Improving Students’ Representational Competence through a Course-Based Undergraduate Research Experience

  • Chandrani Mishra
  • Kari L. Clase
  • Carrie Jo Bucklin
  • Kristy L. Daniel
Part of the Models and Modeling in Science Education book series (MMSE, volume 11)


Visual representations are integral for communicating abstract science concepts and promoting insights for new scientific discoveries. Students’ representational competence is positively correlated with problem solving in the sciences and subsequent improvements in academic performance. We explored how students’ participation in the authentic practice of science with the use of visual representations would impact representational competence. We further tested a model of representational competence to understand how the use of student-generated representations in a Course-Based Undergraduate Research Experience (CURE) revealed undergraduate thinking about biological content, scientific literacy and the process of science. In this mixed methods study, we found that the applied theoretical framework could be used to effectively describe students’ representational competence. We observed all seven levels of representational competence with annotated genomes. In this chapter, we present rich descriptions of each level, including connections between content and scientific practice revealed by analysis of 147 student-generated representations. Additionally, we found that students’ competencies significantly improved after participation in the CURE. Our framework to examine representational competence can be used as a novel way to reveal changes in scientific thinking and examine the impact of undergraduate research experiences.


Biotechnology Genome Representational competence Science education Undergraduate education 



This research and interdisciplinary collaboration was supported in part by a visionary Grant from the Gordon Research Conference on Visualization in Science and Education (2009), the National Institute of General Medical Sciences from the National Institutes of Health, Howard Hughes Medical Institute, the University Grants Development Program and University, Biotechnology Innovation and Regulatory Science Center, Polytechnic Institute, Department of Agricultural and Biological Engineering, Department of Technology Leadership and Innovation, Purdue University. We would also like to thank the Authors Research labs for all of their work to make this possible.


