How Students Learn Content in Science, Technology, Engineering, and Mathematics (STEM) Through Drawing Activities

  • Sally P. W. WuEmail author
  • Martina A. Rau


Recent research suggests that drawing activities can help students learn concepts in the science, technology, engineering, and mathematics (STEM) disciplines. In particular, drawing activities, which mimic the practices of STEM professionals, can help students engage with visual-spatial content. However, prior work has also shown that students struggle to learn from drawing activities. One major issue is that the learning processes underlying the effects of drawing activities are mostly unknown, and therefore, it is unclear how best to design effective drawing activities in STEM learning environments. To address this gap, our review of prior research investigates which learning processes may explain how drawing activities facilitate learning of STEM content. Specifically, we reviewed prior research across cognitive and sociocultural theoretical perspectives. We identified six learning processes fostered by drawing activities. Each learning process describes how drawing can change the way students interact with the content. Our review shows how instructional support for drawing activities that targets each learning process can enhance learning. Our findings have theoretical implications regarding how drawing activities have been studied and yield open questions about the mechanisms accounting for the effects of drawing activities on students’ learning in STEM disciplines. Further, our findings suggest practical recommendations on how to effectively implement drawing activities that help students learn STEM content.


Drawing STEM content knowledge Visual-spatial content Learning processes Instructional design 



This research was supported by the Wisconsin Center for Education Research, the National Science Foundation through Award #DUE1611782, and the Institute of Education Sciences, U.S. Department of Education through Award #R305B150003 to the University of Wisconsin-Madison. The opinions expressed are those of the authors and do not represent views of the National Science Foundation or the U.S. Department of Education.


  1. Acevedo Nistal, A., Van Dooren, W., & Verschaffel, L. (2012). What counts as a flexible representational choice? An evaluation of students’ representational choices to solve linear function problems. Instructional Science, 40(6), 999–1019. Scholar
  2. Ainsworth, S. E., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science (New York, N.Y.), 333(August), 1096–1097.
  3. Ainsworth, S. E., Stieff, M., Desutter, D., Tytler, R., Prain, V., Panagiotopoulos, D., … Puntambekar, S. (2016). Exploring the value of drawing in learning and assessment. Proceedings of international conference of the learning sciences, ICLS, 2, 1082–1089.Google Scholar
  4. Anning, A. (1999). Learning to draw and drawing to learn. International Journal of Art Design Education, 18(2), 163–172. Scholar
  5. Arcavi, A. (2003). The role of visual representations in the learning of mathematics. Educational Studies in Mathematics, 52(3), 215–241.Google Scholar
  6. Atman, C. J., Adams, R. S., Cardella, M. E., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: a comparison of students and expert practitioners. Journal of Engineering Education, 96(4), 359–379. Scholar
  7. Avgerinou, M. D., & Pettersson, R. (2011). Toward a cohesive theory of visual literacy. Journal of Visual Literacy, 30(2), 1–19. Scholar
  8. Backhouse, M., Fitzpatrick, M., Hutchinson, J., Thandi, C. S., & Keenan, I. D. (2017). Improvements in anatomy knowledge when utilizing a novel cyclical “observe-reflect-draw-edit-repeat” learning process. Anatomical Sciences Education, 10(1), 7–22. Scholar
  9. Berland, L., & Crucet, K. (2015). Epistemological trade-offs: accounting for context when evaluating epistemological sophistication of student engagement in scientific practices. Science Education, 0(0), 1–25.
