Drawing for Promoting Learning and Engagement with Dynamic Visualizations

  • Mike Stieff


In recent years multiple design frameworks have been proposed to improve student learning with dynamic visualizations in science classrooms. These design frameworks commonly argue for including learning activities that promote student engagement and learning through animations or simulations of scientific phenomena. This chapter reviews the underlying mechanisms by which drawing activities might offer unique benefits for promoting science learning when coupled with dynamic visualizations in innovative design frameworks. The chapter also considers the potential of drawing as an activity to increase student engagement with the epistemic practices in science to promote deep learning and interest. These two roles for drawing are illustrated by example activities of The Connected Chemistry Curriculum, a technology-infused curriculum that emphasizes drawing with molecular-level simulations.


Student Engagement Science Classroom Social Engagement Cognitive Engagement Emotional Engagement 
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.



This research was supported in part by a grant from the National Science Foundation (DRL-1102349). Any opinions, findings, or conclusions expressed in this article are those of the authors and do not necessarily represent the views of these agencies.


  1. Ainsworth, S. E., & Iacovides, I. (2005). Learning by constructing self-explanation diagrams. Paper presented at the 11th EARLI Conference, Munich, Germany. Retrieved from
  2. Ainsworth, S., & Loizou, A. T. (2003). The effects of self-explaining when learning with text or diagrams. Cognitive Science, 27, 609–681.Google Scholar
  3. Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333, 1096–1097.CrossRefGoogle Scholar
  4. Berthold, K., Eysink, T. H. S., & Renkl, A. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37, 345–363.CrossRefGoogle Scholar
  5. Berthold, K., Röder, H., Knörzer, D., Kessler, W., & Renkl, A. (2011). The double-edged effects of explanation prompts. Computers in Human Behavior, 27, 69–75.CrossRefGoogle Scholar
  6. Britton, L. A., & Wandersee, J. H. (1997). Cutting up text to make moveable, magnetic diagrams: A way of teaching and assessing biological processes. The American Biology Teacher, 59, 288–291.CrossRefGoogle Scholar
  7. Chang, H., Quintana, C., & Krajcik, J. S. (2009). The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter. Science Education, 94, 73–94.Google Scholar
  8. Chi, M. T. H. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1, 73–105.CrossRefGoogle Scholar
  9. Christian, W., & Titus, A. (1998). Developing web-based curricula using Java physlets. Computers in Physics, 12, 227–232.CrossRefGoogle Scholar
  10. Clegg, T.L., Bonsignore, E., Yip, J. C., Gelderblom, H., Kuhn, A., Valenstein, T. & Druin, A. (2012). Technology for promoting scientific practice and personal meaning in life-relevant learning. In Proceedings of the 11th International Conference on Interaction Design and Children (IDC12) (pp. 152–161). New York: ACM.Google Scholar
  11. Cooper, M. M., Groves, N. P., Pargas, R., Bryfczynski, S. P., & Gatlin, T. (2009). OrganicPad: An interactive freehand drawing application for drawing Lewis structures and the development of skills in organic chemistry. Chemistry Education Research and Practice, 10, 296–301.CrossRefGoogle Scholar
  12. Cromley, J. G., Bergey, B. W., Fitzhugh, S. L., Newcombe, N., Wills, T. W., Shipley, T. F., & Tanaka, J. C. (2013). Effects of three diagram instruction methods on transfer of diagram comprehension skills: The critical role of inference while learning. Learning and Instruction, 26, 45–58.CrossRefGoogle Scholar
  13. Dalebroux, A., Goldstein, T. R., & Winner, E. (2008). Short-term mood repair through art-making: Positive emotion is more effective than venting. Motivation and Emotion, 32, 288–295.CrossRefGoogle Scholar
  14. de Bock, D., Verschaffel, L., & Janssens, D. (1998). The predominance of the linear model in secondary school students’ solutions of word problems involving length and area of similar plane figures. Educational Studies in Mathematics, 35, 65–83.CrossRefGoogle Scholar
