Abstract
Visual representations play a critical role in enhancing science, technology, engineering, and mathematics (STEM) learning. Educational psychology research shows that adding visual representations to text can enhance students’ learning of content knowledge, compared to text-only. But should students learn with a single type of visual representation or with multiple different types of visual representations? This article addresses this question from the perspective of the representation dilemma, namely that students often learn content they do not yet understand from representations they do not yet understand. To benefit from visual representations, students therefore need representational competencies, that is, knowledge about how visual representations depict information about the content. This article reviews literature on representational competencies involved in students’ learning of content knowledge. Building on this review, this article analyzes how the number of visual representations affects the role these representational competencies play during students’ learning of content knowledge. To this end, the article compares two common scenarios: text plus a single type of visual representations (T+SV) and text plus multiple types of visual representations (T+MV). The comparison yields seven hypotheses that describe under which conditions T+MV scenarios are more effective than T+SV scenarios. Finally, the article reviews empirical evidence for each hypothesis and discusses open questions about the representation dilemma.
Similar content being viewed by others
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. doi:10.1080/03055698.2012.674636.
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. doi:10.1080/01443410.2013.785064.
Ackerman, P. L. (2003). Cognitive ability and non-ability trait determinants of expertise. Educational Researcher, 32(8), 15–20. doi:10.3102/0013189X032008015.
Ainsworth, S. (1999). Designing effective multi-representational learning environments (Technical Report 58). University of Nottingham: ESRC Centre for Research in Development, Instruction & Training Department of Psychology. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.422.8804&rep=rep1&type=pdf .
Ainsworth, S. (2006). DeFT: a conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198. doi:10.1016/j.learninstruc.2006.03.001.
Ainsworth, S. (2008a). The educational value of multiple-representations when learning complex scientific concepts. In J. K. Gilbert, M. Reiner, & A. Nakama (Eds.), Visualization: theory and practice in science education (pp. 191–208). Amsterdam, The Netherlands: Springer.
Ainsworth, S. (2008b). How do animations influence learning? In D. Robinson & G. Schraw (Eds.), Current perspectives on cognition, learning, and iinstruction: recent innovations in educational technology that facilitate student learning (pp. 37–67). Charlotte, NC: Information Age Publishing Inc.
Ainsworth, S. (2014). The multiple representation principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 464–486). New York, NY: Cambridge University Press. doi:10.1007/978-1-4020-5267-5_9.
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. doi:10.1207/S15327809JLS1101_2.
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. doi:10.1002/tea.20265.
Arcavi, A. (2003). The role of visual representations in the learning of mathematics. Educational Studies in Mathematics, 52(3), 215–241. doi:10.1023/A:1024312321077.
Baddeley, A. (1992). Working memory. Science, 255(5044), 556–559.
Baddeley, A. (2012). Working memory: theories, models, and controversies. Annual Review of Psychology, 63, 1–29. doi:10.1146/annurev-psych-120710-100422.
Baetge, I., & Seufert, T. (2010). Effects of support for coherence formation in computer science education. Ulm, Germany: Paper presented at EARLI SIG 6/7: Instructional Design and Learning and Instruction with Computers.
Baker, R. S., Corbett, A. T., & Koedinger, K. R. (2002). The Resilience of Overgeneralization of Knowledge About Data Representations. New Orleans, LA: Paper presented at the American Educational Research Association Conference.
Bandura, A. (2001). Social cognitive theory: an agentic perspective. Annual Review of Psychology, 52(1), 1–26. doi:10.1146/annurev.psych.52.1.1.
Berthold, K., & Renkl, A. (2009). Instructional aids to support a conceptual understanding of multiple representations. Journal of Educational Research, 101(1), 70–87. doi:10.1037/a0013247.
Betrancourt, M. (2005). The animation and interactivity principles in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 287–296). New York: Cambridge University Press.
Bodemer, D., & Faust, U. (2006). External and mental referencing of multiple representations. Computers in Human Behavior, 22(1), 27–42. doi:10.1016/j.chb.2005.01.005.
Bodner, G. M., & Domin, D. S. (2000). Mental models: the role of representations in problem solving in chemistry. University Chemistry Education, 4(1), 24–30.
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.
Braden, R. A., & Hortin, J. A. (1981). Identifying the theoretical foundations of visual literacy. Lexington, KY: Paper presented at the Annual Conference on Visual Literacy.
Braithwaite, D. W., & Goldstone, R. L. (2013). Integrating formal and grounded representations in combinatorics learning. Journal of Educational Psychology, 105(3), 666–682. doi:10.1037/a0032095.
Carmichael, A., Larson, A., Gire, E., Loschky, L., & Rebello, N. S. (2010). How does visual attention differ between experts and novices on physics problems? Proceedings of the American Institute of Physics (AIP) Conference, 1289, 93. doi:10.1063/1.3515257.
Carney, R. N., & Levin, J. R. (2002). Pictorial illustrations still improve students’ learning from text. Educational Psychology Review, 14(1), 5–26. doi:10.1023/A:1013176309260.
