Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Instructional Science Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. 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

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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.

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

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

  • 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 

  • 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.

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

    Article  Google Scholar 

  • 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 

  • 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 

  • 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 

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

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • 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 

  • 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.

  • 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.

    Chapter  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • 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 

  • 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 

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

    Article  Google Scholar 

  • 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 

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

    Google Scholar 

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

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Chapter  Google Scholar 

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

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

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

    Chapter  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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 

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

  • 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 

  • 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.

    Chapter  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • 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 

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

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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.

    Chapter  Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

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

    Google Scholar 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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.

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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 

  • 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.

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

  • 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 

  • 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 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Educational Psychology, University of Wisconsin—Madison, 1025 W Johnson St, Madison, WI, 53706, USA

    Martina A. Rau

Authors
  1. Martina A. Rau
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martina A. Rau.

Appendix: Sample items from the chemistry knowledge test

Appendix: Sample items from the chemistry knowledge test

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11251-017-9431-3

Keywords

Search

Navigation