Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-25T00:22:18.969Z Has data issue: false hasContentIssue false

Part III - Basic Principles of Multimedia Learning

Published online by Cambridge University Press:  19 November 2021

Edited by
Richard E. Mayer  and
Logan Fiorella
Richard E. Mayer
Affiliation:
University of California, Santa Barbara
Logan Fiorella
Affiliation:
University of Georgia
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Cambridge Handbook of Multimedia Learning , pp. 143 - 182
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

References

Adams, D. M., Mayer, R. E., MacNamara, A., Koening, A., & Wainess, R. (2012). Narrative games for learning: Testing the discovery and narrative hypothesis. Journal of Educational Psychology, 104, 235–249.CrossRefGoogle Scholar
Al-Seghayer, K. (2001). The effect of multimedia annotation modes on L2 vocabulary acquisition: A Comparative study. Language Learning & Technology, 5, 202–232.Google Scholar
Butcher, K. R. (2006). Learning from text and diagrams: Promoting mental model development and inference generation. Journal of Educational Psychology, 98, 182–197.Google Scholar
Butcher, K. R. (2014). The multimedia principle. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed; pp. 174–205). New York: Cambridge University Press.Google Scholar
Chun, D. M., & Plass, J. L. (1996). Effects of multimedia annotations on vocabulary acquisition. The Modern Language Journal, 80, 183–198.Google Scholar
Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53, 445–459.CrossRefGoogle Scholar
Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42, 21–30.Google Scholar
Clark, R. E. (ed.) (2001). Learning from Media. Greenwich, CT: Information Age Publishing.Google Scholar
Clark, R. E., & Salomon, G. (1986). Media in teaching. In Wittrock, M. C. (ed.), Handbook of Research on Teaching (3rd ed., pp. 464–478). New York: Macmillan.Google Scholar
Fleming, M., & Levie, W. H. (eds.) (1993). Instructional Message Design: Principles from the Behavioral and Cognitive Sciences (2nd ed). Englewood Cliffs, NJ: Educational Technology Publications.Google Scholar
Fletcher, J. D., & Tobias, S. (2005). The multimedia principle. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 117–134). New York: Cambridge University Press.CrossRefGoogle Scholar
Hattie, J. (2009). Visible Learning. New York: Routledge.Google Scholar
Jonassen, D. H., Campbell, J. P., & Davidson, M. E. (1994). Learning with media: Restructuring the debate. Educational Technology Research and Development, 42, 20–38.Google Scholar
Jones, L. C., & Plass, J. L. (2002). Supporting listening comprehension and vocabulary acquisition in French with multimedia annotations. The Modern Language Journal, 86, 446–561.Google Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise in instructional design. Human Factors, 40, 1–17.CrossRefGoogle Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner expertise into the design of multimedia instruction. Journal of Educational Psychology, 92, 126–136.Google Scholar
Kozma, R. B. (1994). Will media influence learning? Reframing the debate. Educational Technology Research and Development, 42, 7–19.CrossRefGoogle Scholar
Lee, H., & Mayer, R. E. (2015). Visual aids to learning in a second language: Adding video to an audio lecture. Applied Cognitive Psychology, 29, 445–454.Google Scholar
Levie, H. W., & Lentz, R. (1982). Effects of text illustrations: A review of research. Educational Communication and Technology Journal, 30, 195–232.Google Scholar
Levin, J. R., Anglin, G. J., & Carney, R. N. (1987). On empirically validating functions of pictures in pose. In Willows, D. M., & Houghton, H. A. (eds.), The Psychology of Illustration (Vol. 1, pp. 51–86). New York: Springer.CrossRefGoogle Scholar
Levin, J. R. & Mayer, R. E. (1993). Understanding illustrations in text. In Britton, B. K., Woodward, A., & Binkley, M. (eds.), Learning from Textbooks: Theory and Practice (pp. 95–113). Hillsdale, NJ: Erlbaum.Google Scholar
Mason, L., Pluchino, P., & Tornatora, M. C. (2013). Effects of picture labeling on science text processing and learning: Evidence from eye movements. Reading Research Quarterly, 48(2), 199–214.Google Scholar
Mayer, R. E. (1983). Can you repeat that? Qualitative effects of repetition and advance organizers on learning from science prose. Journal of Educational Psychology, 75, 40–49.Google Scholar
Mayer, R. E. (1989a). Systematic thinking fostered by illustrations in scientific text. Journal of Educational Psychology, 81, 240–246.Google Scholar
Mayer, R. E. (1989b). Models for understanding. Review of Educational Research, 59, 43–64.Google Scholar
Mayer, R. E. (1993). Illustrations that instruct. In Glaser, R. (ed.), Advances in Instructional Psychology (Vol. 4, pp. 253–284). Hillsdale, NJ: Erlbaum.Google Scholar
Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions? Educational Psychologist, 32, 1–19.Google Scholar
Mayer, R. E. (2021). Multimedia Learning (3rd ed). New York: Cambridge University Press.Google Scholar
Mayer, R. E., & Anderson, R. B. (1991). Animations need narrations: An experimental test of a dual-coding hypothesis. Journal of Educational Psychology, 83, 484–490.Google Scholar