Carpenter, T. P. (1971). The performance of first grade students on a nonstandard set of measurement tasks (Report WURDCCL-TR-211). Washington, DC: National Center for Educational Research and Development. Retrieved from http://files.eric.ed.gov/fulltext/ED070662.pdf .
Carraher, D., & Schliemann, A. (2002). The transfer dilemma. The Journal of the Learning Sciences, 11(1), 1–24. doi:10.1207/S15327809JLS1101_1.
Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8(4), 293–332. doi:10.1207/s1532690xci0804_2.
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. doi:10.1007/s10649-006-9036-2.
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4(1), 55–81. doi:10.1016/0010-0285(73)90004-2.
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. doi:10.1016/0364-0213(89)90002-5.
Chi, M. T. H., de Leeuw, N., Chiu, M. H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477. doi:10.1016/0364-0213(94)90016-7.
Chi, M. T. H., Feltovitch, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152. doi:10.1207/s15516709cog0502_2.
Cobb, P. (1995). Cultural tools and mathematical learning: a case study. Journal for Research in Mathematics Education, 26(4), 362–385. doi:10.2307/749480.
Cobb, P., & McClain, K. (2006). Guiding inquiry-based math learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (1st ed., pp. 171–186). New York, NY: Cambridge University Press.
Coll, R. K., & Treagust, D. F. (2003). Investigation of secondary school, undergraduate, and graduate learners’ mental models of ionic bonding. Journal of Research in Science Teaching, 40(5), 464–486. doi:10.1002/tea.10085.
Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction: essays in honour of Robert Glaser. Hillsdale, NJ: Erlbaum.
Collins, A., & Kapur, M. (2014). Cognitive apprenticeship. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 109–127). New York, NY: Cambridge University Press.
Confrey, J., & Maloney, A. (2010). The construction, refinement, and early validation of the equipartitioning learning trajectory. In K. Gomez, L. Lyons, & J. Radinsky (Eds.), Proceedings of the 9th International Conference of the Learning Sciences (Vol. 1, pp. 968–973). Chicago, IL: International Society of the Learning Sciences.
Cook, M., Wiebe, E. N., & Carter, G. (2007). The influence of prior knowledge on viewing and interpreting graphics with macroscopic and molecular representations. Science Education, 92(5), 848–867. doi:10.1002/sce.20262.
Cooper, M. M., Underwood, S. M., & Hilley, C. Z. (2012). Development and validation of the implicit information from lewis structures instrument (IILSI): do students connect structures with properties? Chemistry Education Research and Practice, 13(3), 195–200. doi:10.1039/C2RP00010E.
Cope, A. C., Bezemer, J., Kneebone, R., & Lingard, L. (2015). ‘You see?’ Teaching and learning how to interpret visual cues during surgery. Medical Education, 49(11), 1103–1116. doi:10.1111/medu.12780.
Corradi, D., Elen, J., & Clareboug, G. (2012). Understanding and enhancing the use of multiple external representations in chemistry education. Journal of Science Education and Technology, 21(6), 780–795. doi:10.1007/s10956-012-9366-z.
Cox, R. (1999). Representation construction, externalised cognition and individual differences. Learning and Instruction, 9(4), 343–363. doi:10.1016/S0959-4752(98)00051-6.
Cox, R., & Brna, P. (1995). Supporting the use of external representations in problem solving: the need for flexible learning environments. Journal of Artificial Intelligence in Education, 6, 239–302.
Cramer, K., & Henry, A. (2002). Using manipulative models to build number sense for addition of fractions. In B. Litwiller & G. Bright (Eds.), Making sense of fractions, ratios, and proportions. Reston, VA: National Council of Teachers of Mathematics.
Cramer, K., & Wyberg, T. (2009). Efficacy of different concrete models for teaching the part-whole construct for fractions. Mathematical Thinking and Learning, 11(4), 226–257. doi:10.1080/10986060903246479.
Davidowitz, B., & Chittleborough, G. (2009). Linking the macroscopic and sub-microscopic levels: diagrams. In J. K. Gilbert & D. F. Treagust (Eds.), Multiple representations in chemical education (pp. 169–191). Dordrecht, Netherlands: Springer.
de Jong, T., Ainsworth, S., Dobson, M., Van der Meij, J., Levonen, J., Reimann, P., & Swaak, J. (1998). Acquiring knowledge in science and mathematics: the use of multiple representations in technology-based learning environments. In M. W. Van Someren, W. Reimers, H. P. A. Boshuizen, & T. de Jong (Eds.), Learning with multiple representations (pp. 9–41). Bingley, UK: Emerald Group Publishing Limited.
de Jong, T., Linn, M. C., & Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. science. Science, 340(6130), 305–308. doi:10.1126/science.1230579.
Deliyianni, E., Gagatsis, A., Elia, I., & Panaoura, A. (2015). Representational flexibility and problem-solving ability in fraction and decimal number addition: a structural model. International Journal of Science and Mathematics Education, 1-21. doi:10.1007/s10763-015-9625-6
DeLoache, J. S. (2000). Dual representation and young children’s use of scale models. Child Development, 71(2), 329–338. doi:10.1111/1467-8624.00148.
