Making connections among multiple visual representations: how do sense-making skills and perceptual fluency relate to learning of chemistry knowledge?

Abstract

To learn content knowledge in science, technology, engineering, and math domains, students need to make connections among visual representations. This article considers two kinds of connection-making skills: (1) sense-making skills that allow students to verbally explain mappings among representations and (2) perceptual fluency in connection making that allows students to fast and effortlessly use perceptual features to make connections among representations. These different connection-making skills are acquired via different types of learning processes. Therefore, they require different types of instructional support: sense-making activities and fluency-building activities. Because separate lines of research have focused either on sense-making skills or on perceptual fluency, we know little about how these connection-making skills interact when students learn domain knowledge. This article describes two experiments that address this question in the context of undergraduate chemistry learning. In Experiment 1, 95 students were randomly assigned to four conditions that varied whether or not students received sense-making activities and fluency-building activities. In Experiment 2, 101 students were randomly assigned to five conditions that varied whether or not and in which sequence students received sense-making and fluency-building activities. Results show advantages for sense-making and fluency-building activities compared to the control condition only for students with high prior chemistry knowledge. These findings provide new insights into potential boundary conditions for the effectiveness of different types of instructional activities that support students in making connections among multiple visual representations.

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Notes

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    Statistical outliers were excluded because individual outliers can skew the results of ANCOVAs, which are sensitive to extreme cases. Therefore, effect estimates are more reliable if outliers are excluded. However, in the present case, the exclusion of outliers did not change which effects were significant.

References

  1. Acevedo Nistal, A., Van Dooren, W., & Verschaffel, L. (2013). Students’ reported justifications for their representational choices in linear function problems: An interview study. Educational Studies, 39(1), 104–117. 10.1080/03055698.2012.674636.

    Article  Google Scholar 

  2. Acevedo Nistal, A., Van Dooren, W., & Verschaffel, L. (2015). Improving students’ representational flexibility in linear-function problems: An intervention. Educational Psychology, 34(6), 763–786. http://dx.doi.org/10.1080/01443410.2013.785064.

    Article  Google Scholar 

  3. Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198. 10.1016/j.learninstruc.2006.03.001.

    Article  Google Scholar 

  4. Ainsworth, S., Bibby, P., & Wood, D. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. Journal of the Learning Sciences, 11(1), 25–61. 10.1207/S15327809JLS1101_2.

    Article  Google Scholar 

  5. Airey, J., & Linder, C. (2009). A disciplinary discourse perspective on university science learning: Achieving fluency in a critical constellation of modes. Journal of Research in Science Teaching, 46(1), 27–49. 10.1002/tea.20265.

    Article  Google Scholar 

  6. Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26(2), 147–179. 10.1016/S0364-0213(02)00061-7.

    Article  Google Scholar 

  7. Anderson, T. L., & Bodner, G. M. (2008). What can we do about ‘parker’? A case study of a good student who didn't ‘get’organic chemistry. Chemistry Education Research and Practice, 9(2), 93–101. https://doi.org/10.1039/B806223B.

    Article  Google Scholar 

  8. Atkinson, R. K., Renkl, A., & Merrill, M. M. (2003). Transitioning from studying examples to solving problems: Effects of self-explanation prompts and fading worked-out steps. Journal of Educational Psychology, 95(4), 774–783. http://dx.doi.org/10.1037/0022-0663.95.4.774.

    Article  Google Scholar 

  9. Ayres, P. (2015). State-of-the-art research into multimedia learning: A commentary on Mayer’s handbook of multimedia learning. Applied Cognitive Psychology, 29(4), 631–636.

    Article  Google Scholar 

  10. Baetge, I., & Seufert, T. (2010). Effects of support for coherence formation in computer science education. In Paper presented at the EARLI SIG 6/7, Ulm.

  11. Berthold, K., & Renkl, A. (2009). Instructional aids to support a conceptual understanding of multiple representations. Journal of Educational Research, 101(1), 70–87. 10.1037/a0013247.