Mayer, R. E., & Anderson, R. B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 84, 444–452.CrossRefGoogle Scholar
Mayer, R. E., Bove, W., Bryman, A., Mars, R., & Tapangco, L. (1996). When less is more: Meaningful learning from visual and verbal summaries of science textbook lessons. Journal of Educational Psychology, 88, 64–73.CrossRefGoogle Scholar
Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82, 715–726.CrossRefGoogle Scholar
Mayer, R. E., Hegarty, M., Mayer, S., & Campbell, J. (2005). When static media promote active learning: Annotated illustrations versus narrated animations in multimedia learning. Journal of Experimental Psychology: Applied, 11, 256–265.Google Scholar
Mayer, R. E., Lee, H., & Peebles, A. (2014). Multimedia learning in a second language: A cognitive load perspective. Applied Cognitive Psychology, 28, 653–660.Google Scholar
Mayer, R. E., Sims, V., & Tajika, H. (1995). A comparison of how textbooks teach mathematical problem solving in Japan and the United States. American Educational Research Journal, 32, 443–460.Google Scholar
Moreno, R., & Mayer, R. E. (1999). Multimedia-supported metaphors for meaning making in mathematics. Cognition and Instruction, 17, 215–248.CrossRefGoogle Scholar
Moreno, R. E., & Mayer, R. E. (2002). Learning science in virtual reality environments: Role of methods and media. Journal of Educational Psychology, 94, 598–610.CrossRefGoogle Scholar
Plass, J. L., Chun, D. M., Mayer, R. E., & Leutner, D. (1998). Supporting visual and verbal learning preferences in a second-language multimedia learning environment. Journal of Educational Psychology, 90, 25–36.CrossRefGoogle Scholar
Ponce, H., & Mayer, R. E. (2014). An eye-movement analysis of highlighting and graphic organizer study aids for learning from expository text. Computers in Human Behavior, 41, 21–32.CrossRefGoogle Scholar
Salomon, G. (1994). Interaction of Media, Cognition, and Learning. Hillsdale, NJ: Erlbaum.Google Scholar
Schmeck, A., Mayer, R. E., Opfermann, M., Pfeiffer, V., & Leutner, D. (2014). Drawing pictures during learning from scientific text: Testing the generative drawing effect and the prognostic drawing effect. Contemporary Educational Psychology, 39, 275–286.CrossRefGoogle Scholar
Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representations. Learning and Instruction, 13, 141–156.CrossRefGoogle Scholar
Stull, A., & Mayer, R. E. (2007). Learning by doing versus learning by viewing: Three experimental comparisons of learner-generated versus author-provided graphic organizers. Journal of Educational Psychology, 99, 808–820.Google Scholar
Sung, E., & Mayer, R. E. (2012). When graphics improve liking but not learning from online lessons. Computers in Human Behavior, 28, 1618–1625.Google Scholar
Wetzel, C. D., Radtke, P. H., & Stern, H. W. (1994). Instructional Effectiveness of Video Media. Hillsdale, NJ: Erlbaum.Google Scholar

References

Ainsworth, S. E. (1999). The functions of multiple representations. Computers & Education, 33(2–3), 131–152.Google Scholar
Ainsworth, S. E. (2006). Deft: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198.CrossRefGoogle Scholar
Ainsworth, S. E. (2014). The multiple representation principle in multimedia learning. In Mayer, R. (ed.), The Cambridge Handbook of Multimedia Learning (pp. 464–486). New York: Cambridge University Press.CrossRefGoogle Scholar
Ainsworth, S. E., Bibby, P. A., & Wood, D. J. (1997). Evaluating principles for multi-representational learning environments. Paper Presented at the 7th European Conference for Research on Learning and Instruction, 1997, Athens.Google Scholar
Ainsworth, S. E., Bibby, P. A., & Wood, D. J. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. Journal of the Learning Sciences, 11(1), 25–61.Google Scholar
Ainsworth, S. E., & Loizou, A. T. (2003). The effects of self-explaining when learning with text or diagrams. Cognitive Science, 27(4), 669–681.Google Scholar
Anzai, Y. (1991). Learning and use of representations for physics expertise. In Anders-Ericsson, K., & Smith, J. (eds.), Towards a General Theory of Expertise: Prospects and Limits (pp. 64–92). Cambridge: Cambridge University Press.Google Scholar
Berthold, K., Eysink, T. H. S., & Renkl, A. (2008). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37(4), 345–363.Google Scholar
Bertin, J. (1983). Semiology of Graphics: Diagrams, Networks, Maps (trans. Berg, W. J.). Madison, WI: University of Madison Press.Google Scholar
Bivall, P., Ainsworth, S., & Tibell, L. A. E. (2011). Do haptic representations help complex molecular learning? Science Education, 95(4), 700–719.Google Scholar
Bodemer, D., Ploetzner, R., Bruchmuller, K., & Hacker, S. (2005). Supporting learning with interactive multimedia through active integration of representations. Instructional Science, 33(1), 73–95.Google Scholar
Boshuizen, H., & van der Wiel, W. (1998). Using multiple representations in medicine: How students struggle with them. In Van Someren, M. W., Reimann, P., Boshuizen, H. P. A., & de Jong, T. (eds.), Learning with Multiple Representations (pp. 237–262). Amsterdam: Pergamon.Google Scholar
