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Designing Instructional Science Visualizations in the Trenches: Where Research Meets Production Reality

  • Gaël G. McGill
Chapter

Abstract

Research-based design methods for dynamic visualizations can be successfully implemented in practice only if they take into consideration the pressures of ‘real world’ projects and integrate the vagaries of client-designer communications. One must take into account both the complicated power dynamics and numerous variables inherent to the relationship and dialogue between those who commission or control the creation of instructional media and those who produce it. From a researcher’s standpoint, there are myriad opportunities with which studies in human perception, cognitive psychology and educational research can inform and improve the design of dynamic visualizations. Despite these opportunities, however, designers do not always know or are not always able to leverage relevant aspects of perceptual and cognitive psychology (i.e., how the human visual system works and its relationship to thinking/learning processes) as part of their efforts to satisfy a client’s design preferences and meet stated learning objectives. In this chapter, I introduce both idealized and more realistic models for the designer-client relationship to consider the major variables that interfere with learning objective-driven design. I discuss these variables in the context of both the client- and designer-related disruptions through the use of examples drawn from recent production projects in the life sciences. Variables that transcend the designer-client divide are also addressed. In conclusion, specific strategies for integrating this knowledge within the context of effective instructional visualization design are presented.

Keywords

Learning Objective Learning Module Design Thinking Instructional Medium Dynamic Visualization 
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.

Notes

Acknowledgments

I wish to thank members of both my collaborative academic team at Harvard Medical School and the University of Toronto, as well as members of my Digizyme team who were involved in creating many of the examples shown in this chapter. Funding in support of some of the research activities was provided by the NSF and NASA.