  1. Anderson, T. R., Schönborn, K. J., du Plessis, L., Gupthar, A. S., Hull, T. L. (2012). Identifying and developing students’ ability to reason with concepts and representations in biology. In D.F. Treagust & C. Tsui (Eds.), Multiple representations in biological education. (pp. 19–38). doi: CrossRefGoogle Scholar
  2. Baetu, T. M. (2012). Genomic programs as mechanism schemas: A non-reductionist interpretation. British Journal for the Philosophy of Science, 63, 649–671.CrossRefGoogle Scholar
  3. Baum, D. A., Smith, S. D., & Donovan, S. S. S. (2005). The tree-thinking challenge. Science, 310, 979–980.CrossRefGoogle Scholar
  4. Chi, M. T. H., Feltovich, P. J., & Glasner, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.CrossRefGoogle Scholar
  5. Cuoco, A. A., & Curcio, F. R. (2001). The roles of representation in school mathematics. National Council of Teachers.Google Scholar
  6. Dees, J., Momsen, J. L., Niemi, J., & Montplaisir, L. (2014). Student interpretations of phylogenetic trees in an introductory biology course. CBE-Life Sciences Education, 13(4), 666–676.CrossRefGoogle Scholar
  7. Dikmenli, M., Cardak, O., & Kiray, S.A. (2011). Science student teachers’ ideas of the concept ‘gene’. In annual meeting of the 3rd world conference on educational sciences, Istanbul, Turkey.Google Scholar
  8. Driver, R., Squires, A., Rushworth, P., & Woods-Robinson, V. (1994). Making sense of secondary science: Research into children’s ideas. London: Routledge.Google Scholar
  9. Ferk, B., Vrtacnik, M., Blejec, A., & Gril, A. (2003). Students understanding of molecular structure representations. International Journal of Science Education, 25, 1227–1245.CrossRefGoogle Scholar
  10. Fogle, T. (2001). The dissolution of protein coding genes in molecular biology. In P. Beurton, R. Falk, & H.-J. Rheinberger (Eds.), The concept of the gene in development and evolution. Cambridge: Cambridge University Press.Google Scholar
  11. Gericke, N. M., & Hagberg, M. (2007). Definition of historical models of gene function and their relation to student’s understanding of genetics. Science & Education, 16, 849–881.CrossRefGoogle Scholar
  12. Gerstein, M. B., Bruce, C., Rozowsky, J. S., Zheng, D., Du, J., Korbel, J. O., Emanuelsson, O., Zhang, Z. D., Weissman, S., & Snyder, M. (2007). What is a gene, post-ENCODE? History and updated definition. Genome Research, 17, 669–681.CrossRefGoogle Scholar
  13. Gilbert, J. K. (2005a). Visualization: A metacognitive skill in science and science education. In J. K. Gilbert (Ed.), Visualization in science education (pp. 9–27). Dordrecht: Springer.CrossRefGoogle Scholar
  14. Gilbert, J. K. (2005b). Visualizations in science education (Vol 1). Dordrecht: Springer.CrossRefGoogle Scholar
  15. Gilbert, J. K., & Treagust, D. (Eds.). (2009). Multiple representations in chemical education (Vol. 4). Dordrecht: Springer.Google Scholar
  16. Griffard, P. B. (2013). Deconstructing and decoding complex process diagrams in university biology. In D. Treagust & C.-Y. Tsui (Eds.), Multiple representations in biology education (chapter 10). Dordrecht: Springer.Google Scholar
  17. Griffiths, P. E., & Neumann-Held, E. M. (1999). The many faces of the gene. Bioscience, 49(8), 656–662.CrossRefGoogle Scholar
  18. Halverson, K. L. (2010). Using pipe cleaners to bring the tree of life to life. The American Biology Teacher, 72(4), 223–224.CrossRefGoogle Scholar
  19. Halverson, K. L. (2011). Improving tree-thinking one learnable skill at a time. Evolution: Education and Outreach, 4(1), 95–106.Google Scholar
  20. Halverson, K. L., & Friedrichsen, P. (2013). Learning tree thinking: Developing a new framework of representational competence. In Multiple. representations in biological education (pp. 185–201). Dordrecht: Springer Netherlands.CrossRefGoogle Scholar
  21. Halverson, K. L., Pires, C. J., & Abell, S. K. (2011). Exploring the complexity of tree thinking expertise in an undergraduate systematics course. Science Education, 95(5), 794–823.CrossRefGoogle Scholar
  22. Harle, M., & Towns, M. (2010). A review of spatial ability literature, its connection to chemistry, and implications for instruction. Journal of Chemical Education, 88(3), 351–360.CrossRefGoogle Scholar
  23. Harle, M., & Towns, M. H. (2012). Students’ understanding of external representations of the potassium ion channel protein part II: Structure–function relationships and fragmented knowledge. Biochemistry and Molecular Biology Education, 40(6), 357–363.CrossRefGoogle Scholar
  24. Harle, M., & Towns, M. H. (2013). Students’ understanding of primary and secondary protein structure: Drawing secondary protein structure reveals student understanding better than simple recognition of structures. Biochemistry and Molecular Biology Education, 41(6), 369–376.CrossRefGoogle Scholar
  25. Harrison, M., Dunbar, D., Ratmansky, L., Boyd, K., & Lopatto, D. (2011). Classroom-based science research at the introductory level: Changes in career choices and attitude. CBE-Life Sciences Education, 10(3), 279–286.CrossRefGoogle Scholar
  26. Jordan, T. C., Burnett, S. H., Carson, S., Caruso, S. M., Clase, K., DeJong, R. J., et al. (2014). A broadly implementable research course in phage discovery and genomics for first-year undergraduate students. MBio, 5(1), e01051–e01013.CrossRefGoogle Scholar
  27. Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Visualization in science education (pp. 121–145). Dordrecht: Springer.CrossRefGoogle Scholar
  28. Kozma, R., & Russell, J. (2007). Modelling students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Visualization in science education (pp. 147–168). Dordrecht: Springer.Google Scholar