  10. Bobek, E., & Tversky, B. (2014). Creating visual explanations improves learning. In Proceedings of the 36th annual conference of the cognitive science society (pp. 206–211). Austin: Cognitive Science Society.Google Scholar
  11. Brew, A., Fava, M., & Kantrowitz. (2012). Drawing connections: New directions in drawing. Tracey: Drawing and Visualisation Research, Drawing Kn(September), 0–17.Google Scholar
  12. Brooks, M. (2009). Drawing, visualisation and young children’s exploration of “big ideas.”. International Journal of Science Education, 31(February 2015), 319–341. Scholar
  13. Chang, H.-Y. Y., Quintana, C., & Krajcik, J. (2014). Using drawing technology to assess students’ visualizations of chemical reaction processes. Journal of Science Education and Technology, 23(3), 355–369. Scholar
  14. Cheng, M., & Gilbert, J. K. (2009). Towards a better utilization of diagrams in research into the use of representative levels in chemical education. In J. K. Gilbert & D. Treagust (Eds.), Multiple representations in chemical education (Vol. 4, pp. 55–73). Scholar
  15. Chi, M. T. H. (2008). Three types of conceptual change: belief revision, mental model transformation, and categorical shift. In S. Vosniadou (Ed.), Handbook of research on conceptual change (pp. 61–82). Hillsdale: Erlbaum.Google Scholar
  16. Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. Scholar
  17. Cooper, M. M., Grove, N., Underwood, S. M., & Klymkowsky, M. W. (2010). Lost in Lewis structures: An investigation of student difficulties in developing representational competence. Journal of Chemical Education, 87(8), 869–874. Scholar
  18. Cooper, M. M., Corley, L. M., & Underwood, S. M. (2013). An investigation of college chemistry students’ understanding of structure-property relationships. Journal of Research in Science Teaching, 50(6), 699–721. Scholar
  19. Cooper, M. M., Stieff, M., & DeSutter, D. (2017). Sketching the invisible to predict the visible: from drawing to modeling in chemistry. Topics in Cognitive Science, 1–19.
  20. Cox, R. (1999). Representation construction, externalised cognition and individual differences. Learning and Instruction, 9(4), 343–363. Scholar
  21. Cromley, J. G., Bergey, B. W., Fitzhugh, S., 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. Scholar
  22. Danish, J. A., & Enyedy, N. (2006). Unpacking the mediation of invented representations. In 7th International Conference on Learning Sciences (ICLS ‘06) (pp. 113–119). International Society of the Learning Sciences.Google Scholar
  23. Danish, J. A., & Saleh, A. (2014). Examining how activity shapes students’ interactions while creating representations in early elementary science. International Journal of Science Education, 36(14), 2314–2334. Scholar
  24. Davatzes, A., Gagnier, K., Resnick, I., & Shipley, T. F. (2018). Learning to form accurate mental models. Eos, (February), 1–10.
  25. Day, S. B., & Goldstone, R. L. (2012). The import of knowledge export: connecting findings and theories of transfer of learning. Educational Psychologist, 47(3), 153–176. Scholar
  26. De Bock, D., Verschaffel, L., Janssens, D., Van Dooren, W., & Claes, K. (2003). Do realistic contexts and graphical representations always have a beneficial impact on student’s performance? Negative evidence from a study on modelling non-linear geometry problems. Learning and Instruction, 13(4), 441–463. Scholar
  27. de Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2010). Learning by generating vs. receiving instructional explanations: two approaches to enhance attention cueing in animations. Computers and Education.
  28. de Vere, I., Melles, G., & Kapoor, A. (2011). Developing a drawing culture: new directions in engineering education. Proceedings of the 18th international conference on engineering design (ICED 11): Impacting society through engineering design, Vol 8: Design education, 8(august), 226–235.Google Scholar
  29. de Vries, E. (2006). Students’ construction of external representations in design-based learning situations. Learning and Instruction, 16(3), 213–227. Scholar
  30. diSessa, A. A. (2004). Metarepresentation: native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331. Scholar
  31. diSessa, A. A., & Sherin, B. L. (2000). Meta-representation: an introduction. The Journal of Mathematical Behavior, 19(4), 385–398.Google Scholar
  32. diSessa, A. A., Hammer, D., Sherin, B. L., & Kolpakowski, T. (1991). Inventing graphing: meta-representational expertise in children. The Journal of Mathematical Behavior, 10, 117–160.Google Scholar
  33. Duit, R., & Treagust, D. F. (2008). Conceptual change: a discussion of theoretical, methodological and practical challenges for science education. Cultural Studies of Science Education, 3(2), 297–328. DOI. Scholar
  34. Enyedy, N. (2005). Inventing mapping: creating cultural forms to solve collective problems. Cognition and Instruction, 23(4), 427–466. Scholar
  35. Evagorou, M., Erduran, S., & Mäntylä, T. (2015). The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works. International Journal of STEM Education, 2(1), 11. Scholar
  36. Fan, J. E. (2015). Drawing to learn: how producing graphical representations enhances scientific thinking. Translational Issues in Psychological Science, 1(2), 170–181. Scholar
  37. Fiorella, L., & Mayer, R. E. (2015). Eight ways to promote generative learning. Educational Psychology Review, 1–25.
  38. Fiorella, L., & Zhang, Q. (2018). Drawing boundary conditions for learning by drawing. Educational Psychology Review, pp. 1–23. Educational Psychology review.
