496.CrossRefGoogle Scholar
  49. Tversky, B. (1981). Distortions in memory for maps. Cognitive Psychology, 13, 407–433.CrossRefGoogle Scholar
  50. Tversky, B. (1991). Spatial mental models. In G. H. Bower (Ed.), The psychology of learning and motivation: advances in research and theory (Vol. 27, pp. 109–145). N.Y.: Academic Press.Google Scholar
  51. Tversky, B. (2002). What do sketches say about thinking? In the Proceedings of the AAAI Spring Symposium on Sketch Understanding, 148-151.Google Scholar
  52. Uttal, D. H., & Cohen, C. A. (2012). Spatial thinking and STEM education: when, why and how. Psychology of Learning and Motivation, 57, 147–181.CrossRefGoogle Scholar
  53. Uttal, D. H., Friedman, A., Hand, L. L., & Warren, C. (2010). Learning fine-grained and category information in navigable real-world space. Memory and Cognition, 38, 1026–1040.CrossRefGoogle Scholar
  54. Valentine, S., Vides, F., Lucchese, G., Turner, D., Kim, H., Li, W., Linsey J., & Hammond, T. (2012). Mechanix: A sketch-based tutoring system for statics courses. In Proceedings of AAAI. Google Scholar
  55. Van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: literature review and synthesis. Educational Psychology Review, 17(2), 285–325.CrossRefGoogle Scholar
  56. Vosniadou, S., & Brewer, W. F. (1992). Mental models of the earth: a study of conceptual change in childhood. Cognitive Psychology, 24, 535–585.CrossRefGoogle Scholar
  57. Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial ability for STEM domains: aligning over 50 years of cumulative psychological knowledge solidifies its importance. Journal of Educational Psychology, 101(4), 817.CrossRefGoogle Scholar
  58. Wetzel, J., & Forbus, K. (2012). Teleological representations for multi-modal design explanations. Proceedings of the 26th International Workshop on Qualitative Reasoning. Los Angeles, California.Google Scholar
  59. White, R. T., & Gunstone, R. F. (1992). Probing understanding. New York: Falmer Press.Google Scholar
  60. Yin, P., Forbus, K., Usher, J., Sageman, B., & Jee, B. D. (2010). Sketch Worksheets: A sketch-based educational software system. Proceedings of the 22 nd Annual Conference on Innovative Applications of Artificial Intelligence. Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Benjamin D. Jee
    • 5
  • Dedre Gentner
    • 1
  • David H. Uttal
    • 1
  • Bradley Sageman
    • 1
  • Kenneth Forbus
    • 1
  • Cathryn A. Manduca
    • 2
  • Carol J. Ormand
    • 2
  • Thomas F. Shipley
    • 3
  • Basil Tikoff
    • 4
  1. 1.Northwestern UniversityEvanstonUSA
  2. 2.Carleton CollegeNorthfieldUSA
  3. 3.Temple UniversityPhiladelphiaUSA
  4. 4.University of Wisconsin-MadisonMadisonUSA
  5. 5.Department of PsychologyRhode Island CollegeProvidenceUSA

Personalised recommendations