The Role of Craft-Based Knowledge in the Design of Dynamic Visualizations

  • Jodie Jenkinson


The profession of scientific animation is relatively new, deriving many of its visualization strategies from the practice-based heuristics of medical and scientific illustration, and also from the mainstream film and animation industries. The design of dynamic visualizations involves an elaborate decision-making process with respect to the framing of the narrative, what details to include or exclude, where, when, and how to focus attention, and how to visually represent concepts where the evidence may be lacking or is more hypothetical in nature. Artistic license plays a significant role in this process. It may be used to fill in knowledge gaps when information is missing or unknown. It can also serve the purpose of engaging a difficult-to-reach audience. With recent advances in technology, including the availability of low-cost consumer-level animation software, our enthusiasm for this medium has reached an all-time high. Yet, while we perceive the potential educational value of animations to be great, this is not borne out by the research assessing the impact of animation upon learning. In order to bridge the gap between research (both scientific and educational) and practice we need to engage both communities in a dialogue aimed at wider dissemination of findings, generating additional research perspectives, and putting evidence into effective practice.


Dynamic Visualization Scientific Visualization Visualization Strategy Animation Software Scientific Subject Matter 
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.



The author is grateful for the generous contribution of illustrated works by graduate students and faculty in Biomedical Communications at University of Toronto. Research highlighted here is supported in part by grants NSF #DUE- 1220512 from the National Science Foundation (USA) and SSHRC #SIG-13/14 from the Social Sciences and Humanities Research Council (CAN).