    Google Scholar 

  12. Bodemer, D., & Faust, U. (2006). External and mental referencing of multiple representations. Computers in Human Behavior, 22(1), 27–42. 10.1016/j.chb.2005.01.005.

    Article  Google Scholar 

  13. Bodemer, D., Ploetzner, R., Bruchmüller, K., & Häcker, S. (2005). Supporting learning with interactive multimedia through active integration of representations. Instructional Science, 33(1), 73–95. 10.1007/s11251-004-7685-z.

    Article  Google Scholar 

  14. Bodemer, D., Ploetzner, R., Feuerlein, I., & Spada, H. (2004). The active integration of information during learning with dynamic and interactive visualisations. Learning and Instruction, 14(3), 325–341. 10.1016/j.learninstruc.2004.06.006.

    Article  Google Scholar 

  15. Bowen, C. W. (1990). Representational systems used by graduate students while problem solving in organic synthesis. Journal of Research in Science Teaching, 27(4), 351–370.

    Article  Google Scholar 

  16. Brown, T. L., LeMay, H. E., Bursten, B. E., Murphy, C. J., & Woodward, P. M. (2011). Chemistry - the central science (12th ed.). Prentice Hall.

  17. Charalambous, C. Y., & Pitta-Pantazi, D. (2007). Drawing on a theoretical model to study students’ understandings of fractions. Educational Studies in Mathematics, 64(3), 293–316. 10.1007/s10649-006-9036-2.

    Article  Google Scholar 

  18. Chase, C. C., Shemwell, J. T., & Schwartz, D. L. (2010). Explaining across contrasting cases for deep understanding in science: An example using interactive simulations. In Proceedings of the 9th international conference of the learning sciences (Vol. 1, pp. 153–160). International Society of the Learning Sciences.

  19. Cheng, P. (1999). Unlocking conceptual learning in mathematics and science with effective representational systems. Computers and Education, 33, 109–130.

    Article  Google Scholar 

  20. Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. 10.1016/0364-0213(89)90002-5.

    Article  Google Scholar 

  21. Chi, M. T. H., de Leeuw, N., Chiu, M. H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477. 10.1016/0364-0213(94)90016-7.

    Google Scholar 

  22. Chi, M. T. H., Feltovitch, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152. 10.1207/s15516709cog0502_2.

    Article  Google Scholar 

  23. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  24. Cramer, K. (2001). Using models to build an understanding of functions. Mathematics Teaching in the Middle School, 6(5), 310–318.

    Google Scholar 

  25. diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.

    Article  Google Scholar 

  26. diSessa, A. A., & Sherin, B. L. (2000). Meta-representation: An introduction. The Journal of Mathematical Behavior, 19(4), 385–398. 10.1016/S0732-3123(01)00051-7.

    Article  Google Scholar 

  27. Dreyfus, H., & Dreyfus, S. E. (1986). Five steps from novice to expert. In Mind over machine: The power of human intuition and expertise in the era of the computer (pp. 16–51). New York: The Free Press.

  28. Eilam, B. (2013). Possible constraints of visualization in biology: Challenges in learning with multiplerepresentations. In D. F. Treagust & C.-Y. Tsui (Eds.), Multiple representations in biological education (pp. 55–73). Dordrecht: Springer.

    Google Scholar 

  29. Fahle, M., & Poggio, T. (2002). Perceptual learning. Cambridge, MA: The MIT Press.

    Google Scholar 

  30. Frensch, R., & Rünger, D. (2003). Implicit learning. Current Directions in Psychological Science, 12(1), 13–18. 10.1111/1467-8721.01213.

    Article  Google Scholar 

  31. Gadgil, S., Nokes-Malach, T. J., & Chi, M. T. (2012). Effectiveness of holistic mental model confrontation in driving conceptual change. Learning and Instruction, 22(1), 47–61. 10.1016/j.learninstruc.2011.06.002.

    Article  Google Scholar 

  32. Gegenfurtner, A., Lehtinen, E., & Säljö, R. (2011). Expertise differences in the comprehension of visualizations: A meta-analysis of eye-tracking research in professional domains. Educational Psychology Review, 23(4), 523–552. 10.1007/s10648-011-9174-7.