DeLoache, J. S., & Marzolf, D. P. (1992). When a picture is not worth a thousand words: young children’s understanding of pictures and models. Cognitive Development, 7(3), 317–329. doi:10.1016/0885-2014(92)90019-N.
diSessa, A. A., & Sherin, B. L. (2000). Meta-representation: an introduction. The Journal of Mathematical Behavior, 19(4), 385–398. doi:10.1016/S0732-3123(01)00051-7.
Eastwood, M. L. (2013). Fastest fingers: a molecule-building game for teaching organic chemistry. Journal of Chemical Education, 90(8), 1038–1041. doi:10.1021/ed3004462.
Egan, M. H., & McDonald, C. (2014). Program visualization and explanation for novice c programmers. In J. Whalley & D. D’Souza (Eds.), Proceedings of the Sixteenth Australasian Computing Education Conference (ACE2014) (Vol. 148, pp. 51–57). Darlinghurst, Australia: Australian Computer Society, Inc.
Eilam, B. (2013). Possible constraints of visualization in biology: challenges in learning with multiple representations. In D. F. Treagust & C.-Y. Tsui (Eds.), Multiple representations in biological education (pp. 55–73). Dordrecht, Netherlands: Springer.
Eilam, B., & Ben-Peretz, M. (2012). Teaching, learning, and visual literacy: the dual role of visual representation. Cambridge University Press.
Eitel, A., Scheiter, K., & Schüler, A. (2013). How inspecting a picture affects processing of text. Applied Cognitive Psychology, 28, 48–63. doi:10.1002/acp.2922.
Even, R. (1990). Subject matter knowledge for teaching and the case of functions. Educational Studies in Mathematics, 21(6), 521–544. doi:10.1007/BF00315943.
Even, R. (1998). Factors involved in linking representations of functions. The Journal of Mathamtical Behavior, 17(1), 105–121. doi:10.1016/S0732-3123(99)80063-7.
Fiorella, L., & Mayer, R. E. (2015). Eight ways to promote generative learning. Educational Psychology Review, 1-25. doi: 10.1007/s10648-015-9348-9
Furio, C., Calatayud, M. L., Barcenas, S. L., & Padilla, O. M. (2000). Functional fixedness and function reduction as common sense reasonings in chemical equilibrium and in geometry and polarity of molecules. Science Education, 84(5), 545–565. doi:10.1002/1098-237X(200009)84:5<545::AID-SCE1>3.0.CO;2-1.
Fuson, K. C., & Abrahamson, D. (2005). Understanding ratio and proportion as an example of the apprehending zone and conceptual-phase problem-solving models. In J. I. D. Campbell (Ed.), Handbook of mathematical cognition. New York, NY: Psychology Press.
Fyfe, E. R., McNeil, N. M., Son, J. Y., & Goldstone, R. L. (2014). Concreteness fading in mathematics and science instruction: a systematic review. Educational Psychology Review, 26(1), 9–25. doi:10.1007/s10648-014-9249-3.
Gabel, D. L., & Bunce, D. M. (1994). Research on problem solving: chemistry. In D. L. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 301–326). New York, NY: MacMillan.
Gagatsis, A., & Elia, I. (2004). The effects of different modes of representation on mathematical problem solving. In M. J. Høines & A. B. Fuglestad (Eds.), Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education (Vol. 2, pp. 447–454). Bergen: Bergen University College.
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. doi:10.1007/s10648-011-9174-7.
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science, 7(2), 155–170. doi:10.1207/s15516709cog0702_3.
Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: a general role for analogical encoding. Journal of Educational Psychology, 95(2), 393–405. doi:10.1037/0022-0663.95.2.393.
Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52(1), 45–56. doi:10.1037/0003-066X.52.1.45.
Gibson, E. J. (1969). Principles of perceptual learning and development. New York: Prentice Hall.
Gibson, E. J. (2000). Perceptual learning in development: some basic concepts. Ecological Psychology, 12(4), 295–302. doi:10.1207/S15326969ECO1204_04.
Gilbert, J. K. (2005). Visualization: a metacognitive skill in science and science education. In J. K. Gilbert (Ed.), Visualization in science education (pp. 9–27). Dordrecht, Netherlands: Springer.
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): Springer.
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, Netherlands: Springer.
Goldstone, R. (1997). Perceptual learning. San Diego, CA: Academic Press.
Goldstone, R. L., & Barsalou, L. W. (1998). Reuniting perception and conception. Cognition, 65(2), 231–262. doi:10.1016/S0010-0277(97)00047-4.
Goldstone, R. L., Schyns, P. G., & Medin, D. L. (1997). Learning to bridge between perception and cognition. Psychology of Learning and Motivation, 36, 1–14. doi:10.1016/S0079-7421(08)60279-0.
Goldstone, R. L., & Son, J. Y. (2005). The transfer of scientific principles using concrete and idealized simulations. Journal of the Learning Sciences, 14(1), 69–110. doi:10.1207/s15327809jls1401_4.
Grawemeyer, B. (2006). Evaluation of ERST—an external representation selection tutor. In D. Barker-Plummer, R. Cox, & N. Swoboda (Eds.), Diagrammatic representation and inference (pp. 154–167). Berlin/Heidelberg: Springer. doi:10.1007/11783183_21.