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in Information Visualization: Using Vision to Think. San Diego, CA: Academic Press.Google Scholar
Cheng, P. C.-H. (1996). Functional roles for the cognitive analysis of diagrams in problem solving. Paper presented at the Proceeding of the Eighteenth Annual Conference of the Cognitive Science Society, 1996, Hillsdale, NJ.Google Scholar
Chua, H. F., Yates, J. F., & Shah, P. (2006). Risk avoidance: Graphs versus numbers. Memory & Cognition, 34(2), 399–410.CrossRefGoogle ScholarPubMed
Cooper, M. M., Corley, L. M., & Underwood, S. M. (2013). An investigation of college chemistry students’ understanding of structure–property relationships. Journal of Research in Science Teaching, 50(6), 699–721.CrossRefGoogle Scholar
Corradi, D., Elen, J., & Clarebout, G. (2012). Understanding and enhancing the use of multiple external representations in chemistry education. Journal of Science Education and Technology, 21(6), 1–16.CrossRefGoogle Scholar
Danielson, R. W., Schwartz, N. H., & Lippmann, M. (2015). Metaphorical graphics aid learning and memory. Learning and Instruction, 39, 194–205.Google Scholar
Danielson, R. W., & Sinatra, G. M. (2016). A relational reasoning approach to text-graphic processing. Educational Psychology Review, 29(1), 55–72.Google Scholar
de Koning, B. B., Rop, G., & Paas, F. (2020). Learning from split-attention materials: Effects of teaching physical and mental learning strategies. Contemporary Educational Psychology, 61, 101873.CrossRefGoogle Scholar
Dienes, Z. (1973). The Six Stages in the Process of Learning Mathematics. Slough: NFER-Nelson.Google Scholar
Eilam, B. (2012). Teaching, Learning, and Visual Literacy: The Dual Role of Visual Representation. New York: Cambridge University Press.Google Scholar
Eitel, A., Scheiter, K., Schüler, A., Nyström, M., & Holmqvist, K. (2013). How a picture facilitates the process of learning from text: Evidence for scaffolding. Learning and Instruction, 28, 48–63.CrossRefGoogle Scholar
Fiorella, L., & Zhang, Q. (2018). Drawing boundary conditions for learning by drawing. Educational Psychology Review, 30(3), 1115–1137.Google Scholar
Furberg, A., Kluge, A., & Ludvigsen, S. (2013). Student sensemaking with science diagrams in a computer-based setting. International Journal of Computer-Supported Collaborative Learning, 8(1), 41–64.Google Scholar
Goldman, S. R. (2003). Learning in complex domains: When and why do multiple representations help? Learning and Instruction, 13(2), 239–244.Google Scholar
Green, T. R. G., Petre, M., & Bellamy, R. K. E. (1991). Comprehensibility of visual and textual programs: A test of superlativism against the “match–mismatch” conjecture. In Koenemann-Belliveau, J., Moher, T. G., & Robertson, S. P. (eds.), Proceedings of Empirical Studies of Programmers: Fourth Workshop (pp. 121–146). Norwood, NJ: Ablex Publishing Company.Google Scholar
Hannus, M., & Hyona, J. (1999). Utilization of illustrations during learning of science textbook passages among low- and high-ability children. Contemporary Educational Psychology, 24(2), 95–123.CrossRefGoogle ScholarPubMed
Harp, S. F., & Mayer, R. E. (1997). The role of interest in learning from scientific text and illustrations: On the distinction between emotional interest and cognitive interest. Journal of Educational Psychology, 89(1), 92–102.Google Scholar
Hegarty, M. (2011). The cognitive science of visual-spatial displays: Implications for design. Topics in Cognitive Science, 3(3), 446–474.Google Scholar
Hegarty, M., & Just, M. A. (1993). Constructing mental models of machines from text and diagrams. Journal of Memory and Language, 32(6), 717–742.CrossRefGoogle Scholar
Hegedus, S. J., & Roschelle, J. (2013). The SimCalc Vision and Contributions. Netherlands: Springer.Google Scholar
Hilton, A., & Nichols, K. (2011). Representational classroom practices that contribute to students conceptual and representational understanding of chemical bonding. International Journal of Science Education, 33(16), 2215–2246.Google Scholar
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31.Google Scholar
Kaput, J. J. (1992). Technology and mathematics education. In Grouws, D. A. (ed.), Handbook of Teaching and Learning Mathematics (pp. 515–556). New York: Macmillan.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. Journal of the Learning Sciences, 9(2), 105–143.Google Scholar
Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In Gilbert, J. K. (ed.), Visualization in Science and Education (pp. 121–146). Dordrecht: Kluwer Academic Publishers.Google Scholar
Kress, G., & Van Leeuwen, T. (1996). The Grammar of Visual Design. London: Routledge.Google Scholar
Larkin, J. H., & Simon, H. A. (1987). Why a diagram Is (sometimes) worth 10000 words. Cognitive Science, 11(1), 65–99.Google Scholar
Lee, H., Plass, J. L., & Homer, B. D. (2006). Optimizing cognitive load for learning from computer-based science simulations. Journal of Educational Psychology, 98(4), 902–913.CrossRefGoogle Scholar
Leinhardt, G., Zaslavsky, O., & Stein, M. M. (1990). Functions, graphs, and graphing: Tasks, learning and teaching. Review of Educational Research, 60(1), 1–64.Google Scholar