  29. Lewis, J., Leach, J., & Wood-Robinson, C. (2000). All in the genes? – Young people’s understanding of the nature of genes. Journal of Biological Education, 34, 74–79.CrossRefGoogle Scholar
  30. Mathewson, J. H. (1999). Visual-spatial thinking: An aspect of science overlooked by educators. Science Education, 83, 33–54.CrossRefGoogle Scholar
  31. Matuk, C. (2007). Images of evolution. Journal of Biocommunication, 33(3), E54–E61.Google Scholar
  32. Meyer, M. R. (2001). Representation in realistic mathematics education. In A. A. Cuoco (Ed.), The roles of representation in school mathematics (2001 yearbook) (pp. 238–250). Reston: National Council of Teachers in Mathematics.Google Scholar
  33. National Research Council. (1996). National science education standards. National science education standards: National Academy Press.Google Scholar
  34. Patrick, M. D., Carter, G., & Wiebe, E. N. (2005). Visual representations of DNA replication: Middle grades students’ perceptions and interpretations. Journal of Science Education and Technology, 14, 353–365.CrossRefGoogle Scholar
  35. Peterson, M. P. (1994). Cognitive issues in cartographic visualization. In A. M. MacEachren & D. R. F. Taylor (Eds.), Visualization in modern cartography (pp. 27–43). Oxford: Pergamon.CrossRefGoogle Scholar
  36. Pruitt, K. D., Tatusova, T., Brown, G. R., & Maglott, D. R., (2011). NCBI reference sequences (RefSeq): Current status, new features and genome annotation policy. Nucleic Acids Research, Advance Access, 1–6.CrossRefGoogle Scholar
  37. Rheinberger, H.-J., & Muller-Wille, S. (2008). Gene concepts. In S. Sahotra & A. Plutynski (Eds.), A companion to the philosophy of biology (pp. 3–21). Oxford: Blackwell Publishing.Google Scholar
  38. Roth, W.-M., Bowen, G. M., & McGinn, M. K. (1999). Differences in graph-related practices between high school biology textbooks and scientific ecology journals. Journal of Research in Science Teaching, 36, 977–1019.CrossRefGoogle Scholar
  39. Rutherford, J. F., & Ahlgren, A. (1990). Science for all Americans. New York: Oxford University Press.Google Scholar
  40. Schönborn, K. J., & Bögeholz, S. (2013). Experts’ views on translation across multiple external representations in acquiring biological knowledge about ecology, genetics, and evolution. In Multiple representations in biological education (p. 126). Springer Netherlands.Google Scholar
  41. Shaer, O., Kol, G., Strait, M., Fan, C., Grevet, C., & Elfenbein, S. (2010). G-nome surfer: A tabletop interface for collaborative exploration of genomic data. In Proceedings of human factors in computing systems (1427–1436). New York: ACM Press.Google Scholar
  42. Shaer, O., Strait, M., Valdes, C., Wang, H., Fend, T., Lintz, M., Ferreirae, M., Grote, C., Tempel, K., & Liu, S. (2012). The design, development, and deployment of a tabletop interface for collaborative exploration of genomic data. International Journal of Human-Computer Studies, 70(10), 746–764.CrossRefGoogle Scholar
  43. Shepard, R. (1988). The imagination of the scientist. In K. Egan & D. Nadaner (Eds.), Imagination and education (pp. 153–185). New York: Teachers’ College Press.Google Scholar
  44. Singer, S. R., Nielsen, N. R., & Schweingruber, H. A. (Eds.). (2014). Discipline-based education research: Understanding and improving learning in undergraduate science and engineering. Washington, D.C.: National Academies Press.Google Scholar
  45. Sterk, P., Kersey, P. J., & Apweiler, R. (2006). Genome reviews: Standardizing content and representation of information about complete genomes. OMICS: A Journal of Integrative Biology, 10(2), 114–118.CrossRefGoogle Scholar
  46. Stieff, M., Bateman, R. C., Jr., & Uttal, D. H. (2005). Teaching and learning with three dimensional representations. In J. K. Gilbert (Ed.), Visualization in science education (pp. 93–118). Netherlands: Springer.CrossRefGoogle Scholar
  47. Stotz, K., Griffiths, P. E., & Knight, R. (2004). How biologists conceptualize genes: An empirical study. Studies in History and Philosophy of Science Part C., 35(4), 647–673.CrossRefGoogle Scholar
  48. Takayama, K. (2005). Visualizing the science of genomics. In J. K. Gilbert (Ed.), Visualization in science education (pp. 217–252). Netherlands: Springer.CrossRefGoogle Scholar
  49. Trumbo, J. (1999). Visual literacy and science communication. Science Communication, 20(4), 409–425.CrossRefGoogle Scholar
  50. Tytler, R., Prain, V., Hubber, P., & Waldrip, B. (Eds.). (2013). Constructing representations to learn in science.New York:Springer Science & Business Media.Google Scholar
  51. Waldrip, B., & Prain, V. (2012). Learning from and through representations in science. In B. J. Fraser, K. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 145–155). Dordrecht: Springer.CrossRefGoogle Scholar
  52. Waters, C. K. (1994). Genes made molecular. Philosophy of Science, 61, 163–185.CrossRefGoogle Scholar
  53. Won, M., Yoon, H., & Treagust, D. F. (2014). Students learning strategies with multiple representations: Explanations of the human breathing mechanism. Science Education, 98(5), 840–866.CrossRefGoogle Scholar
  54. Yore, L. D., & Hand, B. (2010). Epilogue: Plotting a research agenda for multiple representations, multiple modality, and multimodal representational competency. Research in Science Education, 40(1), 93–101.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Chandrani Mishra
    • 1
  • Kari L. Clase
    • 1
  • Carrie Jo Bucklin
    • 2
  • Kristy L. Daniel
    • 3
  1. 1.Purdue UniversityWest LafayatteUSA
  2. 2.Southern Utah UniversityCedar CityUSA
  3. 3.Texas State UniversitySan MarcosUSA

Personalised recommendations