References

  1. Ainsworth, S. (2008). How do animations influence learning? In D. Robinson & G. Schraw (Eds.), Current perspectives on cognition, learning, and instruction: Recent innovations in educational technology that facilitate student learning (pp. 37–67). Charlotte, NC: Information Age Publishing.Google Scholar
  2. Andrew, A. D., & Wickens, C. D. (2011). When users want what’s NOT best for them. Ergonomics in Design, 3, 10–14.CrossRefGoogle Scholar
  3. Boucheix, J.-M. (2008). Young learners’ control of technical animations. In R. K. Lowe & W. Schnotz (Eds.), Learning with animation: Research implications for design (pp. 208–234). New York: Cambridge University Press.Google Scholar
  4. Burgstahler, S. E., & Cory, R. C. (2010). Universal design in higher education. Cambridge, MA: Harvard Education Press.Google Scholar
  5. 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
  6. De La Flor, M. (2004). The digital biomedical illustration handbook. Hingham: Charles River Media.Google Scholar
  7. Evanko, D. (2013, July 30). Data visualization: A view of every points of view column. Nature Methods [Web log post]. Retrieved from http://blogs.nature.com/methagora/2013/07/data-visualization-points-of-view.html
  8. Frankel, F. C., & De Pace, A. H. (2012). Visual strategies—A practical guide to graphics for scientists and engineers. New Haven, CT: Yale University Press.Google Scholar
  9. Fry, R., & Kolb, D. (1979). Experiential learning theory and learning experiences in liberal arts education. New Directions for Experiential Learning, 6, 79–92.Google Scholar
  10. Gremmler, T. (2014). Creative education and dynamic media. Hong Kong: City University of Hong Kong Press.Google Scholar
  11. Hasler, B. S., Kersten, B., & Sweller, J. (2007). Learner control, cognitive load and instructional animation. Applied Cognitive Psychology, 21, 713–729.CrossRefGoogle Scholar
  12. Hattie, J. (2009). Visible learning—A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.Google Scholar
  13. Hattie, J., & Yates, G. (2014). Visible learning and the science of how we learn. New York: Routledge.Google Scholar
  14. Hodges, E. R. S. (2003). The Guild handbook of scientific illustration. Hoboken, NJ: Wiley.Google Scholar
  15. Hoffman, D. D. (1998). Visual intelligence—How we create what we see. New York: W. W. Norton & Company Inc..Google Scholar
  16. Hübscher-Younger, T., & Narayanan, N. H. (2008). Turning the tables: Investigating characteristics and efficacy of student-authored animations and multimedia representations. In R. K. Lowe & W. Schnotz (Eds.), Learning with animation: Research implications for design (pp. 235–259). New York: Cambridge University Press.Google Scholar
  17. Jantzen, S., Jenkinson, J., & McGill, G. (2015, August 5). Molecular visualization principles. Retrieved from https://bmcresearch.utm.utoronto.ca/sciencevislab/index.php/portfolio/molecular-visualization-principles/
  18. Jenkinson, J. (2017). The role of craft-based knowledge in the design of dynamic visualizations. In R. Lowe, & R. Ploetzner (Eds.), Learning from dynamic visualization—Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  19. 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
  20. Jenkinson, J., & McGill, G. (2013). Using 3D animation in biology education: Examining the effects of visual complexity in the representation of dynamic molecular events. Journal of Biocommunication, 39, 42–49.Google Scholar
  21. Johnson, B., & Pierce, J. T. (2014). Design school wisdom. San Francisco: Chronicle Books.Google Scholar
  22. Kaplan, A. (1964). The conduct of inquiry: Methodology for behavioral science. New Brunswick, NJ: Transaction Publishers.Google Scholar
  23. Kirby, J. R. (2008). Mental representations, cognitive strategies, and individual differences. In R. K. Lowe & W. Schnotz (Eds.), Learning with animation: Research implications for design (pp. 165–180). New York: Cambridge University Press.Google Scholar
  24. Kolb, D. (1976). Learning style inventory: Technical manual. Boston: McBer and Company.Google Scholar
  25. Kosslyn, S. M. (1994). Image and brain—The resolution of the imagery debate. Cambridge, MA: MIT Press.Google Scholar
  26. Kriz, S., & Hegarty, M. (2007). Top-down and bottom-up influences on learning from animations. International Journal of Human-Computer Studies, 65, 911–930.CrossRefGoogle Scholar
  27. Lowe, R. (2000). Visual literacy in science and technology education. Connect—UNESCO International Science, Technology & Environmental Educational Newsletter, XXV(2), 1–3.Google Scholar
  28. Lowe, R. (2006). Animations: A key advance for open and distance learning? In M. Tulloch, S. Relf, & P. Uys (Eds.), Breaking down boundaries: International experience in open, distance and flexible learning: Selected papers from the 2005 ODLAA Conference (pp. 189–195). Bathurst: Charles Sturt University.Google Scholar
  29. Lowe, R. K. (2008). Learning from animation: Where to look, when to look. In R. K. Lowe & W. Schnotz (Eds.), Learning with animation: Research implications for design (pp. 49–68). New York: Cambridge University Press.Google Scholar