Lemke, J. L. (2004). The literacies of science. In Saul, E. W. (ed.), Crossing Borders in Literacy and Science Instruction: Perspectives on Theory and Practice (pp. 33–47). Newark, DE: International Reading Association.Google Scholar
Levin, J. R., Anglin, G. J., & Carney, R. N. (1987). On empirically validating functions of pictures in prose. In Willows, D. M., & Houghton, H. A. (eds.), The Psychology of Illustration: I. Basic Research (pp. 51–85). New York: Springer.Google Scholar
Lohse, G. L., Biolsi, K., Walker, N., & Rueler, H. (1994). A classification of visual representations. Communications of the A.C.M., 37(12), 36–49.Google Scholar
Lowe, R., Schnotz, W., & Rasch, T. (2010). Aligning affordances of graphics with learning task requirements. Applied Cognitive Psychology, 25(3), 452–459.Google Scholar
Madden, S. P., Jones, L. L., & Rahm, J. (2011). The role of multiple representations in the understanding of ideal gas problems. Chemistry Education Research and Practice, 12(3), 283–293.Google Scholar
Mason, L., Tornatora, M. C., & Pluchino, P. (2013). Do fourth graders integrate text and picture in processing and learning from an illustrated science text? Evidence from eye-movement patterns. [Article]. Computers & Education, 60(1), 95–109.Google Scholar
Mayer, R. E. (2014). Cognitive theory of multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning: Second edition (pp. 43–71). New York: Cambridge University Press.Google Scholar
McCrudden, M. T., & Rapp, D. N. (2017). How visual displays affect cognitive processing. Educational Psychology Review, 29(3), 623–639.Google Scholar
McDermott, L. C., Rosenquist, M. L., & van Zee, E. H. (1987). Student difficulties in connecting graphs and physics: Examples from kinematics. American Journal of Physics, 55(6), 503–513.Google Scholar
McElhaney, K. W., Chang, H.-Y., Chiu, J. L., & Linn, M. C. (2014). Evidence for effective uses of dynamic visualisations in science curriculum materials. Studies in Science Education, 51(1), 49–85.Google Scholar
Meyer, J., Shinar, D., & Leiser, D. (1997). Multiple factors that determine performance with tables and graphs. Human Factors, 39(2), 268–286.CrossRefGoogle Scholar
Minogue, J., & Borland, D. (2015). Investigating students’ ideas about buoyancy and the influence of haptic feedback. Journal of Science Education and Technology, 25(2), 187–202.Google Scholar
Nemirovsky, R., Tierney, C., & Wright, T. (1998). Body motion and graphing. Cognition and Instruction, 16(2), 119–172.Google Scholar
Olympiou, G., Zacharias, Z., & de Jong, T. (2013). Making the invisible visible: Enhancing students’ conceptual understanding by introducing representations of abstract objects in a simulation. Instructional Science, 41(3), 575–596.Google Scholar
Paik, E. S., & Schraw, G. (2013). Learning with animation and illusions of understanding. Journal of Educational Psychology, 105(2), 278–289.Google Scholar
Parnafes, O., & Disessa, A. (2004). Relations between types of reasoning and computational representations. International Journal of Computers for Mathematical Learning, 9(3), 251–280.Google Scholar
Peirce, C. S. (1906). Prolegomena to an apology for pragmaticism. The Monist, 16, 492–546.Google Scholar
Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of game-based learning. Educational Psychologist, 50(4), 258–283.Google Scholar
Prain, V., & Tytler, R. (2012). Learning through constructing representations in science: A framework of representational construction affordances. International Journal of Science Education, 34(17), 2751–2773.Google Scholar
Prain, V., & Waldrip, B. (2006). An exploratory study of teachers’ and students’ use of multi‐modal representations of concepts in primary science. International Journal of Science Education, 28(15), 1843–1866.Google Scholar
Rau, M. A. (2018). Sequencing support for sense making and perceptual induction of connections among multiple visual representations. Journal of Educational Psychology, 110(6), 811–833.Google Scholar
Rau, M. A. (2020). Cognitive and socio-cultural theories on competencies and practices involved in learning with multiple external representations. In Van Meter, P., List, A., Lombardi, D, & Kendeou, P. (eds.), Handbook of Learning from Multiple Representations and Perspectives (pp. 17–32). New York: Routledge.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.Google Scholar
Rau, M. A., Aleven, V., Rummel, N., & Pardos, Z. (2013). 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.Google Scholar
Rau, M., 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 Cerri, S., Clancey, W., Papadourakis, G., & Panourgia, K. (eds.), Intelligent Tutoring Systems (Vol. 7135, pp. 174–184). Berlin: Springer.Google Scholar
Rau, M. A., Bowman, H. E., & Moore, J. W. (2017). An adaptive collaboration script for learning with multiple visual representations in chemistry. Computers & Education, 109, 38–55.Google Scholar
Reed, S. K. (2010). Thinking Visually. New York: Psychology Press.Google Scholar
Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In O’Malley, C. (ed.), Computer Supported Collaborative Learning (pp. 69–97). Berlin: Springer.Google Scholar