  1. Ainsworth, S. (2008). How do animations influence learning? In D. H. Robinson & G. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning (pp. 37–67). Charlotte, NC: Information Age Publishing.Google Scholar
  2. Betrancourt, M., & Chassot, A. (2008). Making sense of animation. In R. K. Lowe & W. Schnotz (Eds.), Learning with animations: Research implications for design (pp. 141–164). New York: Cambridge University Press.Google Scholar
  3. Block, B. (2008). The visual story: Creating the visual structure of film, tv and digital media (2nd ed.). Burlington, MA: Focal Press/Elsevier.Google Scholar
  4. Boucheix, J.-M., Lowe, R. K., Putri, D. K., & Groff, J. (2013). Cueing animations: Dynamic signaling aids information extraction and comprehension. Learning and Instruction, 25, 71–84.CrossRefGoogle Scholar
  5. Crosby, R. W., & Cody, J. (1991). Max Brödel, the man who put art into medicine. New York: Springer.Google Scholar
  6. De Koning, B. B., & Jarodzka, H. (2017). Attention guidance strategies for supporting learning from dynamic visualizations. In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization—Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  7. De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2009). Towards a framework for attention cueing in instructional animations: Guidelines for research and design. Educational Psychology Review, 21, 113–140.CrossRefGoogle Scholar
  8. De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2011). Attention cueing in an instructional animation: The role of presentation speed. Computers in Human Behavior, 27, 41–45.CrossRefGoogle Scholar
  9. Fischer, S., Lowe, R. K., & Schwan, S. (2008). Effects of presentation speed of a dynamic visualization on the understanding of a mechanical system. Applied Cognitive Psychology, 22, 1126–1141.CrossRefGoogle Scholar
  10. Fischer, S., & Schwan, S. (2010). Comprehending animations: Effects of spatial cueing versus temporal scaling. Learning and Instruction., 20, 465–475.CrossRefGoogle Scholar
  11. Goodsell, D. S. (2010). The machinery of life (2nd ed.). New York: Copernicus Books Springer.Google Scholar
  12. Goodsell, D. S., & Johnson, G. T. (2007). Filling in the gaps: Artistic license in education and outreach. PLoS Biology, 5(12), e308.CrossRefGoogle Scholar
  13. Hodges, E. (Ed.). (2003). The guild handbook of scientific illustration (2nd ed.). Hoboken, NJ: Wiley.Google Scholar
  14. Iwasa, J. H. (2010). Animating the model figure. Trends in Cell Biology, 20, 699–704.CrossRefGoogle Scholar
  15. Jantzen, S. G., Jenkinson, J., & McGill, G. (2015). Transparency in film: Increasing credibility of scientific animation using citation. Nature Methods, 12, 293–297.CrossRefGoogle Scholar
  16. Jastrzebski, Z. (1985). Scientific illustration: A guide for the beginning artist. Englewood Cliffs, NJ: Prentice-Hall, Inc.Google Scholar
  17. Jenkinson, J., & McGill, G. (2012). Visualizing protein interactions and dynamics: Evolving a visual language for molecular animation. CBE–Life Sciences Education, 11, 103–110.CrossRefGoogle Scholar
  18. Johnson, G. T., & Hertig, S. (2014). A guide to the visual analysis and communication of biomolecular structural data. Nature Reviews Molecular Cell Biology, 15, 690–698.CrossRefGoogle Scholar
  19. Lewalter, D. (2003). Cognitive strategies for learning from static and dynamic visuals. Learning and Instruction, 13, 177–189.CrossRefGoogle Scholar
  20. Linn, M. C., Chang, H. Y., Chiu, J. L., Zhang, H. Z., & McElhaney, K. (2010). Can desirable difficulties overcome deceptive clarity in scientific visualizations? In A. Benjamin (Ed.), Successful remembering and successful forgetting: A festschrift in honor of Robert A. Bjork (pp. 239–262). New York: Routledge.Google Scholar
  21. Lowe, R. K. (2003). Animation and learning: Selective processing of information in dynamic graphics. Learning and Instruction, 13, 157–176.CrossRefGoogle Scholar
  22. Lowe, R., Schnotz, W., & Rasch, T. (2011). Aligning affordances of graphics with learning task requirements. Applied Cognitive Psychology, 25, 452–459.CrossRefGoogle Scholar
  23. Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University Press.CrossRefGoogle Scholar
  24. McCloud, S. (1993). Understanding comics. New York: Kitchen Sink Press/Harper Collins.Google Scholar
  25. McGill, G. (2008). Molecular movies… Coming to a lecture near you. Cell, 133, 1127–1132.CrossRefGoogle Scholar
  26. McGill, G. (2017). Designing instructional science visualizations in the trenches: Where research meets production reality. In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization—Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  27. Rieber, L. (1989). The effects of computer animated elaboration strategies and practice on factual and application learning in an elementary science lesson. Journal of Educational Computing Research, 5, 431–444.CrossRefGoogle Scholar
  28. Rieber, L., & Hannafin, M. J. (1988). Effects of textual and animated orienting activities and practice on learning from computer-based instruction. Computers in Schools, 5(1/2), 77–89.CrossRefGoogle Scholar
  29. Sanger, M., & Greenbowe, T. J. (2000). Addressing student misconceptions concerning electron flow in aqueous solutions with instruction including computer animations and conceptual change strategies. International Journal of Science Education, 22, 521–537.CrossRefGoogle Scholar
  30. Schnotz, W., & Lowe, R. K. (2008). A unified view of learning from animated and static graphics. In R. K. Lowe & W. Schnotz (Eds.), Learning with animations: Research implications for design (pp. 49–68). New York: Cambridge University Press.Google Scholar
  31. Schwan, S., & Papenmeier, F. (2017). Learning from animations: From 2d to 3d? In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization—Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  32. Sharpe, J., Lumsden, C., & Woolridge, N. (2008). In silico: 3D animation and simulation of cell biology with Maya and MEL. New York: Morgan Kaufmann.Google Scholar
  33. Thomas, F., & Johnston, O. (1981). The illusion of life: Disney animation. New York: Hyperion.Google Scholar
  34. Tufte, E. (1997). Visual explanations. Cheshire, CT: Graphics Press Inc.Google Scholar
  35. Tversky, B., Heiser, J., MacKenzie, R., Lozano, S., & Morrison, J. B. (2008). Enriching animations. In R. K. Lowe & W. Schnotz (Eds.), Learning with animations: Research implications for design (pp. 263–285). New York: Cambridge University Press.Google Scholar
  36. Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human-Computer Studies, 57, 247–262.CrossRefGoogle Scholar
  37. Wagner, I., & Schnotz, W. (2017). Learning from static and dynamic visualizations: What kind of questions should we ask? In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization—Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  38. Ware, C. (2004). Information visualization: Perception for design. San Francisco: Morgan Kaufmann.Google Scholar
  39. Wilson-Pauwels, L. (1997). Bringing it into focus: Visual cues and their role in directing attention. Journal of Biocommunication, 24(3), 12–16.Google Scholar
  40. Wood, P. (1994). Scientific illustration (2nd ed.). New York: Van Nostrand Reinhold.Google Scholar
  41. Woolridge, N. (2012). To cut, or not to cut. Journal of Biocommunication, 38(1), 27–30.Google Scholar
  42. Woolridge, N. (2013). Drawing a line in the mind: Some reflections on the fundamental nature of linear depiction. Journal of Biocommunication, 39(1), 26–31.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of Toronto MississaugaMississaugaCanada

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