    Article  Google Scholar 

  33. Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170. 10.1207/s15516709cog0702_3.

    Article  Google Scholar 

  34. Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52(1), 45–56. http://dx.doi.org/10.1037/0003-066X.52.1.45.

    Article  Google Scholar 

  35. Gibson, E. J. (1969). Principles of perceptual learning and development. New York: Prentice Hall.

    Google Scholar 

  36. Gibson, E. J. (2000). Perceptual learning in development: Some basic concepts. Ecological Psychology, 12(4), 295–302. 10.1207/S15326969ECO1204_04.

    Article  Google Scholar 

  37. Gilbert, J. K. (2005). Visualization: A metacognitive skill in science and science education. In J. K. Gilbert (Ed.), Visualization: Theory and practice in science education (pp. 9–27). Dordrecht: Springer.

    Google Scholar 

  38. Gilbert, J. K. (2008). Visualization: An emergent field of practice and inquiry in science education. In J. K. Gilbert, M. Reiner, & M. B. Nakhleh (Eds.), Visualization: Theory and practice in science education (Vol. 3, pp. 3–24). Dordrecht: Springer.

    Google Scholar 

  39. Gilbert, J. K., & Treagust, D. F. (2009). Towards a coherent model for macro, submicro and symbolic representations in chemical education. In J. K. Gilbert & D. F. Treagust (Eds.), Multiple representations in chemical education (pp. 333–350). Dordrecht: Springer.

    Google Scholar 

  40. Goldstone, R. (1997). Perceptual learning. San Diego, CA: Academic.

    Google Scholar 

  41. Goldstone, R. L., & Barsalou, L. W. (1998). Reuniting perception and conception. Cognition, 65(2), 231–262. 10.1016/S0010-0277(97)00047-4.

    Article  Google Scholar 

  42. Gutwill, J. P., Frederiksen, J. R., & White, B. Y. (1999). Making their own connections: Students’ understanding of multiple models in basic electricity. Cognition and Instruction, 17(3), 249–282. 10.1207/S1532690XCI1703_2.

    Article  Google Scholar 

  43. Hinze, S. R., Rapp, D. N., Williamson, V. M., Shultz, M. J., Deslongchamps, G., & Williamson, K. C. (2013). Beyond ball-and-stick: Students’ processing of novel STEM visualizations. Learning and Instruction, 26, 12–21. 10.1016/j.learninstruc.2012.12.002.

    Article  Google Scholar 

  44. Johnson, C. I., & Mayer, R. E. (2010). Applying the self-explanation principle to multimedia learning in a computer-based game-like environment. Computers in Human Behavior, 26(6), 1246–1252.

    Article  Google Scholar 

  45. Jones, L. L., Jordan, K. D., & Stillings, N. A. (2005). Molecular visualization in chemistry education: The role of multidisciplinary collaboration. Chemistry Education Research and Practice, 6(3), 136–149. 10.1039/B5RP90005K.

    Article  Google Scholar 

  46. Kaput, J. (1987). Towards a theory of symbol use in mathematics. In C. Janvier (Ed.), Problems of representations in the teaching and learning of mathematics (pp. 159–195). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  47. Kellman, P. J., & Garrigan, P. B. (2009). Perceptual learning and human expertise. Physics of Life Reviews, 6(2), 53–84. 10.1016/j.plrev.2008.12.001.

    Article  Google Scholar 

  48. Kellman, P. J., & Massey, C. M. (2013). Perceptual learning, cognition, and expertise. In B. H. Ross (Ed.), The psychology of learning and motivation (Vol. 558, pp. 117–165). New York: Elsevier Academic.

    Google Scholar 

  49. Kellman, P. J., Massey, C. M., Roth, Z., Burke, T., Zucker, J., Saw, A., et al. (2008). Perceptual learning and the technology of expertise: Studies in fraction learning and algebra. Pragmatics and Cognition, 16(2), 356–405. 10.1075/pc.16.2.07kel.