  39. Fish, J., & Scrivener, S. (2007). Amplifying the mind’s eye: sketching and visual cognition. Leonardo, 23(1), 117–126.Google Scholar
  40. Forbus, K. D., Usher, J., Lovett, A., Lockwood, K., & Wetzel, J. (2011). CogSketch: sketch understanding for cognitive science research and for education. Topics in Cognitive Science, 3(4), 648–666. Scholar
  41. Forbus, K. D., Chang, M., McLure, M., & Usher, M. (2017). The cognitive science of sketch worksheets. Topics in Cognitive Science, 1–22.
  42. Frankel, F. (2005). Translating science into pictures: a powerful learning tool. Invention and Impact: Building Excellence in Undergraduate Science, Technology, Engineering, and Mathematics (STEM) Education, 155–158.Google Scholar
  43. Gadgil, S., Nokes-Malach, T. J., & Chi, M. T. H. (2012). Effectiveness of holistic mental model confrontation in driving conceptual change. Learning and Instruction, 22(1), 47–61. Scholar
  44. Gagnier, K. M., Atit, K., Ormand, C. J., & Shipley, T. F. (2016). Comprehending diagrams: sketching to support spatial reasoning. Topics in Cognitive Science, 1–19.
  45. Gan, Y. (2007). Drawing out ideas: student-generated drawings’ roles in supporting understanding of “light”. Institute for Knowledge Innovation and Technology Summer Institute Summer Institute, 2007, IKIT, Ontario Institute for Studies in Education, University of Toronto, (1995), 1–36.Google Scholar
  46. Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52(1), 45–56. Scholar
  47. Glogger-Frey, I., Fleischer, C., Grüny, L., Kappich, J., & Renkl, A. (2015). Inventing a solution and studying a worked solution prepare differently for learning from direct instruction. Learning and Instruction, 39, 72–87. Scholar
  48. Gobert, J. D. (2018). The effects of different learning tasks on model-building in plate tectonics: Diagramming versus explaining. Journal of Geoscience Education, 53(4):444–455Google Scholar
  49. Gobert, J. D., & Clement, J. J. (1999). Effects of student-generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching, 36(1), 39–53.<39::AID-TEA4>3.0.CO;2-I.Google Scholar
  50. Goldschmidt, G. (1994). On visual design thinking: the vis kids of architecture. Design Studies, 15(2), 158–174. Scholar
  51. Goldschmidt, G. (2003). The backtalk of self-generated sketches. Design Issues, 19(1), 72–88. Scholar
  52. Goldschmidt, G. (2014). An anthology of theories and models of design.
  53. Greenhalgh, T., & Peacock, R. (2005). Effectiveness and efficiency of search methods in systematic reviews of complex evidence: audit of primary sources. British Medical Journal, 331(7524), 1064–1065. Scholar
  54. Greeno, J. G., & Hall, R. P. (1997). Practicing representation: Learning with and about representational forms. The Phi Delta Kappan, 78(5), 361–67.
  55. 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. Scholar
  56. Hay, D. B., Williams, D., Stahl, D., & Wingate, R. J. (2013). Using drawings of the brain cell to exhibit expertise in neuroscience: exploring the boundaries of experimental culture. Science Education, 97(3), 468–491. Scholar
  57. Hegarty, M. (2004). Mechanical reasoning by mental simulation. Trends in Cognitive Sciences, 8(6), 280–285. Scholar
  58. Hegarty, M. (2012). Meta-representational competence as an aspect of spatial intelligence. Cognitive Science, 1240–1241.Google Scholar
  59. Hellenbrand, J. (2018). Lernen durch sinnstiftendes Zeichnen. Universität Duisburg-Essen.Google Scholar
  60. Jee, B. D., Gentner, D., Uttal, D. H., Sageman, B., Forbus, K. D., Manduca, C. a., et al. (2014). Drawing on experience: how domain knowledge is reflected in sketches of scientific structures and processes. Research in Science Education, 44(6), 859–883. Scholar
  61. Johri, A., Roth, W.-M., & Olds, B. M. (2013). The role of representations in engineering practices: taking a turn towards inscriptions. Journal of Engineering Education, 102(1), 2–19. Scholar
  62. Johri, A., Olds, B. M., & O’Connor, K. (2014). Situative frameworks for engineering learning research. Cambridge Handbook of Engineering Education Research, (January 2014), 47–66.