Greene, J. A., Hutchison, L. A., Costa, L. J., & Crompton, H. (2012). Investigating how college students’ task definitions and plans relate to self-regulated learning processing and understanding of a complex science topic. Contemporary Educational Psychology, 37(4), 307–320. doi:10.1016/j.cedpsych.2012.02.002.
Greeno, J. G., & Engeström, Y. (2014). Learning in activity. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences. Cambridge, England: Cambridge University Press.
Gruber, H., Graf, M., Mandl, H., Renkl, A., & Stark, R. (1995). Fostering Applicable Knowledge by Multiple Perspectives and Guided Problem Solving. Nijmergen, The Netherlands: Paper presented at the 6th Conference of the European Association for Research on Learning and Instruction.
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. doi:10.1207/S1532690XCI1703_2.
Harel, A. (2015). What is special about expertise? Visual expertise reveals the interactive nature of real-world object recognition. Neuropsychologia, 83, 88–99. doi:10.1016/j.neuropsychologia.2015.06.004.
Hartman, H. J. (2002). Metacognition in learning and instruction. The Netherlands: Kluwer Academic Publisher.
Hattie, J. (2012). Visible learning: a synthesis of over 800 meta-analyses relating to achievement. New York, NY: Routledge.
Hecht, S. A., Vagi, K. J., Torgesen, J. K., Berch, D. B., & Mazzocco, M. M. M. (2007). Fraction skills and proportional reasoning. In D. B. Berch & M. M. M. Mazzocco (Eds.), Why is math so hard for some children? The nature and origins of mathematical learning difficulties and disabilities (pp. 121–132). Baltimore, MD, US: Paul H Brookes Publishing.
Hegarty, M., & Just, M. A. (1993). Constructing mental models of machines from text and diagrams. Journal of Memory and Language, 32(6), 717–742. doi:10.1006/jmla.1993.1036.
Hegarty, M., & Waller, D. A. (2005). Individual differences in spatial abilities. In P. Shah & A. Miyake (Eds.), The Cambridge handbook of visuospatial thinking (pp. 121–169). New York, NY: Cambridge University Press. doi:10.1017/CBO9780511610448.005.
Hill, M. J. (2015). Scientific representational fluency: defining, diagnosing, and developing. Sydney: Doctoral dissertation, University of Sydney. Retrieved from http://ses.library.usyd.edu.au/bitstream/2123/14194/1/hill_mj_thesis.pdf .
Hinze, S. R., Rapp, D. N., Williamson, V. M., Shultz, M. J., Deslongchamps, G., & Williamson, K. C. (2013a). Beyond ball-and-stick: students’ processing of novel stem visualizations. Learning and Instruction, 26, 12–21. doi:10.1016/j.learninstruc.2012.12.002.
Hinze, S. R., Williamson, V. M., Shultz, M. J., Williamson, K. C., Deslongchamps, G., & Rapp, D. N. (2013b). When do spatial abilities support student comprehension of stem visualizations? Cognitive Processing, 14(2), 129–142. doi:10.1007/s10339-013-0539-3.
Holzinger, A., Kickmeier-Rust, M. D., & Albert, D. (2008). Dynamic media in computer science education; content complexity and learning performance: Is less more? Educational Technology & Society, 11(1), 279–290.
Jacobs, J. E., & Paris, S. G. (1987). Children’s metacognition about reading: issues in definition, measurement, and instruction. Educational Psychologist, 22(3-4), 255–278. doi:10.1080/00461520.1987.9653052.
Janvier, C., Girardon, C., & Morand, J. (1993). Mathematical symbols and representations. In P. S. Wilson (Ed.), Research ideas for the classroom: high school mathematics (pp. 79–102). Reston, VA: National Council of Teachers of Mathematics.
Justi, R., & Gilbert, J. K. (2002). Models and modelling in chemical education. In O. de Jong, R. Justi, D. F. Treagust, & J. H. van Driel (Eds.), Chemical education: towards research-based practice (pp. 47–68). Dordrecht, The Netherlands: Kluwer Academic Publishers.
Justi, R., Gilbert, J. K., & Ferreira, P. F. (2009). The application of a “model of modelling” to illustrate the importance of metavisualisation in respect of the three types of representation. In J. K. Gilbert & D. F. Treagust (Eds.), Multiple representations in chemical education (pp. 285–307). Berlin/Heidelberg: Springer.
Kalyuga, S., & Singh, A. M. (2015). Rethinking the boundaries of cognitive load theory in complex learning. Educational Psychology Review, 1-22. doi:10.1007/s10648-015-9352-0
Kellman, P. J., & Garrigan, P. B. (2009). Perceptual learning and human expertise. Physics of Life Reviews, 6(2), 53–84. doi:10.1016/j.plrev.2008.12.001.
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, NY: Elsevier Academic Press.
Kellman, P. J., Massey, C. M., Roth, Z., Burke, T., Zucker, J., Saw, A., & Wise, J. (2008). Perceptual learning and the technology of expertise: studies in fraction learning and algebra. Pragmatics & Cognition, 16(2), 356–405. doi:10.1075/pc.16.2.07kel.
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. doi:10.1111/j.1756-8765.2009.01053.x.
