Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Research in Science Education Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. This accurately describes the interface used in these experiments; since then, the interface has been streamlined so that pressing a “start glyph” button is unnecessary, and users can merge or split their ink as they desire.

  2. In our coding scheme, each part of the sketch that contained an arrow was coded as a relational part. Although there are five arrows in the geoscience student’s sketch, the participant grouped the arrows into three spatially separated parts; thus, only three relational parts were counted. (Coding also took into account the labels given to the parts, so additional relational parts may be present in Fig. 5 in addition to those indicated in red.)

  3. The coefficient of agreement for mean ordering was lower than the median and modal values because one student sketched the events in perfect reverse order (Kendall’s W = 0). The median value of Kendall’s W was .75 and the mode was 1.0 for geoscience students who drew at least two causal events.

  4. Experiments 2 and 3 both showed a similar statistical interaction, i.e., an advantage for geoscience students on the geoscience images, but no difference between the groups on the control images. However, whereas in experiment 2 both groups produced a high proportion of relations on the control images, in experiment 3 both groups produced a low proportion of relations on the control images.

  5. We used CogSketch’s ordering of events but omitted events not considered to be key events.

  6. This pattern has also been observed in biology. Louis Gomez (personal communication, May 2012) reports that students who understand the photosynthesis process tend to copy diagrams of photosynthesis in causal order.

  7. CogSketch normally requires the user to categorize their glyphs as objects or relations, but to allow for more spontaneity, this feature was not used in this research.

References

  • Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333, 1096–1097.

    Article  Google Scholar 

  • Black, A. A. (2005). Spatial ability and earth science conceptual understanding. Journal of Geoscience Education, 53(4), 402–414.

    Google Scholar 

  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.

    Article  Google Scholar 

  • Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

    Article  Google Scholar 

  • Chi, M. T. H., Glaser, R., & Farr, M. (Eds.). (1988). The nature of expertise. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • 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.

  • 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 

  • Do, E. Y. (2005). Design sketches and sketch design tools. Knowledge Based Systems, 18, 383–405.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Forbus, K. (2011). Qualitative modeling. Wiley Interdisciplinary Reviews: Cognitive Science, 2(4), 374–391.

    Google Scholar 

  • 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, France

  • 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.

    Article  Google Scholar 

  • 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 

  • Gentner, D., & Stevens, A. L. (Eds.). (1983). Mental models. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Gobert, J. (2000). A typology of models for plate tectonics: inferential power and barriers to understanding. International Journal of Science Education, 22, 937–977.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Goel, V. (1995). Sketches of thought. Cambridge: MA: MIT Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Heiser, J., & Tversky, B. (2006). Arrows in comprehending and producing mechanical diagrams. Cognitive Science, 30, 581–592.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Jee, B. D., & Wiley, J. (2007). How goals affect the organization and use of domain knowledge. Memory and Cognition, 35, 837–851.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kindfield, A. (1992). Expert diagrammatic reasoning in biology. Paper presented at the AAAI Symposium on Reasoning with Diagrammatic Representations I, Stanford University.

  • Kulik, J. A., & Kulik, C. C. (1988). Timing of feedback and verbal learning. Review of Educational Research, 58, 79–97.

    Article  Google Scholar 

  • Landy, D., & Goldstone, R. L. (2007). Formal notations are diagrams: evidence from a production task. Memory and Cognition, 35, 2033–2040.

    Article  Google Scholar 

  • Larkin, J., & Simon, H. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11, 65–99.

    Article  Google Scholar 

  • Libarkin, J. C., & Brick, C. (2002). Research methodologies in science education: visualization and the geosciences. Journal of Geoscience Education, 50, 449–455.

    Google Scholar 

  • 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 

  • Lowe, R. K. (1993). Constructing a mental representation from an abstract technical diagram. Learning and Instruction, 3, 157–179.

    Article  Google Scholar 

  • Marshak, S. (2005). Earth: portrait of a planet. New York: W.W. Norton.

    Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

  • Plimmer, B., & Hammond, T. (2008). Getting started with sketch tools: a tutorial on sketch recognition tools. Lecture Notes in Computer Science, 5223, 9–12.

    Article  Google Scholar 

  • 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 

  • Schunn, C. D., & Anderson, J. R. (1999). The generality/specificity of expertise in scientific reasoning. Cognitive Science, 23, 337–370.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Suwa, M., & Tversky, B. (1997). What architects and students perceive in their sketches: a protocol analysis. Design Studies, 18, 385–403.

    Article  Google Scholar 

  • Taylor, H. A., & Tversky, B. (1992). Descriptions and depictions of environments. Memory and Cognition, 20, 483–496.

    Article  Google Scholar 

  • Tversky, B. (1981). Distortions in memory for maps. Cognitive Psychology, 13, 407–433.

    Article  Google Scholar 

  • 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 

  • Tversky, B. (2002). What do sketches say about thinking? In the Proceedings of the AAAI Spring Symposium on Sketch Understanding, 148-151.

  • Uttal, D. H., & Cohen, C. A. (2012). Spatial thinking and STEM education: when, why and how. Psychology of Learning and Motivation, 57, 147–181.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Vosniadou, S., & Brewer, W. F. (1992). Mental models of the earth: a study of conceptual change in childhood. Cognitive Psychology, 24, 535–585.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wetzel, J., & Forbus, K. (2012). Teleological representations for multi-modal design explanations. Proceedings of the 26th International Workshop on Qualitative Reasoning. Los Angeles, California.

  • White, R. T., & Gunstone, R. F. (1992). Probing understanding. New York: Falmer Press.

  • 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.

Download references

Author information

Authors and Affiliations

  1. Northwestern University, Evanston, IL, 60208, USA

    Dedre Gentner, David H. Uttal, Bradley Sageman & Kenneth Forbus

  2. Carleton College, Northfield, MN, 55057, USA

    Cathryn A. Manduca & Carol J. Ormand

  3. Temple University, Philadelphia, PA, 19122, USA

    Thomas F. Shipley

  4. University of Wisconsin-Madison, Madison, WI, 53706, USA

    Basil Tikoff

  5. Department of Psychology, Rhode Island College, 600 Mount Pleasant Avenue, Providence, RI, 02908, USA

    Benjamin D. Jee

Authors
  1. Benjamin D. Jee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dedre Gentner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David H. Uttal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bradley Sageman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kenneth Forbus
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Cathryn A. Manduca
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Carol J. Ormand
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Thomas F. Shipley
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Basil Tikoff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benjamin D. Jee.

Additional information

Author Note

The authors thank Jack Butler, Nathaniel Goldin-Meadow, Katherine James, and Nadeeka Dias for assistance with data collection and coding, and Jeffrey Usher for technical assistance with CogSketch. This research was supported by NSF grant SBE-0541957, the Spatial Intelligence and Learning Center (SILC).

Appendix

Appendix

Table 4 Image lists for the experiments
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jee, B.D., Gentner, D., Uttal, D.H. et al. Drawing on Experience: How Domain Knowledge Is Reflected in Sketches of Scientific Structures and Processes. Res Sci Educ 44, 859–883 (2014). https://doi.org/10.1007/s11165-014-9405-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11165-014-9405-2

Keywords

Search

Navigation