  63. Jonassen, D. H. (2003). Using cognitive tools to represent problems. Journal of Research on Technology in Education, 35(3), 362–381. Scholar
  64. Jonassen, D. H., Strobel, J., & Gottdenker, J. (2005). Model building for conceptual change. Interactive Learning Environments, 13(1), 15–37. Scholar
  65. Kavakli, M., & Gero, J. S. (2001). Sketching as mental imagery processing. Design Studies, 22(4), 347–364. Scholar
  66. Kavakli, M., & Gero, J. S. (2002). The structure of concurrent cognitive actions: A case study on novice and expert designers. Design Studies, 23(1), 25–40.Google Scholar
  67. Kirsh, D. (2010). Thinking with external representations. AI & Society, 25(4), 441–454. Scholar
  68. Kothiyal, A., Murthy, S., & Chandrasekharan, S. (2016). “Hearts pump and hearts beat”: engineering estimation as a form of model-based reasoning. Proceedings of international conference of the learning sciences, ICLS, 1(2015), 242–249.Google Scholar
  69. Kozma, R., & Russell, J. (2005). Students becoming chemists: developing representational competence. Visualization in Science Education, 121–145.Google Scholar
  70. Kozma, R., Chin, E., Russell, J., & Marx, N. (2000). The roles of representations and tools in the chemistry laboratory and their implications for chemistry learning. Journal of the Learning Sciences, 9(February 2013), 105–143. Scholar
  71. Lajoie, S. P. (2008). Metacognition, self regulation, and self-regulated learning: a rose by any other name? Educational Psychology Review, 20(4), 469–475. Scholar
  72. Latour, B. (1986). Visualization and cognition. Knowledge and Society.
  73. Latour, B. (1990). Drawing things together. In Representations in scientific practice (pp. 19–68). Cambridge: MIT Press. Scholar
  74. Leenaars, F. A. J., Van Joolingen, W. R., & Bollen, L. (2013). Using self-made drawings to support modelling in science education. British Journal of Educational Technology, 44(1), 82–94. Scholar
  75. Lehrer, R., & Schauble, L. (2003). Symbolic communication in mathematics and science: co-constituting inscription and thought. In Language, Literacy, and Cognitive Development: The Development and Consequences of Symbolic Communication (pp. 167–192).Google Scholar
  76. Lehrer, R., & Schauble, L. (2015). The development of scientific thinking. Handbook of Child Psychology and Developmental Science, 671–714.
  77. Leopold, C., & Leutner, D. (2012). Science text comprehension: drawing, main idea selection, and summarizing as learning strategies. Learning and Instruction, 22(1), 16–26. Scholar
  78. Leopold, C., & Leutner, D. (2015). Improving students’ science text comprehension through metacognitive self-regulation when applying learning strategies. Metacognition and Learning, 10(3), 313–346. Scholar
  79. 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. Scholar
  80. 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(2), 284–289. Scholar
  81. Lin, L., Ha Lee, C., Kalyuga, S., Wang, Y., Guan, S., & Wu, H. (2016). The effect of learner-generated drawing and imagination in comprehending a science text. The Journal of Experimental Education, 0973(March).
  82. Lobato, J., Hohensee, C., & Diamond, J. M. (2014). What can we learn by comparing students’ diagram-construction processes with the mathematical conceptions inferred from their explanations with completed diagrams? Mathematics Education Research Journal, 26(3), 607–634. Scholar
  83. Mason, L., Lowe, R., & Tornatora, M. C. (2013). Self-generated drawings for supporting comprehension of a complex animation. Contemporary Educational Psychology, 38(3), 211–224. Scholar
  84. Mayer, R. E. (2009). Research-based principles for designing multimedia instruction. In Applying Science of Learning in Education (pp. 1–12). Retrieved Feb 6 2016
  85. McCracken, W. M., & Newstetter, W. C. (2001). Text to diagram to symbol: Representational transformations in problem-solving. Frontiers in Education Conference, 2001. 31st Annual, 2, F2G–13–17.