    Article  Google Scholar 

  50. Kellman, P. J., Massey, C. M., & Son, J. Y. (2009). Perceptual learning modules in mathematics: Enhancing students’ pattern recognition, structure extraction, and fluency. Topics in Cognitive Science, 2(2), 285–305. https://doi.org/10.1111/j.1756-8765.2009.01053.x.

    Article  Google Scholar 

  51. Kirschner, P., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential and inquiry-based teaching. Educational Psychologist, 41, 75–86.

    Article  Google Scholar 

  52. Koedinger, K. R., Booth, J. L., & Klahr, D. (2013). Instructional complexity and the science to constrain it. Science Education, 342(6161), 935–937. 10.1126/science.1238056.

    Google Scholar 

  53. Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The knowledge-learning-instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757–798. 10.1111/j.1551-6709.2012.01245.x.

    Article  Google Scholar 

  54. 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. The Journal of the Learning Sciences, 9(2), 105–143. 10.1207/s15327809jls0902_1.

    Article  Google Scholar 

  55. Kozma, R., & Russell, J. (2005a). Students becoming chemists: Developing representational competence. In J. Gilbert (Ed.), Visualization in science education (pp. 121–145). Dordrecht: Springer.

    Google Scholar 

  56. Kozma, R., & Russell, J. (2005b). Multimedia learning of chemistry. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 409–428). New York: Cambridge University Press.

    Google Scholar 

  57. Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science: A Multidisciplinary Journal, 11(1), 65–100. 10.1111/j.1551-6708.1987.tb00863.x.

    Article  Google Scholar 

  58. Loudon, M. (2009). Organic chemistry (5th ed.). Roberts and Company Publishers.

  59. Massey, C. M., Kellman, P. J., Roth, Z., & Burke, T. (2011). Perceptual learning and adaptive learning technology—Developing new approaches to mathematics learning in the classroom. In N. L. Stein & S. W. Raudenbush (Eds.), Developmental cognitive science goes to school (pp. 235–249). New York: Routledge.

    Google Scholar 

  60. Mayer, R. E. (2009). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 31–48). New York: Cambridge University Press.

    Google Scholar 

  61. Moore, J. W., & Stanitski, C. L. (2015). Chemistry: The molecular science (5th ed.). Stamford, CT: Cengage Learning.

    Google Scholar 

  62. NCTM. (2000). Principles and standards for school mathematics. Reston, VA: National Council of Teachers of Mathematics.

    Google Scholar 

  63. NCTM. (2006). Curriculum focal points for prekindergarten through grade 8 mathematics: A quest for coherence. Reston, VA: NCTM.

    Google Scholar 

  64. Noss, R. R., Healy, L., & Hoyles, C. (1997). The construction of mathematical meanings: Connecting the visual with the symbolic. Educational Studies in Mathematics, 33, 203–233. 10.1023/A:1002943821419.

    Article  Google Scholar 

  65. NRC. (2006). Learning to think spatially. Washington, DC: National Academies Press.

    Google Scholar 

  66. O’Keefe, P. A., Letourneau, S. M., Homer, B. D., Schwartz, R. N., & Plass, J. L. (2014). Learning from multiple representations: An examination of fixation patterns in a science simulation. Computers in Human Behavior, 35, 234–242.

    Article  Google Scholar 

  67. Özgün-Koca, S. A. (2008). Ninth grade students studying the movement of fish to learn about linear relationships: The use of video-based analysis software in mathematics classrooms. The Mathematics Educator, 18(1), 15–25.

    Google Scholar 

  68. Pape, S. J., & Tchoshanov, M. A. (2001). The role of representation (s) in developing mathematical understanding. Theory into Practice, 40(2), 118–127. 10.1207/s15430421tip4002_6.

    Article  Google Scholar 

  69. Rau, M. A. (2015). Enhancing undergraduate chemistry learning by helping students make connections among multiple graphical representations. Chemistry Education Research and Practice, 16, 654–669. https://doi.org/10.1039/C5RP00065C.