  15. de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179–201.CrossRefGoogle Scholar
  16. 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
  17. De Petrillo, L., & Winner, E. (2005). Does art improve mood? A test of a key assumption underlying art therapy. Art Therapy, 22, 205–212.CrossRefGoogle Scholar
  18. Dickey, M. (2005). Engaging by design: How engagement strategies in popular computer and video games can inform instructional design. Educational Technology Research and Development, 53, 67–83.CrossRefGoogle Scholar
  19. Edelson, D. C., Gordin, D. N., & Pea, R. D. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. Journal of the Learning Sciences, 8, 391–450.CrossRefGoogle Scholar
  20. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109.CrossRefGoogle Scholar
  21. Gilbert, J. (2005). Visualization in science education. Dordrecht: Springer.CrossRefGoogle Scholar
  22. Gobert, J. (2000). A typology of models for plate tectonics: Inferential power and barriers to understanding. International Journal of Science Education, 22, 937–977.CrossRefGoogle Scholar
  23. Gobert, J. (2005). The effects of different learning tasks on conceptual understanding in science: Teasing out representational modality of diagramming versus explaining. Journal of Geoscience Education, 53, 444–455.CrossRefGoogle Scholar
  24. Gooding, D. (2004). Visualization, inference and explanation in the sciences. In G. Malcolm (Ed.), Studies in multidisciplinarity (Vol. 2, pp. 1–25). Amsterdam: Elsevier.Google Scholar
  25. Harris, J., Mishra, P., & Koehler, M. (2009). Teachers’ technological pedagogical content knowledge and learning activity types: Curriculum-based technology integration reframed. Journal of Research on Technology in Education, 41, 393–416.CrossRefGoogle Scholar
  26. Hayes, D., Symington, D., & Martin, M. (1994). Drawing during science activity in the primary school. International Journal of Science Education, 16, 265–277.CrossRefGoogle Scholar
  27. Hegedus, S., & Kaput, J. (2004). An introduction to the profound potential of connected algebra activities: Issues of representation, engagement and pedagogy. In Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education (pp. 129–136). Toronto: OISE/UT.Google Scholar
  28. Hofstein, A., & Lunetta, V. N. (2003). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88, 28–54.CrossRefGoogle Scholar
  29. Jee, B. D., Gentner, D., Forbus, K., Sageman, B., & Uttal, D. H. (2009). Drawing on experience: Use of sketching to evaluate knowledge of spatial scientific concepts. In N. A. Taatgen & H. van Rijn (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2499–2504). Amsterdam: Cognitive Science Society.Google Scholar
  30. Johnson, J. K., & Reynolds, S. J. (2005). Concept sketches – Using student- and instructor-generated, annotated sketches for learning, teaching, and assessment in geology courses. Journal of Geoscience Education, 53, 85–95.CrossRefGoogle Scholar
  31. Kelly, R. M., & Jones, L. L. (2007). Exploring how different features of animations of sodium chloride dissolution affect students’ explanations. Journal of Science Education and Technology, 16, 413–429.CrossRefGoogle Scholar
  32. Khishfe, R., & Abd-El-Khalick, F. (2002). Influence of explicit and reflective versus implicit inquiry-oriented instruction. Journal of Research in Science Teaching, 39, 551–578.CrossRefGoogle Scholar
  33. Kozma, R. (2003). Material and social affordances of multiple representations for science understanding. Learning and Instruction, 13, 205–226.CrossRefGoogle Scholar
  34. Kozma, R., Chin, E., Russell, J., & Marx, N. (2000). The role of representations and tools in the chemistry laboratory and their implications for chemistry learning. Journal of the Learning Sciences, 9, 105–143.CrossRefGoogle Scholar
  35. Kozma, R., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34, 949–968.CrossRefGoogle Scholar
  36. Latour, B. (1990). Drawing things together. In M. Lynch & S. Woolgar (Eds.), Representation in scientific practice (pp. 19–68). Cambridge, MA: MIT Press.Google Scholar
  37. Leutner, D., Leopold, C., & Sumfleth, E. (2009). Cognitive load and science text comprehension: Effects of drawing and mentally imagining text content. Computers in Human Behavior, 25, 284–289.CrossRefGoogle Scholar