Rouet, J. F., Britt, M. A., & Durik, A. M. (2017). RESOLV: Readers’ representation of reading contexts and tasks. Educational Psychologist, 52(3), 200–215.Google Scholar
Russell, J., Kozma, R., Becker, D., & Susskind, T. (2000). SMV: Chem; Synchronized Multiple Visualizations in Chemistry. New York: John Wiley.Google Scholar
Rutten, N., van Joolingen, W. R., & van der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education, 58(1), 136–153.Google Scholar
Salomon, G. (1984). Television is easy and print is tough – The differential investment of mental effort in learning as a function of perceptions and attributions. Journal of Educational Psychology, 76(4), 647–658.CrossRefGoogle Scholar
Scaife, M., & Rogers, Y. (1996). External cognition: How do graphical representations work? International Journal of Human–Computer Studies, 45(2), 185–213.Google Scholar
Scheiter, K., Gerjets, P., Huk, T., Imhof, B., & Kammerer, Y. (2009). The effects of realism in learning with dynamic visualizations. Learning and Instruction, 19(6), 481–494.CrossRefGoogle Scholar
Schneider, S., Dyrna, J., Meier, L., Beege, M., & Rey, G. D. (2018). How affective charge and text–picture connectedness moderate the impact of decorative pictures on multimedia learning. Journal of Educational Psychology, 110(2), 233–249.Google Scholar
Schnotz, W. (2002). Commentary – Towards an integrated view of learning from text and visual displays. Educational Psychology Review, 14(1), 101–120.Google Scholar
Schoenfeld, A. H., Smith, J. P., & Arcavi, A. (1993). Learning: The microgenetic analysis of one student’s evolving understanding of a complex subject matter domain. In Glaser, R. (ed.), Advances in Instructional Psychology (pp. 55–176). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Schwonke, R., Berthold, K., & Renkl, A. (2009). How multiple external representations are used and how they can be made more useful. Applied Cognitive Psychology, 23(9), 1227–1243.Google Scholar
Seufert, T. (2019). Training for coherence formation when learning from text and picture and the interplay with learners’ prior knowledge. Frontiers in Psychology, 10, 193.Google Scholar
Shah, P., & Freedman, E. G. (2011). Bar and line graph comprehension: An interaction of top‐down and bottom‐up processes. Topics in Cognitive Science, 3(3), 560–578.Google Scholar
Stieff, M., Hegarty, M., & Deslongchamps, G. (2011). Identifying representational competence with multi-representational displays. Cognition and Instruction, 29(1), 123–145.Google Scholar
Stull, A. T., Hegarty, M., Dixon, B., & Stieff, M. (2012). Representational translation with concrete models in organic chemistry. Cognition and Instruction, 30(4), 404–434.Google Scholar
Suthers, D. D. (2014). Empirical studies of the value of conceptually explicit notations in collaborative learning. In Okada, A., Buckingham Shum, S. J., & Sherborne, T. (eds.), Knowledge Cartography: Software Tools and Mapping Techniques (pp. 1–22). London: Springer.Google Scholar
Treagust, D. F., & Tsui, C.-Y. (2013). Contributions of multiple representations to biology education. In Treagust, D. F., & Tsui, C.-Y. (eds.), Multiple Representations in Biology Education (pp. 349–367). Dordrecht: Springer.CrossRefGoogle Scholar
Tversky, B. (2011). Visualizing thought. Topics in Cognitive Science, 3(3), 499–535.Google Scholar
Tytler, R., Prain, V., Hubber, P., & Waldrip, B. (2013). Constructing Representations to Learn in Science. Rotterdam: Sense Publishers.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.CrossRefGoogle Scholar
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.Google Scholar
van Joolingen, W. R., & de Jong, T. (2003). SIMQUEST: Authoring educational simulations. In Murray, T., Blessing, S., & Ainsworth, S. E. (eds.), Tools for Advanced Technology Learning Environments (pp. 1–32). Amsterdam: Kluwer Academic Publishers.Google Scholar
van Labeke, N., & Ainsworth, S. (2003). A microgenetic approach to understanding translation between representations. Paper presented at the 10th EARLI Conference, 2003, Padova, Italy.Google Scholar
Virk, S., Clark, D., & Sengupta, P. (2015). Digital games as multi-representational environments for science learning: Implications for theory, research, and design. Educational Psychologist, 50(4), 284–312.Google Scholar
Ware, C. (2008). Visual Thinking for Design. Burlington, MA: Morgan Kaufmann Publishers.Google Scholar
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.Google Scholar
Wilder, A., & Brinkerhoff, J. (2007). Supporting representational competence in high school biology with computer-based biomolecular visualizations. Journal of Computers in Mathematics and Science Teaching, 26(1), 5–26.Google Scholar
Won, M., Yoon, H., & Treagust, D. F. (2014). Students’ learning strategies with multiple representations: Explanations of the human breathing mechanism. Science Education, 98(5), 840–866.Google Scholar
Wu, S. P. W., & Rau, M. A. (2018). Effectiveness and efficiency of adding drawing prompts to an interactive educational technology when learning with visual representations. Learning and Instruction, 55, 93–104.Google Scholar