    Article  Google Scholar 

  70. Rau, M. A. (2016a). Conditions for the effectiveness of multiple visual representations in enhancing STEM learning. Educational Psychology Review. https://doi.org/10.1007/s10648-016-9365-3.

    Google Scholar 

  71. Rau, M. A. (2016b). A framework for discipline-specific grounding of educational technologies with multiple visual representations. IEEE Transactions on Learning Technologies. https://doi.org/10.1109/TLT.2016.2623303.

    Google Scholar 

  72. Rau, M. A., Aleven, V., & Rummel, N. (2013). Interleaved practice in multi-dimensional learning tasks: Which dimension should weinterleave? Learning and Instruction, 23, 98–114. https://doi.org/10.1016/j.learninstruc.2012.07.003.

    Article  Google Scholar 

  73. Rau, M. A., Aleven, V., Rummel, N., & Pardos, Z. (2014). How should intelligent tutoring systems sequence multiple graphical representations of fractions? A multi-methods study. International Journal of Artificial Intelligence in Education, 24(2), 125–161. https://doi.org/10.1007/s40593-013-0011-7.

    Article  Google Scholar 

  74. Rau, M. A., Aleven, V., & Rummel, N. (2015). Successful learning with multiple graphical representations and self-explanation prompts. Journal of Educational Psychology, 107(1), 30–46. https://doi.org/10.1037/a0037211.

    Article  Google Scholar 

  75. Rau, M. A., Aleven, V., & Rummel, N. (2016). Supporting students in making sense of connections and in becoming perceptually fluentin making connections among multiple graphical representations. Journal of Educational Psychology. https://doi.org/10.1037/edu0000145.

    Google Scholar 

  76. Rau, M. A., Aleven, V., & Rummel, N. (2017). Making connections between multiple graphical representations of fractions: Conceptualunderstanding facilitates perceptual fluency, but not vice versa. Instructional Science. https://doi.org/10.1007/s11251-017-9403-7.

    Google Scholar 

  77. Richman, H. B., Gobet, F., Staszewski, J. J., & Simon, H. A. (1996). Perceptual and memory processes in the acquisition of expert performance: The EPAM model. In K. A. Ericsson (Ed.), The road to excellence? The acquisition of expert performance in the arts and sciences, sports and games (pp. 167–187). Mahwah, NJ: Erlbaum Associates.

    Google Scholar 

  78. Roediger, H. L., III, & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255.

    Article  Google Scholar 

  79. Schnotz, W. (2005). An integrated model of text and picture comprehension. In R. E. Mayer (Ed.), The cambridge handbook of multimedia learning (pp. 49–69). New York: Cambridge University Press.

    Google Scholar 

  80. Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13(2), 141–156. https://doi.org/10.1016/S0959-4752(02)00017-8.

    Article  Google Scholar 

  81. Schooler, J. W., Fiore, S., & Brandimonte, M. A. (1997). At a loss from words: Verbal overshadowing of perceptual memories. Psychology of Learning and Motivation: Advances in Research and Theory, 37, 291–340. 10.1016/S0079-7421(08)60505-8.

    Article  Google Scholar 

  82. Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13(2), 227–237. 10.1016/S0959-4752(02)00022-1.

    Article  Google Scholar 

  83. Seufert, T., & Brünken, R. (2006). Cognitive load and the format of instructional aids for coherence formation. Applied Cognitive Psychology, 20, 321–331. 10.1002/acp.1248.

    Article  Google Scholar 

  84. Shanks, D. (2005). Implicit learning. In K. Lamberts & R. Goldstone (Eds.), Handbook of cognition (pp. 202–220). London: Sage.

    Google Scholar 

  85. Stern, E., Aprea, C., & Ebner, H. G. (2003). Improving cross-content transfer in text processing by means of active graphical representation. Learning and Instruction, 13(2), 191–203. 10.1016/S0959-4752(02)00020-8.

