Introduction to Multiple Representations: Their Importance in Biology and Biological Education

  • Chi-Yan Tsui
  • David F. Treagust
Part of the Models and Modeling in Science Education book series (MMSE, volume 7)


This chapter introduces the theoretical perspectives associated with multiple external representations (MERs) (Ainsworth, Comput Educ, 33(2/3):131–152, 1999) and their importance in biology and biological education. We first review Ainsworth’s functional taxonomy of MERs and the related literature in this area of research. Next, we propose a theoretical cube model to examine and interpret the major themes and theoretical positions of the chapters in this volume, discussing some examples to illustrate our arguments. We conclude by examining the pedagogical functions that MERs can take on giving new meanings—as biology goes from in vivo and in vitro to an in silico research culture and practice using a systems approach to solving biology-based global problems (National Research Council, A new biology for the 21st century, National Academic Press, Washington, DC, 2009)—and the concomitant changes that can be made to improve biological education for the twenty-first century.


Biological Knowledge Multiple Representation External Representation Cube Model Pedagogical Function 
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.



We would like to thank John Gilbert, the editor of the series Models and Modeling in Science Education, for his valuable and critical comments and suggestions on an earlier draft of this chapter. We are grateful to Kathleen Fisher, Anat Yarden, Kristy Halverson, and Shaaron Ainsworth, who reviewed the final draft of this chapter. Their comments and suggestions—particularly those from Shaaron Ainsworth whose theoretical framework we have adopted for this volume—have undoubtedly enabled us to improve the quality of this chapter. Any remaining inadequacies are ours.