  38. Leutner, D., & Schmeck, A. (2014). The generative drawing principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 433–448). New York: Cambridge University Press.CrossRefGoogle Scholar
  39. Linn, M. C. (2010). How can selection and drawing support learning from dynamic visualizations. In K. Gomez, L. Lyons & J. Radinsky (Eds.), Proceedings of the Ninth International Conference of the Learning Sciences (ICLS) (Vol. 1, pp. 165–166). Mahwah, NJ: Erlbaum.Google Scholar
  40. Linn, M. C., Lee, H.-S., Tinker, R., Husic, F., & Chiu, J. L. (2006). Teaching and assessing knowledge integration in science. Science, 313, 1049–1050.CrossRefGoogle Scholar
  41. Lowe, R. (2003). Animation and learning: Selective processing of information in dynamic graphics. Learning and Instruction, 13, 157–176.CrossRefGoogle Scholar
  42. Lowe, R., & Boucheix, J.-M. (2017). A composition approach to design of educational animations. In R. Lowe, & R. Ploetzner (Eds.), Learning from dynamic visualization – Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  43. Lowe, R., & Mason, L. (2017). Self-generated drawing: A help or hindrance to learning from animation? In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization – Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  44. Lowe, R., & Schnotz, W. (Eds.). (2008). Learning with animation: Research implications for design. New York: Cambridge University Press.Google Scholar
  45. Mason, L., Lowe, R., & Tornatora, M. C. (2013). Self-generated drawings for supporting comprehension of a complex animation. Contemporary Educational Psychology, 38, 211–224.CrossRefGoogle Scholar
  46. Mayer, R. E. (2009). Multimedia learning (2nd ed.). New York: Cambridge University Press.CrossRefGoogle Scholar
  47. McGrath, M. B., & Brown, J. R. (2005). Visual learning for science and engineering. IEEE Computer Graphics and Applications, 25, 56–63.CrossRefGoogle Scholar
  48. Miller, G. A., Galanter, E., & Pribram, K. H. (1960). Plans and the structure of behavior. New York: Holt, Rinehart, & Winston.CrossRefGoogle Scholar
  49. Newmann, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). The significance and sources of student engagement. In F. M. Newman (Ed.), Student engagement and achievement in American secondary schools (pp. 11–39). New York: Teachers College Press.Google Scholar
  50. Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology, 45, 255–287.CrossRefGoogle Scholar
  51. Plass, J. L., Milne, C., Homer, B. D., Jordan, T., Schwartz, R. N., Hayward, E. O., & Barrientos, J. (2012). Investigating the effectiveness of computer simulations for chemistry learning. Journal of Research in Science Teaching, 49, 394–419.CrossRefGoogle Scholar
  52. Ploetzner, R., & Breyer, B. (2017). Strategies for learning from animation with and without narration. In R. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization – Innovations in research and application. Berlin: Springer (this volume).Google Scholar
  53. Ploetzner, R., & Fillisch, B. (2017). Not the silver bullet: Learner-generated drawings make it difficulty to understand broader spatiotemporal structures in complex animations. Learning and Instruction, 47, 13–24.Google Scholar
  54. Prain, V., & Tytler, R. (2012). Learning through constructing representations in science: A framework of representational construction affordances. International Journal of Science Education, 34, 2751–2773.CrossRefGoogle Scholar
  55. Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J. S., Fretz, E., Duncan, R. G., & Soloway, E. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13, 337–386.CrossRefGoogle Scholar
  56. Rasco, R. W., Tennyson, R. D., & Boutwell, R. C. (1975). Imagery instructions and drawings in learning prose. Journal of Educational Psychology, 67, 188–192.CrossRefGoogle Scholar
  57. Reiser, B. J., Tabak, I., Sandoval, W. A., Smith, B., Steinmuller, F., & Leone, T. J. (2001). BGuILE: Strategic and conceptual scaffolds for scientific inquiry in biology classrooms. In S. M. Carver & D. Klahr (Eds.), Cognition and instruction: Twenty-five years of progress (pp. 263–305). Mahwah, NJ: Erlbaum.Google Scholar