    Article  Google Scholar 

  86. Stieff, M. (2005). Connected chemistry—A novel modeling environment for the chemistry classroom. Journal of Chemical Education, 82(3), 489–493. 10.1021/ed082p489.

    Article  Google Scholar 

  87. Stieff, M. (2007). Mental rotation and diagrammatic reasoning in science. Learning and Instruction, 17(2), 219–234. 10.1016/j.learninstruc.2007.01.012.

    Article  Google Scholar 

  88. Stieff, M., Hegarty, M., & Deslongchamps, G. (2011). Identifying representational competence with multi-representational displays. Cognition and Instruction, 29(1), 123–145. 10.1080/07370008.2010.507318.

    Article  Google Scholar 

  89. Sweller, J., van Merrienboër, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.

    Article  Google Scholar 

  90. Taber, K. S. (2014). The significance of implicit knowledge for learning and teaching chemistry. Chemistry Education Research and Practice, 15, 447–461.

    Article  Google Scholar 

  91. Treagust, D. F., & Tsui, C.-Y. (2013). Conclusion: Contributions of multiple representations to biological education. In Multiple representations in biological education (pp. 349–367). Dordrecht: Springer.

  92. Urban-Woldron, J. (2009). Interactive simulations for the effective learning of physics. Journal of Computers in Mathematics and Science Teaching, 28(2), 163–176.

    Google Scholar 

  93. Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., et al. (2013). The malleability of spatial skills: A meta-analysis of training studies. Psychological Bulletin, 139(2), 352–402. 10.1037/a0028446.

    Article  Google Scholar 

  94. van der Meij, J., & de Jong, T. (2006). Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment. Learning and Instruction, 16(3), 199–212. https://doi.org/10.1016/j.learninstruc.2006.03.007.

    Article  Google Scholar 

  95. van Merrienboër, J. J. G., Clark, R. E., & de Croock, M. B. M. (2002). Blueprints for complex learning: The 4C/ID-model. Educational Technology Research and Development, 50(2), 39–64.

    Article  Google Scholar 

  96. VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems and other tutoring systems. Educational Psychologist, 46(4), 197–221. 10.1080/00461520.2011.611369.

    Article  Google Scholar 

  97. Vreman-de Olde, C., & De Jong, T. (2007). Scaffolding learners in designing investigation assignments for a computer simulation. Journal of Computer Assisted Learning, 22, 63–73. https://doi.org/10.1111/j.1365-2729.2006.00160.x.

    Article  Google Scholar 

  98. Wertsch, J. V., & Kazak, S. (2011). Saying more than you know in instructional settings. In T. Koschmann (Ed.), Theories of learning and studies of instructional practice (pp. 153–166). New York: Springer. 10.1007/978-1-4419-7582-9_9.

  99. Wibraham, A. C. (2005). Chemistry. Prentice Hall.

  100. Wise, J. A., Kubose, T., Chang, N., Russell, A., & Kellman, P. J. (2000). Perceptual learning modules in mathematics and science instruction. In P. Hoffman & D. Lemke (Eds.), Teaching and learning in a network world (pp. 169–176). Amsterdam: IOS Press.

    Google Scholar 

  101. Wu, H. K., & Shah, P. (2004). Exploring visuospatial thinking in chemistry learning. Science Education, 88(3), 465–492. 10.1002/sce.10126.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Science Foundation, Award 1611782, by the UW—Madison Graduate School and the Wisconsin Center for Education Research. I thank Amanda Evenstone, Joseph Michaelis, Oana Martin, Abigail Dreps, Brady Cleveland, William Keesler, Taryn Gordon, and Theresa Shim for their contributions.

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Appendix: Sample items from the chemistry knowledge test

Appendix: Sample items from the chemistry knowledge test

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Rau, M.A. Making connections among multiple visual representations: how do sense-making skills and perceptual fluency relate to learning of chemistry knowledge?. Instr Sci 46, 209–243 (2018). https://doi.org/10.1007/s11251-017-9431-3

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Keywords

  • Multiple representations
  • Chemistry
  • Connection making
  • Sense making
  • Perceptual fluency