Kieren, T. E. (1993). Rational and fractional numbers: from quotient fields to recursive understanding. In T. P. Carpenter, E. Fennema, & T. A. Romberg (Eds.), Rational numbers: an integration of research. Hillsdale, NJ: Erlbaum.
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. doi:10.1111/j.1551-6709.2012.01245.x.
Kohl, P. B., & Finkelstein, N. D. (2008). Patterns of multiple representation use by experts and novices during physics problem solving. Physical Review Special Topics-Physics Education Research, 4, 010111. doi:10.1103/PhysRevSTPER.4.010111.
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. doi:10.1207/s15327809jls0902_1.
Kozma, R. B., & Russell, J. (1997). Multimedia and understanding: expert and novice responses to different representations of chemical phenomena. Journal of Research in Science Teaching, 34(9), 949–968. doi:10.1002/(SICI)1098-2736(199711)34:9<949::AID-TEA7>3.0.CO;2-U.
Kozma, R., & Russell, J. (2005). Students becoming chemists: developing representationl competence. In J. Gilbert (Ed.), Visualization in science education (pp. 121–145). Dordrecht, Netherlands: Springer.
Lamon, S. J. (Ed.). (1999). Teaching fractions and ratios for understanding. Mahwah, NJ: Lawrence Erlbaum Associates.
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. doi:10.1111/j.1551-6708.1987.tb00863.x.
Lave, J., & Wenger, E. (1991). Situated learning: legitimate peripheral participation. Cambridge, UK: Cambridge University Press.
Linenberger, K. J., & Bretz, S. L. (2012). Generating cognitive dissonance in student interviews through multiple representations. Chemistry Education Research and Practice, 13(3), 172–178. doi:10.1039/C1RP90064A.
Linn, M. C., Eylon, B. S., Rafferty, A., & Vitale, J. M. (2015). Designing instruction to improve lifelong inquiry learning. Eurasia Journal of Mathematics, Science & Technology Education, 11(2), 217–225. doi:10.12973/eurasia.2015.1317a.
Magner, U. I., Schwonke, R., Aleven, V., Popescu, O., & Renkl, A. (2012). Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments. Learning and Instruction, 29, 141–152. doi:10.1016/j.learninstruc.2012.07.002.
Mason, L., Pluchino, P., Tornatora, M. C., & Ariasi, N. (2013a). An eye-tracking study of learning from science text with concrete and abstract illustrations. The Journal of Experimental Education, 81(3), 356–384. doi:10.1080/00220973.2012.727885.
Mason, L., Pluchino, P., & Tornatora, M. C. (2013b). Effects of picture labeling on science text processing and learning: evidence from eye movements. Reading Research Quarterly, 48(2), 199–214. doi:10.1002/rrq.41.
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, NY: Routledge.
Mayer, R. E. (2005). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 31–48). New York, NY: Cambridge University Press.
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, NY: Cambridge University Press.
Mayer, R. E., & Feldon, D. (2014). Five common but questionable principles of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 97–116). New York, NY: Cambridge University Press.
Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82(4), 715–726. doi:10.1037/0022-0663.82.4.715.
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52. doi:10.1207/S15326985EP3801_6.
McElhaney, K. W., Chang, H. Y., Chiu, J. L., & Linn, M. C. (2015). Evidence for effective uses of dynamic visualisations in science curriculum materials. Studies in Science Education, 51(1), 49–85. doi:10.1080/03057267.2014.984506.
McKendree, J., Small, C., Stenning, K., & Conlon, T. (2002). The role of representation in teaching and learning critical thinking. Educational Review, 54(1), 57–67. doi:10.1080/00131910120110884.
McNeil, N. M., & Fyfe, E. R. (2012). “Concreteness fading” promotes transfer of mathematical knowledge. Learning and Instruction, 22(6), 440–448. doi:10.1016/j.learninstruc.2012.05.001.
McNeil, N., & Jarvin, L. (2007). When theories don’t add up: disentangling the manipulatives debate. Theory Into Practice, 46(4), 309–316. doi:10.1080/00405840701593899.
McNeil, N. M., Uttal, D. H., Jarvin, L., & Sternberg, R. J. (2009). Should you show me the money? Concrete objects both hurt and help performance on mathematics problems. Learning and Instruction, 19(2), 171–184. doi:10.1016/j.learninstruc.2008.03.005.
Middendorf, J., & Pace, D. (2004). Decoding the disciplines: a model for helping students learn disciplinary ways of thinking. New Directions for Teaching and Learning, 98, 1–12. doi:10.1002/tl.142.
Miura, I. T., & Yamagishi, J. M. (2002). The development of rational number sense. In B. Litwiller & G. Bright (Eds.), Making sense of fractions, ratios, and proportions. Reston, VA: National Council of Teachers of Mathematics.
Moreira, R. F. (2013). A game for the early and rapid assimilation of organic nomenclature. Journal of Chemical Education, 90(8), 1035–1037. doi:10.1021/ed300473r.
Moreno, R., Ozogul, G., & Reisslein, M. (2011). Teaching with concrete and abstract visual representations: effects on students’ problem solving, problem representations, and learning perceptions. Journal of Educational Psychology, 103(1), 32–47. doi:10.1037/a0021995.