  86. Nathan, M. J., & Alibali, M. W. (2010). Learning sciences. Wiley Interdisciplinary Reviews: Cognitive Science, 1(3), 329–345. Scholar
  87. Nathan, M. J., & Sawyer, R. K. (2014). Foundations of the learning sciences. In The Cambridge Handbook of Learning Sciences (pp. 21–43).Google Scholar
  88. Nathan, M. J., Eilam, B., & Kim, S. (2007). To disagree, we must also agree: how intersubjectivity structures and perpetuates discourse in a mathematics classroom. Journal of the Learning Sciences, 16(4), 523–563. Scholar
  89. National Research Council. (2012a). A framework for K-12 science education: practices, crosscutting concepts, and Core ideas. Washington, D.C.: National Academies Press. Scholar
  90. National Research Council. (2012b). Discipline-based education research: understanding and improving learning in undergraduate science and engineering. Journal of Engineering Education, 102(4), 261. Scholar
  91. Nersessian, N. J. (2008). Mental modeling in conceptual change. Handbook of Research on Conceptual Change, (April), 768.
  92. Nichols, K., Ranasinghe, M., & Hanan, J. (2013). Translating between representations in a social context: a study of undergraduate science students’ representational fluency. Instructional Science, 41(4), 699–728. Scholar
  93. Nyachwaya, J. M., Mohamed, A.-R., Roehrig, G. H., Wood, N. B., Kern, A. L., & Schneider, J. L. (2011). The development of an open-ended drawing tool: an alternative diagnostic tool for assessing students’ understanding of the particulate nature of matter. Chemistry Education Research and Practice, 12(2), 121–132. Scholar
  94. Nyachwaya, J. M., Warfa, A.-R. M., Roehrig, G. H., & Schneider, J. L. (2014). College chemistry students’ use of memorized algorithms in chemical reactions. Chemistry Education Research and Practice, 15(1), 81–93. Scholar
  95. Osborne, R. J., & Wittrock, M. C. (1983). Learning science: a generative process. Science Education, 67(4), 489–508. Scholar
  96. Palmer, S. E. (1978). Fundamental aspects of cognitive representation. In Cognition and Categorization (pp. 259–303).Google Scholar
  97. Papaphotis, G., & Tsaparlis, G. (2008). Conceptual versus algorithmic learning in high school chemistry: the case of basic quantum chemical concepts. Part 1. Statistical analysis of a quantitative study. Chemistry Education Research and Practice, 9(4), 323. Scholar
  98. Parnafes, O. (2010). Representational practices in the activity of student-generated representations (SGR) for promoting conceptual understanding. Proceedings of the 2010 international conference of the learning sciences, 1(July), 301–308.Google Scholar
  99. Parnafes, O., Aderet-German, T., & Ward, E. T. (2012). Drawing for understanding: an instructional approach for promoting learning and understanding. Paper Presented at the Annual Meeting of the American Educational Research Association, Vancouver, Canada, (February), 1–36.Google Scholar
  100. Pinker, S. (1990). A theory of graph comprehension. In Artificial Intelligence and the Future of Testing (pp. 73–126).
  101. Ploetzner, R., & Fillisch, B. (2017). Not the silver bullet: learner-generated drawings make it difficult to understand broader spatiotemporal structures in complex animations. Learning and Instruction, 47, 13–24. Scholar
  102. Prain, V., & Tytler, R. (2012). Learning through constructing representations in science: a framework of representational construction affordances. International Journal of Science Education, 34(17), 2751–2773. Scholar
  103. Purcell, A. T., & Gero, J. S. (1998). Drawings and the design process. Design Studies, 19(4), 389–430. Scholar
  104. Quillin, K., & Thomas, S. (2015). Drawing-to-learn: a framework for using drawings to promote model-based reasoning in biology. CBE Life Sciences Education, 14(1), 1–16. Scholar
  105. Roth, W.-M., & McGinn, M. K. (1998). Inscriptions: toward a theory of representing as social practice. Review of Educational Research, 68(1), 35–59. Scholar
  106. Rau, M. A. (2017). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning. Educational Psychology Review, 29(4):717–61.