Yore, L., & Hand, B. (2010). Epilogue: Plotting a research agenda for multiple representations, multiple modality, and multimodal representational competency. Research in Science Education, 40(1), 93–101.Google Scholar
Zhang, J. J. (1996). A representational analysis of relational information displays. International Journal of Human–Computer Studies, 45(1), 59–74.Google Scholar
Zhang, Z. H., & Linn, M. C. (2011). Can generating representations enhance learning with dynamic visualizations? Journal of Research in Science Teaching, 48(10), 1177–1198.Google Scholar

References

Amadieu, F., Tricot, A., & Marine, C. (2009). Prior knowledge in learning from a non-linear electronic document: Disorientation and coherence of the reading sequences. Computers in Human Behavior, 25, 381–388.Google Scholar
Amadieu, F., van Gog, T., Paas, F., Tricot, A., & Marine, C. (2009). Effects of prior knowledge and concept-map structure on disorientation, cognitive load, and learning. Learning and Instruction, 19, 376–386.Google Scholar
Arslan-Ari, I. (2017). Learning from instructional animations: How does prior knowledge mediate the effect of visual cues? Journal of Computer Assisted Learning, 34, 140–149.Google Scholar
Blayney, P., Kalyuga, S., & Sweller, J. (2010). Interactions between the isolated–interactive elements effect and levels of learner expertise: Experimental evidence from an accountancy class. Instructional Science, 38, 277–287.Google Scholar
Blayney, P., Kalyuga, S. & Sweller, J. (2016). The impact of complexity on the expertise reversal effect: Experimental evidence from testing accounting students. Educational Psychology, 36, 1868–1885.Google Scholar
Brunstein, A., Betts, S., & Anderson, J. R. (2009). Practice enables successful learning under minimal guidance. Journal of Educational Psychology, 101, 790–802.Google Scholar
Camp, G., Paas, F., Rikers, R., & van Merriënboer, J. J. G. (2001). Dynamic problem selection in air traffic control training: A comparison between performance, mental effort, and mental efficiency. Computers in Human Behavior, 17, 575–595.Google Scholar
Chen, I.-J., & Yen, J.-C. (2013). Hypertext annotation: Effects of presentation formats and learner proficiency on reading comprehension and vocabulary learning in foreign languages. Computers & Education, 63, 416–423.Google Scholar
Chen, O., Kalyuga, S., & Sweller, J. (2017). The expertise reversal effect is a variant of the more general element interactivity effect. Educational Psychology Review, 29, 393–405.Google Scholar
Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorisation and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.Google Scholar
Chi, M. T. H., Glaser, R., & Farr, M. J. (eds.) (1988). The Nature of Expertise. Hillsdale, NJ: Erlbaum.Google Scholar
Chiu, T. K. (2018). Learner expertise and emotional design in multimedia learning. In Kay, J., and Luckin, R. (eds.), Rethinking Learning in the Digital Age: Making the Learning Sciences Count (13th International Conference of the Learning Sciences (ICLS) 2018, Vol. 3). London: International Society of the Learning Sciences.Google Scholar
Clarke, T., Ayres, P., & Sweller, J. (2005). The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications. Educational Technology Research and Development, 53, 15–24.Google Scholar
Cook, M., & Visser, R. (2014). Multimedia presentations of mitosis: An examination of split-attention, modality, redundancy, and cueing. Journal of Educational Multimedia and Hypermedia, 23, 145–162.Google Scholar
Cooper, G., Tindall-Ford, S., Chandler, P., & Sweller, J. (2001). Learning by imagining procedures and concepts. Journal of Experimental Psychology: Applied, 7, 68–82.Google Scholar
Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and Instructional Methods: A Handbook for Research on Interaction. New York: Irvington Publishers.Google Scholar
Ericsson, K. A., & Smith, J. (eds.) (1991). Toward a General Theory of Expertise. Cambridge: Cambridge University Press.Google Scholar
Federico, P.-A. (1980). Adaptive instruction: Trends and issues, In Snow, R., Federico, P.-A., and Montague, W. (eds.), Aptitude, Learning, and Instruction: Vol. 1, Cognitive Process Analyses of Aptitude (pp. 1–26). Hillsdale, NJ: Erlbaum.Google Scholar
Homer, B. D., & Plass, J. L. (2010). Expertise reversal for iconic representations in science visualizations. Instructional Science, 38, 259–276.Google Scholar
Hu, S., Vongpumivitch, V., Chang, J., & Liou, H. (2014). The effects of L1 and L2 e-glosses on incidental vocabulary learning of junior high-school English students. ReCALL: The Journal of the European Association for Computer Assisted Language Learning, 26, 80–99.Google Scholar
Jiang, D., Kalyuga, S., & Sweller, J. (2018). The curious case of improving foreign language listening skills by reading rather than listening: An expertise reversal effect. Educational Psychology Review, 30, 1139–1165.Google Scholar
Johnson, A. M., Ozogul, G., & Reisslein, M. (2015). Supporting multimedia learning with visual signalling and animated pedagogical agent: Moderating effects of prior knowledge. Journal of Computer Assisted Learning, 31, 97–115.Google Scholar
Kalyuga, S. (2006a). Assessment of learners’ organized knowledge structures in adaptive learning environments. Applied Cognitive Psychology, 20, 333–342.Google Scholar