Moss, J. (2005). Pipes, tubes, and beakers: New approaches to teaching the rational-number system. In J. Brantsford & S. Donovan (Eds.), How people learn: a targeted report for teachers (pp. 309-349): National Academy Press.
Moyer, P., Bolyard, J., & Spikell, M. A. (2002). What are virtual manipulatives? Teaching Children Mathematics, 8, 372–377.
Nakiboglu, C. (2003). Instructional misconceptions of Turkish prospective chemistry teachers about atomic orbitals and hybridization. Chemistry Education Research and Practice, 4(2), 171–188. doi:10.1039/B2RP90043B.
Nathan, M. J., Walkington, C. A., Srisurichan, R., & Alibali, M. W. (2011). Modal engagements in precollege engineering: tracking math and science concepts across symbols, sketches, software, silicone and wood. In Proceedings of the 118th American Society for Engineering Education. Vancouver, BC, Canada: American Society for Engineering Education.
NCES. (2011). The Nation’s Report Card: Reading 2011. Washington, D.C.: National Center for Education Statistics.
NCTM. (2000). Principles and standards for school mathematics. Reston, VA: National Council of Teachers of Mathematics.
NCTM. (2006). Curriculum focal points for prekindergarten through grade 8 mathematics: a quest for coherence. Reston, VA.
NGSS (2013). Next Generataion Science Standards. Retrieved from http://www.nextgenscience.org/next-generation-science-standards
Nicoll, G. (2001). A report of undergraduates’ bonding misconceptions. International Journal of Science Education, 23(7), 707–730. doi:10.1080/09500690010025012.
Nistal, A. A., Van Dooren, W., Clarebout, G., Elen, J., & Verschaffel, L. (2010a). Representational flexibility in linear-function problems: a choice/no-choice study. In L. Verschaffel, E. De Corte, J. Elen, & T. D. Jong (Eds.), Representational flexibility in function problems (pp. 74–93). Oxon: Routledge.
Nistal, A., Van Dooren, W., & Verschaffel, L. (2010b, August 26-28). Representational flexibility in linear function problems. Paper presented at the EARLI SIG 2: Comprehension of Text and Graphics Tracing the Mind: How do We Learn from Text and Graphics? Tübingen, Germany
Nistal, A., Van Dooren, W., & Verschaffel, L. (2011). What counts as a flexible representational choice? An evaluation of students’ representational choices to solve linear function problems. Instructional Science, 39(1), 1–21. doi:10.1007/s11251-011-9199-9.
NMAP. (2008). Foundations for Success: Report of the National Mathematics Advisory Board Panel: U.S. Government Printing Office.
Northedge, A. (2002). Organizing excursions into specialist discourse communities: a sociocultural account of university teaching. In G. Wells & G. Claxton (Eds.), Learning for life in the 21st century. Oxford, UK: Blackwell Publishing Ltd.
Northedge, A. (2003). Enabling participation in academic discourse. Teaching in Higher Education, 8(2), 169–180. doi:10.1080/1356251032000052429.
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. doi:10.1023/A:1002943821419.
NRC. (1996). National science education standards. Washington, D.C.: National Academy Press.
NRC (Ed.). (2002). Scientific research in education. Washington, DC: National Academy Pres.
NRC. (2006). Learning to think spatially. Washington, D.C.: National Academies Press.
NSES (2013). National Science Education Standards. Retrieved from http://www.nap.edu/catalog/4962.html
OECD. (2010). PISA 2009 Results: Executive Summary. Washington, D.C.: US Government Printing Office.
Olympiou, G., & Zacharia, Z. C. (2012). Blending physical and virtual manipulatives: an effort to improve students’ conceptual understanding through science laboratory experimentation. Science Education, 96(1), 21–47. doi:10.1002/sce.20463.
Paivio, A. (1986). Mental representations: a dual coding approach. Oxford, UK: Oxford University Press.
Pape, S. J., & Tchoshanov, M. A. (2001). The role of representation(s) in developing mathematical understanding. Theory Into Practice, 40(2), 118–127. doi:10.1207/s15430421tip4002_6.
Patel, Y., & Dexter, S. (2014). Using multiple representations to build conceptual understanding in science and mathematics. In M. Searson & M. Ochoa (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2014 (Vol. 2014, pp. 1304–1309). Chesapeake, VA: AACE.
Peirce, C. S., Hartshorne, C., Weiss, P., & Burks, A. (1935). Collected Papers of Charles Sanders Peirce (Vol. I-VI). Cambridge, MA: Harvard University Press.
Pintrich, P. R. (2000). Multiple goals, multiple pathways: the role of goal orientation in learning and achievement. Journal of Educational Psychology, 92(3), 544–555. doi:10.1037/0022-0663.92.3.544.
Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686.
Proctor, R. M. J., Baturo, A. R., & Cooper, T. J. (2002). Integrating concrete and virtual materials in an elementary mathematics classroom: a case study of success with fractions. In A. McDougall, J. Murnane, & D. Chambers (Eds.), Proceedings of the 7th World Conference on Computers in Education. Darlinghurst, Australia: Australian Computer Society, Inc.