  107. Sawyer, R. (2005). Social emergence: Societies as complex systems. New York, NY: Cambridge University Press.Google Scholar
  108. Schank, P., & Kozma, R. B. (2002). Learning chemistry through the use of a representation based knowledge building environment. Journal of Computers in Mathematics and Science Teaching, 21(3), 253–279.Google Scholar
  109. Scheiter, K., Schleinschok, K., & Ainsworth, S. E. (2017a). Why sketching may aid learning from science texts: contrasting sketching with written explanations. Topics in Cognitive Science, 1–17.
  110. Scheiter, K., Schubert, C., & Schüler, A. (2017b). Self-regulated learning from illustrated text: eye movement modelling to support use and regulation of cognitive processes during learning from multimedia. British Journal of Educational Psychology, 1–15.
  111. Schleinschok, K., Eitel, A., & Scheiter, K. (2017). Do drawing tasks improve monitoring and control during learning from text? Learning and Instruction, 1–16.
  112. Schmeck, A., Mayer, R. E., Opfermann, M., Pfeiffer, V., & Leutner, D. (2014). Drawing pictures during learning from scientific text: testing the generative drawing effect and the prognostic drawing effect. Contemporary Educational Psychology, 39(4), 275–286. Scholar
  113. Schmidgall, S. P., Eitel, A., & Scheiter, K. (2018). Why do learners who draw perform well? Investigating the role of visualization, generation and externalization in learner-generated drawing. Learning and Instruction, (January).
  114. Schnotz, W. (2002). Commentary: towards an integrated view of learning from text and visual displays. Educational Psychology Review, 14(1), 101–120. Scholar
  115. Schnotz, W. (2005). An integrated model of text and picture compherension. In The Cambridge handbook of multimedia learning (pp. 49–69).Google Scholar
  116. Schnotz, W. (2014). Integrated model of text and picture comprehension. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 72–103). Cambridge: Cambridge University Press. Scholar
  117. Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in Science Education, 36(1–2), 111–139. Scholar
  118. Schwamborn, A., Thillmann, H., Opfermann, M., & Leutner, D. (2011). Cognitive load and instructionally supported learning with provided and learner-generated visualizations. Computers in Human Behavior, 27(1), 89–93. Scholar
  119. Schwartz, D. L., & Heiser, J. (2006). Spatial representations and imagery in learning. The Cambridge Handbook of the Learning Sciences, 283–298.
  120. Schwartz, D. L., & Martin, T. (1988). Inventing to prepare for future learning: the hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129–184. Scholar
  121. Schwarz, C. V., Reiser, B. J., Davis, E. a., Kenyon, L., Achér, A., Fortus, D., et al. (2009). Developing a learning progression for scientific modeling: making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632–654. Scholar
  122. Sins, P. H. M., Savelsbergh, E. R., & van Joolingen, W. R. (2005). The difficult process of scientific modelling: an analysis of novices’ reasoning during computer-based modelling. International Journal of Science Education, 27(14), 1695–1721. Scholar
  123. Snyder, J. (2013). Drawing practices in image-enabled collaboration. In Proceedings of the 2013 conference on computer supported cooperative work (pp. 741–751). ACM.Google Scholar
  124. Stieff, M., Hegarty, M., & Deslongchamps, G. (2011). Identifying representational competence with multi-representational displays. Cognition and Instruction, 29(1), 123–145. Scholar
  125. Suwa, M., Tversky, B., Gero, J., & Purcell, T. (2001). Seeing into sketches: regrouping parts encourages new interpretations. Visual and Spatial Reasoning in Design II, (1994), 207–219.Google Scholar
  126. Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. Scholar
  127. Tippett, C. D. (2016). What recent research on diagrams suggests about learning with rather than learning from visual representations in science. International Journal of Science Education, 38(5), 725–746. Scholar
  128. Tversky, B. (2011). Visualizing thought. Topics in Cognitive Science, 3(3), 499–535. Scholar
  129. Ullman, D. G., Wood, S., & Craig, D. (1990). The importance of drawing in the mechanical design process. Computers and Graphics, 14(2), 263–274. Scholar
  130. Uttal, D. H., & O’Doherty, K. (2008). Comprehending and learning from visual representations: a developmental approach. In Visualization: Theory and Practice in Science Education (pp. 53–72). Springer Netherlands.Google Scholar
  131. Uziak, J., & Fang, N. (2017). Improving students ’ freehand sketching skills in mechanical engineering curriculum. International Journal of Mechanical Engineering Education, 0(0), 1–13.