Kalyuga, S. (2006b). Rapid cognitive assessment of learners’ knowledge structures. Learning and Instruction, 16, 1–11.Google Scholar
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.Google Scholar
Kalyuga, S. (2008a). Relative effectiveness of animated and static diagrams: An effect of learner prior knowledge. Computers in Human Behavior, 23, 852–861.CrossRefGoogle Scholar
Kalyuga, S. (2008b). When less is more in cognitive diagnosis: A rapid assessment method for adaptive learning environments. Journal of Educational Psychology, 100, 603–612.Google Scholar
Kalyuga, S. (2009). Managing Cognitive Load in Adaptive Multimedia Learning. New York: Information Science Reference.Google Scholar
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). Expertise reversal effect. Educational Psychologist, 38, 23–31.Google Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40, 1–17.Google Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92, 126–136.Google Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (2001). Learner experience and efficiency of instructional guidance. Educational Psychology, 21, 5–23.Google Scholar
Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579–588.Google Scholar
Kalyuga, S., Law, Y. K., & Lee, C. H. (2013). Expertise reversal effect in reading Chinese texts with added causal words. Instructional Science, 41, 481–497.Google Scholar
Kalyuga, S., & Renkl, A. (2010). Expertise reversal effect and its instructional implications: Introduction to the special issue. Instructional Science, 38, 209–215.Google Scholar
Kalyuga, S., Rikers, R., & Paas, F. (2012). Educational implications of expertise reversal effects in learning and performance of complex cognitive and sensorimotor skills. Educational Psychology Review, 24, 313–337.Google Scholar
Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.Google Scholar
Kalyuga, S., & Sweller, J. (2005). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology, Research and Development, 53, 83–93.Google Scholar
Kalyuga, S., & Sweller, J. (2018). Cognitive load and expertise reversal. In Ericsson, K. A., Hoffman, R. R., Kozbelt, A., & Williams, A. M. (eds.), Cambridge Handbooks in Psychology. The Cambridge Handbook of Expertise and Expert Performance (pp. 793–811). New York: Cambridge University Press.Google Scholar
Khacharem, A., Zoudji, B., & Kalyuga, S. (2015). Expertise reversal for different forms of instructional designs in dynamic visual representations. British Journal of Educational Technology, 46, 756–767.Google Scholar
Khacharem, A., Zoudji, B., Kalyuga, S., & Ripoll, H. (2013). The expertise reversal effect for sequential presentation in dynamic soccer visualizations. Journal of Sport and Exercise Psychology, 35, 260–269.Google Scholar
Khacharem, A., Zoudji, B., Spanjers, I. A. E., & Kalyuga, S. (2014). Improving learning from animated soccer scenes: Evidence for the expertise reversal effect. Computers in Human Behavior, 35, 339–349.Google Scholar
Kouma, R. B. (1991). Learning with media. Review of Educational Research, 61, 179–211.Google Scholar
Lee, H., Plass, J. L., & Homer, B. D. (2006). Optimizing cognitive load for learning from computer-based science simulations. Journal of Educational Psychology, 98, 902–913.Google Scholar
Lee, J., & Spector, J. M. (2012). Effects of model-centered instruction on effectiveness, efficiency, and engagement with ill-structured problem solving. Instructioinal Science, 40, 537–557.Google Scholar
Leppink, J., Broers, N. J., Imbos, T., van der Vleuten, C. P., & Berger, M. P. (2012). Self-explanation in the domain of statistics: An expertise reversal effect. Higher Education, 63, 771–785.Google Scholar
Leslie, K. C., Low, R., Jin, P., & Sweller, J. (2012). Redundancy and expertise reversal effects when using educational technology to learn primary school science. Educational Technology Research and Development, 60, 1–13.Google Scholar
Levie, W., & Lentz, R. (1982). Effects of text illustrations: A review of research. Educational Communication and Technology Journal, 30, 195–232.Google Scholar
Lohman, D. F. (1986). Predicting mathemathanic effects in the teaching of higher-order thinking skills. Educational Psychologist, 21, 191–208.Google Scholar
Magner, U. I. E., Schwonke, R., Aleven, V., Popescu, O., & Renkl, A. (2014). Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments. Learning and Instruction, 29, 141–152.Google Scholar
Mayer, R. E. (1989). Models for understanding. Review of Educational Research, 59, 43–64.Google Scholar
Mayer, R. E. (1999). Research-based principles for the design of instructional messages. The case of multimedia explanations. Document Design, 1, 7–20.Google Scholar
Mayer, R. E. (2001). Multimedia Learning. Cambridge: Cambridge University Press.Google Scholar
Mayer, R. E., & Gallini, J. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82, 715–726.Google Scholar
Mayer, R. E., Steinhoff, K., Bower, G., & Mars, R. (1995). A generative theory of textbook design: Using annotated illustrations to foster meaningful learning of science text. Educational Technology Research and Development, 43, 31–43.Google Scholar