Rasch, T., & Schnotz, W. (2009). Interactive and non-interactive pictures in multimedia learning environments: effects on learning outcomes and learning efficiency. Learning and Instruction, 19(5), 411–422. doi:10.1016/j.learninstruc.2009.02.008.
Rathmell, E. C., & Leutzinger, L. P. (1991). Number representations and relationships. The Arithmetic Teacher, 38(7), 20–23.
Rau, M. A., & Wu, S. P. W. (2015). ITS support for conceptual and perceptual processes in learning with multiple graphical representations. In C. Conati, N. Heffernan, A. Mitrovic & M. F. Verdejo (Eds.), Artificial Intelligence in Education (pp. 398–407). Switzerland: Springer International Publishing.
Rau, M. A., Aleven, V., Rummel, N., & Rohrbach, S. (2012). Sense making alone doesn’t do it: Fluency matters too! ITS support for robust learning with multiple representations. In S. Cerri, W. Clancey, G. Papadourakis & K. Panourgia (Eds.), Intelligent tutoring systems (Vol. 7315, pp. 174–184). Berlin/Heidelberg: Springer.
Rau, M. A., Aleven, V., & Rummel, N. (2013a). Interleaved practice in multi-dimensional learning tasks: which dimension should we interleave? Learning and Instruction, 23, 98–114.
Rau, M. A., Aleven, V., & Rummel, N. (2013b). Complementary effects of sense-making and fluencybuilding support for connection making: A matter of sequence? In H. C. Lane, K. Yacef, J. Mostow & P. Pavlik (Eds.), Artificial Intelligence in Education (pp. 329–338). Berlin Heidelberg: Springer.
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. doi:10.1007/s40593-013-0011-7.
Rau, M.A., Aleven, V., & Rummel, N. (2015a). Successful learning with multiple graphical representations and self-explanation prompts. Journal of Educational Psychology, 107(1), 30–46. doi:10.1037/a0037211.
Rau, M. A., Michaelis, J. E., & Fay, N. (2015b). Connection making between multiple graphical representations: A multi-methods approach for domain-specific grounding of an intelligent tutoring system for chemistry. Computers and Education, 82, 460–485. doi:10.1016/j.compedu.2014.12.009.
Reiser, B. J., & Tabak, I. (2014). Scaffolding. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 44–62). New York, NY: Cambridge University Press.
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.
Roberts, M. J., Gilmore, D. J., & Wood, D. J. (1997). Individual differences and strategy selection in reasoning. British Journal of Psychology, 88(3), 473–492. doi:10.1111/j.2044-8295.1997.tb02652.x.
Rocke, A. J. (2010). Image and reality: Kekulé, Kopp, and the scientific imagination. Chicago, IL: The University of Chicago Press.
Roschelle, J. (1992). Learning by collaborating: convergent conceptual change. Journal of the Learning Sciences, 2(3), 235–276. doi:10.1207/s15327809jls0203_1.
Savec, V. F., Sajovic, I., & Grm, K. S. W. (2009). Action research to promote the formation of linkages by chemistry students between the macro, submicro, and symbolic representational levels. In J. K. Gilbert & D. F. Treagust (Eds.), Multiple representations in chemical education (pp. 309–331). Netherlands: Springer.
Sawyer, R. K. (2006). Analyzing collaborative discourse. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (1st ed., pp. 187–204). New York, NY: Cambridge University Press.
Scheiter, K., Schüler, A., Gerjets, P., Huk, T., & Hesse, F. W. (2014). Extending multimedia research: how do prerequisite knowledge and reading comprehension affect learning from text and pictures. Computers in Human Behavior, 31, 73–84. doi:10.1016/j.chb.2013.09.022.
Schnotz, W. (2005). An integrated model of text and picture comprehension. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (1st ed., pp. 49–69). New York, NY: Cambridge University Press.
Schnotz, W. (2014). An integrated model of text and picture comprehension. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 72–103). New York, NY: Cambridge University Press.
Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13(2), 141–156. doi:10.1016/S0959-4752(02)00017-8.
Schönborn, K. J., & Anderson, T. R. (2006). The importance of visual literacy in the education of biochemists. Biochemistry and Molecular Biology Education, 34(2), 94–102. doi:10.1002/bmb.2006.49403402094.
Schönborn, K. J., & Bögeholz, S. (2013). Experts’ views on translation across multiple external representations in acquiring biological knowledge about ecology, genetics, and evolution. In D. F. Treagust & C.-Y. Tsui (Eds.), Multiple representations in biological education (pp. 111–128). Netherlands: Springer.
Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13(2), 227–237. doi:10.1016/S0959-4752(02)00022-1.
Shanks, D. (2005). Implicit Learning. In K. Lamberts & R. Goldstone (Eds.), Handbook of cognition (pp. 202–220). London: Sage.
Shusterman, G. P., & Shusterman, A. J. (1997). Teaching chemistry with electron density models. Journal of Chemical Education, 74(7), 771–776. doi:10.1021/ed074p771.