  132. Valanides, N., Efthymiou, I., & Angeli, C. (2013). Interplay of internal and external representations: students’ drawings and textual explanations about shadow phenomena. Journal of Visual Literacy, 32(2), 67–84.Google Scholar
  133. Van Joolingen, W. R., Aukes, A. V. A., Gijlers, H., & Bollen, L. (2015). Understanding elementary astronomy by making drawing-based models. Journal of Science Education and Technology, 24(2–3), 256–264. Scholar
  134. Van Meter, P. (2001). Drawing construction as a strategy for learning from text. Journal of Educational Psychology, 93(1), 129–140. Scholar
  135. Van Meter, P., & Firetto, C. M. (2013). Cognitive model of drawing construction. In Learning Through Visual Displays (pp. 247–280).Google Scholar
  136. Van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: literature review and synthesis. Educational Psychology Review, 17(4), 285–325. Scholar
  137. Van Meter, P., Aleksic, M., Schwartz, A., & Garner, J. (2006). Learner-generated drawing as a strategy for learning from content area text. Contemporary Educational Psychology, 31(2), 142–166. Scholar
  138. Verstijnen, I. M., van Leeuwen, C., Goldschmidt, G., Hamel, R., & Hennessey, J. M. (1998). Creative discovery in imagery and perception: combining is relatively easy, restructuring takes a sketch. Acta Psychologica, 99(2), 177–200. Scholar
  139. Vosniadou, S. (1994). Capturing and modeling the process of conceptual change. Learning and Instruction, 4(1), 45–69. Scholar
  140. Vosniadou, S. (2003). Exploring the relationships between conceptual change and intentional learning. In G. M. Sinatra & P. R. Pintrich (Eds.), Intentional conceptual change (pp. 377–406). Mahwah: Lawrence Erlbaum Associates, Inc.. Scholar
  141. Vosniadou, S., & Brewer, W. F. (1992). Mental models of the earth: a study of conceptual change in childhood. Cognitive Psychology, 24(4), 535–585. Scholar
  142. Wagner, I., Schnotz, W., Stieff, M., & Mayer, R. E. (2017). Learning from dynamic visualization. In R. K. Lowe & R. Ploetzner (Eds.), Learning from dynamic visualization—innovations in research and application (pp. 333–356). Berlin: Springer. Scholar
  143. Wang, C. Y., & Barrow, L. H. (2011). Characteristics and levels of sophistication: an analysis of chemistry students’ ability to think with mental models. Research in Science Education, 41(4), 561–586. Scholar
  144. 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(530), 489–547. Scholar
  145. Wilkerson-Jerde, M. H. (2014). Construction, categorization, and consensus: Student generated computational artifacts as a context for disciplinary reflection. Educational Technology Research and Development, 62(1), 99–121. Scholar
  146. Wilkerson-Jerde, M. H., Gravel, B. E., & Macrander, C. A. (2015). Exploring shifts in middle school learners’ modeling activity while generating drawings, animations, and computational simulations of molecular diffusion. Journal of Science Education and Technology, 24(2–3), 396–415. Scholar
  147. Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. Metacognition in Educational Theory and Practice, 277–304.
  148. Wylie, R., & Chi, M. T. H. (2014). The self-explanation principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge Handbook of Multimedia Learning (pp. 413–432). Cambridge University Press.
  149. Wu, S. P. W., & Rau, M. A. (2018). Effectiveness and efficiency of adding drawing prompts to an interactive educational technology when learning with visual representations. Learning and Instruction, 55, 93–104.
  150. Wu, S. P. W., Corr, J., & Rau, M. A. (2019). How instructors frame students’ interactions with educational technologies can enhance or reduce learning with multiple representations. Computers & Education, 128, 199–213.
  151. Yang, M. C. (2009). Observations on concept generation and sketching in engineering design. Research in Engineering Design, 20(1), 1–11. Scholar
  152. Zhang, Z. H., & Linn, M. C. (2011). Can generating representations enhance learning with dynamic visualizations? Journal of Research in Science Teaching, 48(10), 1177–1198. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Educational PsychologyUniversity of Wisconsin–MadisonMadisonUSA

Personalised recommendations