Mayer, R., Stiehl, C., & Greeno, J. (1975). Acquisition of understanding and skill in relation to subjects’ preparation and meaningfulness of instruction. Journal of Educational Psychology, 67, 331–350.Google Scholar
Nihalani, P. K., Mayrath, M., & Robinson, D. H. (2011). When feedback harms and collaboration helps in computer simulation environments: An expertise reversal effect. Journal of Educational Psychology, 103, 776–785.Google Scholar
Ollerenshaw, A., Aidman, E., & Kidd, G. (1997). Is an illustration always worth ten thousand words? Effects of prior knowledge, learning style, and multimedia illustrations on text comprehension. International Journal of Instructional Media, 24, 227–238.Google Scholar
Paas, F., Tuovinen, J. E., van Merriënboer, J. J. G., & Darabi, A. A. (2005). A motivational perspective on the relation between mental effort and performance. Educational Technology Research and Development, 53, 25–34.Google Scholar
Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86.Google Scholar
Reimann, P., & Chi, M. T. H. (1989). Human expertise. In Gilhooly, K. J. (ed.), Human and Machine Problem Solving (pp. 161–191). New York: Plenum Press.Google Scholar
Reisslein, J., Atkinson, R. K., Seeling, P., & Reisslein, M. (2006). Encountering the expertise reversal effect with a computer-based environment on electrical circuit analysis. Learning and Instruction, 16, 92–103.Google Scholar
Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21, 1–29.Google Scholar
Renkl, A., & Atkinson, R. (2003). Structuring the transition from example study to problem solving in cognitive skills acquisition: A cognitive load perspective. Educational Psychologist, 38, 15–22.Google Scholar
Renkl, A., Atkinson, R. K., Maier, U. H., & Staley, R. (2002). From example study to problem solving: Smooth transitions help learning. Journal of Experimental Education, 70, 293–315.Google Scholar
Rey, G. D., & Buchwald, F. (2011). The expertise reversal effect: Cognitive load and motivational explanations. Journal of Experimental Psychology: Applied, 17, 33–48.Google Scholar
Rey, G. D., & Fischer, A. (2013). The expertise reversal effect concerning instructional explanations. Instructional Science, 41, 407–429.Google Scholar
Richter, J., & Scheiter, K. (2019). Studying the expertise reversal of the multimedia signaling effect at a process level: Evidence from eye tracking. Instructional Science, 47, 627–658.Google Scholar
Richter, J., Scheiter, K., & Eitel, A. (2018). Signaling text–picture relations in multimedia learning: The influence of prior knowledge. Journal of Educational Psychology, 110, 544–560.Google Scholar
Roelle, J., & Berthold, K. (2013). The expertise reversal effect in prompting focused processing of instructional explanations. Instructional Science, 41, 635–656.Google Scholar
Salden, R., Aleven, V., Schwonke, R., & Renkl, A. (2010). The expertise reversal effect and worked examples in tutored problem solving. Instructional Science, 38, 289–307.Google Scholar
Salden, R. J. C. M., Paas, F., & van Merriënboer, J. J. G. (2006). A comparison of approaches to learning task selection in the training of complex cognitive skills. Computers in Human Behavior, 22, 321–333.Google Scholar
Schnotz, W., & Rasch, T. (2005). Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning. Educational Technology Research and Development, 53, 47–58.Google Scholar
Snow, R., & Lohman, D. (1984). Toward a theory of cognitive aptitude for learning from instruction. Journal of Educational Psychology, 76, 347–376.Google Scholar
Song, D. (2016). Expertise reversal effect and sequencing of learning tasks in online English as a second language learning environment. Interactive Learning Environments, 24, 423–437.CrossRefGoogle Scholar
Spanjers, I. A. E., Wouters, P., van Gog, T., & van Merriënboer, J. J. G. (2011). An expertise reversal effect of segmentation in learning from animated worked-out examples. Computers in Human Behavior, 27, 46–52.Google Scholar
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: Springer.Google Scholar
Tobias, S. (1976). Achievement treatment interactions. Review of Educational Research, 46, 61–74.CrossRefGoogle Scholar
Tobias, S. (1989). Another look at research on the adaptation of instruction to student characteristics. Educational Psychologist, 24, 213–227.Google Scholar
Tuovinen, J., & Sweller, J. (1999). A comparison of cognitive load associated with discovery learning and worked examples. Journal of Educational Psychology, 91, 334–341.Google Scholar
van Merriënboer, J. J. G. (1990). Strategies for programming instruction in high school: Program completion vs. program generation. Journal of Educational Computing Research, 6, 265–287.Google Scholar
van Merriënboer, J. J. G., & de Croock, M. B. M. (1992). Strategies for computer-based programming instruction: Program completion vs. program generation. Journal of Educational Computing Research, 8, 365–394.Google Scholar
van Merriënboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learner’s mind: Instructional design principles for complex learning. Educational Psychologist, 38, 5–13.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×