  30. Lowe, R. K., & Boucheix, J.-M. (2012). Dynamic diagrams: A composition alternative. In P. Cox, B. Plimmer, & P. Rogers (Eds.), Diagrammatic representation and inference (pp. 233–240). Berlin: Springer.CrossRefGoogle Scholar
  31. Lowe, R., Boucheix, J.-M., & Fillisch, B. (2017). Demonstration tasks for assessment. In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization—Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  32. Lowe, R. K., Jenkinson, J., & McGill, G. (2014, August). Learning functional relations in complex animations. Paper presented at the EARLI SIG 2 (Comprehension of Text and Graphics) Meeting, Rotterdam, Netherlands.Google Scholar
  33. Lowe, R., Schnotz, W., & Rasch, T. (2011). Aligning affordances of graphics with learning task requirements. Applied Cognitive Psychology, 25, 452–459.CrossRefGoogle Scholar
  34. Marx, V. (2013). Data visualization: Ambiguity as a fellow traveler. Nature Methods., 10, 613–615.CrossRefGoogle Scholar
  35. Mayer, R. (2014). The Cambridge handbook of multimedia learning (2nd ed.). New York: Cambridge University Press.CrossRefGoogle Scholar
  36. Mayer, R. E., & Moreno, R. (2002). Animation as an aid to multimedia learning. Educational Psychology Review, 14, 87–99.CrossRefGoogle Scholar
  37. McGill, G. (2014). Crafting scientific visualization—Creative process and best practices. Biozoom, 3, 17–19.Google Scholar
  38. McGill, G., Nowakowski, D., & Blacklow, S. C. (2017). Creating molecular visualizations: It’s the journey and the destination that counts. Cell (submitted).Google Scholar
  39. Metros, S. E. (2008). The educator’s role in preparing visually literate learners. Theory Into Practice, 47, 102–109.CrossRefGoogle Scholar
  40. Munzner, T. (2014). Visualization analysis and design. Boca Raton, FL: CRC Press.Google Scholar
  41. Pinker, S. (2014). The sense of style: The thinking person’s guide to writing in the 21st century. New York: Penguin.Google Scholar
  42. Plass, J. L., Homer, B. D., & Hayward, E. O. (2009). Design factors for educationally effective animations and simulations. Journal of Computing Higher Education, 21, 31–61.CrossRefGoogle Scholar
  43. Ploetzner, R., & Lowe, R. (2012). A systematic characterization of expository animations. Computers in Human Behavior, 28, 781–794.CrossRefGoogle Scholar
  44. Ploetzner, R., & Lowe, R. (2014). Simultaneously presented animations facilitate the learning of higher-order relationships. Computers in Human Behavior, 34, 12–22.CrossRefGoogle Scholar
  45. Ploetzner, R., & Lowe, R. (2017). Looking across instead of back and forth—How the simultaneous presentation of multiple animation episodes facilitates learning. In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization—Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  46. Rappolt-Schlichtmann, G., Daley, S. G., & Rose, L. T. (2012). A research reader in universal design for learning. Cambridge, MA: Harvard Education Press.Google Scholar
  47. Roediger, H. L., Agarwal, P. K., Kang, S. H. K., & Marsh, E. J. (2010). Benefits of testing memory—Best practices and boundary conditions. In G. M. Davies & D. B. Wright (Eds.), New frontiers in applied memory (pp. 13–49). Brighton, UK: Psychology Press.Google Scholar
  48. Roediger, H. L., & Karpicke, J. D. (2006). The power of testing memory: Basic research and implications for educational practice. Perspectives on Psychological Science, 1, 181–210.CrossRefGoogle Scholar
  49. Schnotz, W., & Rasch, T. (2008). Functions of animation in comprehension and learning. In R. K. Lowe & W. Schnotz (Eds.), Learning with animation: Research implications for design (pp. 92–113). New York: Cambridge University Press.Google Scholar
  50. 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
  51. Smallman, H. S., & Cook, M. B. (2011). Naîve realism: Folk fallacies in the design and use of visual displays. Topics in Cognitive Science, 3, 579–608.CrossRefGoogle Scholar
  52. Strobelt, H., Oelke, D., Kwon, B. C., Schrek, T., & Pfister, H. (2015). Guidelines for effective use of text highlighting techniques. IEEE Transactions on Visualizations and Computer Graphics, 22, 489–498.CrossRefGoogle Scholar
  53. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.CrossRefGoogle Scholar
  54. Tufte, E. R. (1997). Visual explanations. Cheshire, CT: Graphics Press.Google Scholar
  55. Tversky, B., Morrison, J. B., & Bétrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human Computer Studies, 57, 247–262.CrossRefGoogle Scholar
  56. Ware, C. (2008). Visual thinking for design. Waltham, MA: Morgan Kaufman.Google Scholar
  57. Ware, C. (2013). Information visualization—Perception for design. Waltham, MA: Morgan Kaufman.Google Scholar
  58. Weinschenk, S. M. (2011). 100 things every designer should know about people. Berkeley, CA: New Riders.Google Scholar
  59. Yenawine, P. (2013). Visual thinking strategies—Using art to deepen learning across school disciplines. Cambridge, MA: Harvard Education Press.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Harvard Medical School & Digizyme Inc.BrooklineUSA

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