  58. Roschelle, J., Schechtman, N., Tatar, D., Hegedus, S., Hopkins, B., Empson, S., & Gallagher, L. (2010). Integration of technology, curriculum, and professional development for advancing middle school mathematics: Three large-scale studies. American Educational Research Journal, 47, 833–878.CrossRefGoogle Scholar
  59. Roy, M., & Chi, M. T. H. (2005). The self-explanation principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 271–286). New York: Cambridge University Press.CrossRefGoogle Scholar
  60. Ryan, S., Yip, J., Stieff, M., & Druin, A. (2013). Cooperative inquiry as a community of practice. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), Proceedings of the 10th International Conference on Computer-Supported Collaborative Learning (pp. 145–148). Madison, WI: International Society for the Learning Sciences.Google Scholar
  61. 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, 872–879.CrossRefGoogle Scholar
  62. Snowman, J., & Cunningham, D. J. (1975). A comparison of pictorial and written adjunct aids in learning from text. Journal of Educational Psychology, 67, 307–311.CrossRefGoogle Scholar
  63. Stein, M., & Power, B. (1996). Putting art on the scientist’s palette. In R. S. Hubbard & K. Ernst (Eds.), New entries: Learning by writing and drawing (pp. 59–68). Portsmouth, NH: Heinemann.Google Scholar
  64. Stieff, M. (2005). Connected chemistry – A novel modeling environment for the chemistry classroom. Journal of Chemical Education, 82, 489–493.CrossRefGoogle Scholar
  65. Stieff, M. (2011a). Fostering representational competence through argumentation with multi-representational displays. Proceedings of the 9th international conference on computer-supported collaborative learning (Vol. 1, pp. 288–295). Mahwah, NJ: Erlbaum.Google Scholar
  66. Stieff, M. (2011b). Improving representational competence using multi-representational learning environments. Journal of Research in Science Teaching, 48, 1137–1158.CrossRefGoogle Scholar
  67. Stieff, M., Bateman, R., & Uttal, D. H. (2005). Teaching and learning with three-dimensional representations. In J. Gilbert (Ed.), Visualization in science education (pp. 93–120). Oxford: Oxford University Press.CrossRefGoogle Scholar
  68. Stieff, M., & McCombs, M. (2006). Increasing representational fluency with visualization tools. In Proceedings of the Seventh International Conference of the Learning Sciences (ICLS) (Vol.1, pp. 730–736). Mahwah, NJ: Erlbaum.Google Scholar
  69. Stieff, M., Nighelli, T., Yip, J., Ryan, S., & Berry, A. (2012). The connected chemistry curriculum (Vols. 1–9). Chicago, IL: University of Illinois.Google Scholar
  70. Stieff, M., & Wilensky, U. (2003). Connected Chemistry – incorporating interactive simulations into the chemistry classroom. Journal of Science Education & Technology, 12, 285–302.CrossRefGoogle Scholar
  71. Tai, R., Liu, C. Q., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science, 312, 1143–1144.CrossRefGoogle Scholar
  72. Van Meter, P., & Firetto, C. M. (2013). Cognitive model of drawing construction: Learning through the construction of drawings. In G. Schraw, M. McCrudden, & D. Robinson (Eds.), Learning through visual displays (pp. 247–280). Scottsdale, AZ: Information Age Publishing.Google Scholar
  73. Van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: Literature review and synthesis. Educational Psychology Review, 17, 285–325.CrossRefGoogle Scholar
  74. Wu, H.-k., & Huang, Y.-L. (2007). Ninth-grade student engagement in teacher-centered and student-centered technology-enhanced learning environments. Science Education, 91, 727–749.CrossRefGoogle Scholar
  75. Wu, H.-k., Krajcik, J. S., & Soloway, E. (2001). Promoting conceptual understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38, 821–842.CrossRefGoogle Scholar
  76. Zhang, H. Z., & Linn, M. C. (2011). Can generating representations enhance learning with dynamic visualizations? Journal of Research in Science Teaching, 48, 1177–1198.CrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Chemistry, Learning Sciences Research InstituteUniversity of Illinois, ChicagoChicagoUSA

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