  1. Ainsworth, S. (1999). The functions of multiple representations. Computers in Education, 33(2/3), 131–152.CrossRefGoogle Scholar
  2. Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 15(3), 183–198.CrossRefGoogle Scholar
  3. Ainsworth, S. (2008a). The educational value of multiple-representations when learning complex scientific concepts. In J. K. Gilbert, M. Reiner, & M. Nakhleh (Eds.), Visualization: Theory and practice in science education (pp. 191–208). London: Springer.CrossRefGoogle Scholar
  4. Ainsworth, S. (2008b). How should we evaluate multimedia learning environments? In J.-F. Rouet, R. Lowe, & W. Schnotz (Eds.), Understanding multimedia documents (pp. 249–265). Dordrecht, The Netherlands: Springer Science & Business Media.CrossRefGoogle Scholar
  5. Aubusson, P., Harrison, A. G., & Ritchie, S. M. (Eds.). (2006). Metaphor and analogy in science education. Dordrecht, The Netherlands: Springer.Google Scholar
  6. Biological Sciences Curriculum Study. (2006). BSCS Biology: A human approach (teacher guide) (3rd ed.). Dubuque, IA: Kendall/Hunt.Google Scholar
  7. Buckley, C. B. (2000). Interactive multimedia and model-based learning in biology. International Journal of Science Education, 22(9), 895–935.CrossRefGoogle Scholar
  8. Clement, J. J., & Rae-Mamirez, M. A. (Eds.). (2008). Model based learning and instruction in science. Dordrecht, The Netherlands: Springer.Google Scholar
  9. Cook, M., Wiebe, E., & Carter, G. (2008). The influence of prior knowledge on viewing and interpreting graphics with macroscopic and molecular representations. Science Education, 92(5), 848.CrossRefGoogle Scholar
  10. Dagher, Z. R. (1994). Does the use of analogies contribute to conceptual change? Science Education, 78(6), 601–614.CrossRefGoogle Scholar
  11. de Jong, T., Ainsworth, S. E., Dobson, M., van der Hulst, A., Levonen, J., Reimann, P., et al. (1998). Acquiring knowledge in science and mathematics: The use of multiple representations in technology-based learning environments. In M. W. van Someren, P. Reimann, H. P. A. Boshuizen, & T. de Jong (Eds.), Learning with multiple representations (pp. 9–40). London: Elsevier Science.Google Scholar
  12. Geig, P., & Rubba, P. (1993). Translation of representations of the structure and the relationship to reasoning, gender, spatial reasoning, and specific prior knowledge. Journal of Research in Science Teaching, 30(8), 883–903.CrossRefGoogle Scholar
  13. Gentner, D., & Stevens, A. L. (Eds.). (1983). Mental models. Hillsdale, NJ: Lawrence ERlbaum Associates.Google Scholar
  14. Gilbert, J. K., & Boulter, C. J. (1998). Learning science through models and modelling. In B. J. Fraser (Ed.), International handbook of science education (pp. 53–66). Dordrecht, The Netherlands: Kluwer.CrossRefGoogle Scholar
  15. Gilbert, J. K., Reiner, M., & Nakhleh, M. (Eds.). (2008). Visualization: Theory and practice in science education. New York/London: Springer.Google Scholar
  16. Gilbert, J. K., & Treagust, D. (Eds.). (2009). Multiple representations in chemical education. Dordrecht, The Netherlands: Springer.Google Scholar
  17. Hermann, P., Waxman, S. R., & Mewdin, D. L. (2010). Anthropocentrism is not the first step in children’s reasoning about the natural world. Proceedings of the National Academy of Sciences of the United States of America, 107(22), 9979–9984.CrossRefGoogle Scholar
  18. Jaipal, K. (2010). Meaning making through multiple modalities in a biology classroom: A multimodal semiotics discourse analysis. Science Education, 94(1), 48–72.Google Scholar
  19. Johnstone, A. H. (1982). Macro and micro chemistry. School Science Review, 19(3), 71–73.Google Scholar
  20. Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7, 75–83.CrossRefGoogle Scholar
  21. Kings, N. J., Davies, J., Verrill, D., Aral, S., Brynjolfsson, E., & van Alstyne, M. (2008). Social networks, social computing and knowledge management. In P. Warren, J. Davies, & D. Brown (Eds.), ICT futures: Delivering pervasive, real-time and secure services (pp. 17–26). West Sussex, UK: Wiley.Google Scholar
  22. Lemke, J. L. (1990). Talking science: Language, learning, and values. Norwood, NJ: Ablex Publishing Corporation.Google Scholar
  23. 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/New York: Routledge.Google Scholar
  24. Ligorioa, M., Izzotti, A., Pulliero, A., & Arrigoc, P. (2011). Mutagens interfere with microRNA maturation by inhibiting DICER: An in silico biology analysis. Mutation Research, 717, 116–128.CrossRefGoogle Scholar
  25. Marbach-Ad, G., & Stavy, R. (2000). Students’ cellular and molecular explanations of genetic phenomena. Journal of Biological Education, 34(4), 200–205.CrossRefGoogle Scholar
  26. Martins, I., & Ogborn, J. (1997). Metaphorical reasoning about genetics. International Journal of Educational Research, 19(6), 48–63.Google Scholar
  27. Meijer, M. R., Bulte, A. M. W., & Pilot, A. (2009). Structure–property relations between macro and micro representations: Relevant meso-levels in authentic tasks. In J. K. Gilbert & D. Treagust (Eds.), Multiple representations in chemical education (pp. 195–213). Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
  28. National Research Council [NRC]. (2009). A new biology for the 21st century. Washington, DC: National Academic Press.Google Scholar
  29. Paivio, A. (1986). Mental representations: A dual coding approach. New York: Oxford University Press.Google Scholar
  30. Palsson, B. (2000). The challenges of in silico biology. Nature Biotechnology, 18, 1147–1150.CrossRefGoogle Scholar
  31. Pozzer, L. L., & Roth, W.-M. (2003). Prevalence, structure, and functions of photographs in high school biology textbooks. Journal of Research in Science Teaching, 40(10), 1089–1114.CrossRefGoogle Scholar
  32. Rodrigo, G., Carrera, J., & Elena, S. (2010). Network design meets in silico evolutionary biology. Biochimie, 92, 746–752.CrossRefGoogle Scholar
  33. Spiro, R. J., Feltovich, P. J., Coulson, R. L., & Anderson, D. K. (1989). Multiple analogies for complex concepts: Antidotes for analogy-induced misconception in advanced knowledge acquisition. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 498–531). Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  34. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4, 295–312.CrossRefGoogle Scholar
  35. Taber, K. S. (2009). Learning at the symbolic level. In J. K. Gilbert & D. Treagust (Eds.), Multiple representations in chemical education (pp. 75–105). Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
  36. Treagust, D. F., Harrison, A. G., & Venville, G. J. (1998). Teaching science effectively with analogies: An approach for preservice and inservice teacher education. Journal of Science Teacher Education, 9(2), 85–101.CrossRefGoogle Scholar
  37. Tsui, C.-Y., & Treagust, D. F. (2003). Genetics reasoning with multiple external representations. Research in Science Education, 33(1), 111–135.CrossRefGoogle Scholar
  38. Tsui, C.-Y., & Treagust, D. F. (2007). Understanding genetics: Analysis of secondary students’ conceptual status. Journal of Research in Science Teaching, 44(2), 205–235.CrossRefGoogle Scholar
  39. Tsui, C.-Y., & Treagust, D. F. (2010). Evaluating secondary students’ scientific reasoning in genetics using a two-tier diagnostic instrument. International Journal of Science Education, 32(8), 1073–1098.CrossRefGoogle Scholar
  40. van der Meij, J., & de Jong, T. (2011). The effects of directive self-explanation prompts to support active processing of multiple representations in a simulation-based learning environment. Journal of Computer Assisted Learning, 27, 411–423.CrossRefGoogle Scholar
  41. van Someren, M. W., Reimann, P., Boshuizen, H. P. A., & de Jong, T. (Eds.). (1998). Learning with multiple representations. London: Pergamon.Google Scholar
  42. Vollmer, G. (1984). Mesocosm and objective knowledge. In F. M. Wuketits (Ed.), Concepts and approaches in evolutionary epistemology (pp. 69–121). Dordrecht, The Netherlands: D. Reidel Publishing Company.Google Scholar
  43. von Baeyer, H. C. (2003). Information: The new language of science. Cambridge, MA: Harvard University Press.Google Scholar
  44. Waldrip, B., Prain, V., & Carolan, J. (2010). Using multi-modal representations to improve learning in junior secondary science. Research in Science Education, 40(1), 65–80.CrossRefGoogle Scholar
  45. Werner, E. (2003). In silico multicellular systems biology and minimal genomes. Drug Discovery Today, 8(24), 1121–1127.CrossRefGoogle Scholar
  46. White, T., & Pea, R. (2011). Distributed by design: On the promises and pitfalls of collaborative learning with multiple representations. The Journal of the Learning Sciences, 20(3), 489–547.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2013

Authors and Affiliations

  1. 1.Science and Mathematics Education CentreCurtin UniversityPerthAustralia

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