Siegler, R. S., Carpenter, T., Fennell, F., Geary, D., Lewis, J., Okamoto, Y., & Wray, J. (2010). Developing effective fractions instruction: a practice guide. Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
Stieff, M. (2007). Mental rotation and diagrammatic reasoning in science. Learning and Instruction, 17(2), 219–234. doi:10.1016/j.learninstruc.2007.01.012.
Strickland, A. M., Kraft, A., & Bhattacharyya, G. (2010). What happens when representations fail to represent? Graduate students’ mental models of organic chemistry diagrams. Chemistry Education Research and Practice, 11(4), 293–301. doi:10.1039/C0RP90009E.
Taber, K. S. (1998). An alternative conceptual framework from chemistry education. International Journal of Science Education, 20(5), 597–608. doi:10.1080/0950069980200507.
Taber, K. S. (2013). Revisiting the chemistry triplet: drawing upon the nature of chemical knowledge and the psychology of learning to inform chemistry education. Chemistry Education Research and Practice, 14(2), 156–168. doi:10.1039/C3RP00012E.
Talanquer, V. (2006). Commonsense chemistry: a model for understanding students’ alternative conceptions. Journal of Chemical Education, 83(5), 811–817. doi:10.1021/ed083p811.
Thompson, D. R., & Saldanha, L. A. (2003). Fractions and multiplicative reasoning. In J. Kilpatrick, G. Martin, & D. Schrifter (Eds.), Research companion to the principles and standards for school mathematics. Reston, VA: NCTM.
Tobias, S. (1992). Disciplinary cultures and general education: what can we learn from our learners? Teaching Excellence, 4(6), 1–3.
Tversky, B. (2011). Visualizing thought. Topics in Cognitive Science, 3(3), 499–535. doi:10.1111/j.1756-8765.2010.01113.x.
Uesaka, Y., & Manalo, E. (2006). Active comparison as a means of promoting the development of abstract conditional knowledge and appropriate choice of diagrams in math word problem solving. In D. Barker-Plummer, R. Cox, & N. Swoboda (Eds.), Diagrammatic representation and inference (pp. 181–195). Berlin/Heidelberg: Springer. doi:10.1007/11783183_25.
Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., & Newcombe, N. S. (2013). The Malleability of spatial skills: a meta-analysis of training studies. Psychological Bulletin, 139(2), 352–402. doi:10.1037/a0028446.
Uttal, D. H., & O’Doherty, K. (2008). Comprehending and learning from ‘visualizations’: a developmental perspective. In J. Gilbert (Ed.), Visualization: theory and practice in science education (pp. 53–72). Netherlands: Springer.
Van der Meij, J., & de Jong, T. (2011). The effects of directive self-explanation prompts to support active processing of multiple representations in a simulation-based learning environment. Journal of Computer Assisted Learning, 27(5), 411–423. doi:10.1111/j.1365-2729.2011.00411.x.
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. doi:10.1007/s10648-005-8136-3.
Vygotsky, L. S. (1978a). Internalization of higher psychological functions. In M. W. Cole, V. John-Steiner, S. Scribner, & E. Souberman (Eds.), Mind in society (pp. 52–57). Cambridge, MA: Harvard University Press.
Vygotsky, L. S. (1978b). Interaction between learning and development. In M. W. Cole, V. John-Steiner, S. Scribner, & E. Souberman (Eds.), Mind in society (pp. 79–91). Cambridge, MA: Harvard University Press.
Wertsch, J. V. (1997). Properties of mediated action. In J. V. Wertsch (Ed.), Mind as action (pp. 23–72). New York: Oxford University Press.
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. doi:10.1007/978-1-4419-7582-9_9.
White, T., & Pea, R. (2011). Distributed by design: on the promises and pitfalls of collaborative learning with multiple representations. Journal of the Learning Sciences, 20(3), 489–547. doi:10.1080/10508406.2010.542700.
Winne, P. H., & Azevedo, R. (2014). Metacognition. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 63–87). New York, NY: Cambridge University Press.
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, The Netherlands: IOS Press.
Wu, H. K., & Shah, P. (2004). Exploring visuospatial thinking in chemistry learning. Science Education, 88(3), 465–492. doi:10.1002/sce.10126.
Wylie, R., & Chi, M. T. (2014). The self-explanation principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 413–432). New York, NY: Cambridge University Press.
Yanik, H. B., Helding, B., & Flores, A. (2008). Teaching the concept of unit in measurement interpretation of rational numbers. Elementary Education Online, 7(3), 693–705.
Yerushamly, M. (1991). Student perceptions of aspects of algebraic function using multiple representation software. Journal of Computer Assisted Learning, 7, 42–57. doi:10.1111/j.1365-2729.1991.tb00223.x.
Zacharia, Z. C., Olympiou, G., & Papaevripidou, M. (2008). Effects of experimenting with physical and virtual manipulatives on students’ conceptual understanding in heat and temperature. Journal of Research in Science Teaching, 45(9), 1021–1035. doi:10.1002/tea.20260.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Rau, M.A. Conditions for the Effectiveness of Multiple Visual Representations in Enhancing STEM Learning. Educ Psychol Rev 29, 717–761 (2017). https://doi.org/10.1007/s10648-016-9365-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10648-016-9365-3