Studying Visual Displays: How to Instructionally Support Learning

Abstract

Visual displays are very frequently used in learning materials. Although visual displays have great potential to foster learning, they also pose substantial demands on learners so that the actual learning outcomes are often disappointing. In this article, we pursue three main goals. First, we identify the main difficulties that learners have when learning from visual displays. Knowledge about these difficulties is an important basis for selecting appropriate support procedures. Second, we present an overview of empirically tested support procedures and the evidence about their effectiveness. We distinguish between material-oriented interventions and learner-oriented interventions. Material-oriented interventions are, for example, reducing the visual displays’ complexity, cueing/signaling, or physically integrating text and pictures. Learner-oriented interventions refer to the training of learning prerequisites, pre-training, and prompting. Third, we outline fruitful lines of further research with a specific focus on (a) the tentative explanations we provide on the basis of the best available evidence, (b) promising but not yet fully approved support procedures, and (c) important issues that have largely not been researched up to now.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. Ackerman, R., & Leiser, D. (2014). The effect of concrete supplements on metacognitive regulation during learning and open-book test taking. British Journal of Educational Psychology, 84, 329–348. doi:10.1111/bjep.12021.

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Amadieu, F., Mariné, C., & Laimay, C. (2011). Attention-guiding effect and cognitive load in comprehension of animation. Computers in Human Behavior, 27, 36–40. doi:10.1016/j.chb.2010.05.009.

    Article  Google Scholar 

  4. Ayres, P., & Sweller, J. (2014). The split-attention principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 206–226). New York: Cambridge University Press.

    Google Scholar 

  5. Bartholomé, T., & Bromme, R. (2009). Coherence formation when learning from text and pictures: what kind of support for whom? Journal of Educational Psychology, 101, 282–293. doi:10.1037/a0014312.

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Berthold, K., & Renkl, A. (2010). How to foster active processing of explanations in instructional communication. Educational Psychology Review, 22, 25–40. doi:10.1007/s10648-010-9124-9.

    Article  Google Scholar 

  8. Berthold, K., Eysink, T. H., & Renkl, A. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37, 345–363. doi:10.1007/s11251-008-9051-z.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Boucheix, J.-M., & Lowe, R. K. (2010). An eye tracking comparison of external pointing cues and internal continuous cues in learning with complex animations. Learning and Instruction, 20, 123–135. doi:10.1016/j.learninstruc.2009.02.015.

    Article  Google Scholar 

  12. Boucheix, J.-M., & Schneider, E. (2009). Static and animated presentations in learning dynamic mechanical systems. Learning and Instruction, 19, 112–127. doi:10.1016/j.learninstruc.2008.03.004.

    Article  Google Scholar 

  13. Brucker, B., Scheiter, K., & Gerjets, P. (2014). Learning with dynamic and static visualizations: realistic details only benefit learners with high visuospatial abilities. Computers in Human Behavior, 36, 330–339. doi:10.1016/j.chb.2014.03.077.

    Article  Google Scholar 

  14. Butcher, K., & Aleven, V. (2007). Integrating visual and verbal knowledge during classroom learning with computer tutors. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society (pp. 137–142). Austin: Cognitive Science Society.

    Google Scholar 

  15. Canham, M., & Hegarty, M. (2010). The effect of knowledge and display design on comprehension of complex graphics. Learning and Instruction, 20, 155–166. doi:10.1016/j.learninstruc.2009.02.014.

    Article  Google Scholar 

  16. Catrambone, R., & Seay, F. A. (2002). Using animations to help students learn computer algorithms. Human Factors, 44, 495–511. doi:10.1518/0018720024497637.

    Article  Google Scholar 

  17. Cromley, J. G., Bergey, B. W., Fitzhugh, S. L., Newcombe, N., Wills, T. W., Shipley, T. F., & Tanaka, J. C. (2013a). Effectiveness of student-constructed diagrams and self-explanation instruction. Learning and Instruction, 26, 45–58. doi:10.1016/j.learninstruc.2013.01.003.

    Article  Google Scholar 

  18. Cromley, J. G., Perez, A. C., Fitzhugh, S., Newcombe, N., Wills, T. W., & Tanaka, J. C. (2013b). Improving students’ diagrammatic reasoning: a classroom intervention study. The Journal of Experimental Education, 81, 511–537. doi:10.1080/00220973.2012.745465.

    Article  Google Scholar 

  19. De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2007). Attention cueing as a means to enhance learning from an animation. Applied Cognitive Psychology, 21, 731–746. doi:10.1002/acp.1346.

    Article  Google Scholar 

  20. De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2009). Towards a framework for attention cueing in instructional animations: guidelines for research and design. Educational Psychology Review, 21, 113–140. doi:10.1007/s10648-009-9098-7.

    Article  Google Scholar 

  21. De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2010). Attention guidance in learning from a complex animation: seeing is understanding? Learning and Instruction, 20, 111–122. doi:10.1016/j.learninstruc.2009.02.010.

    Article  Google Scholar 

  22. De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2011). Improved effectiveness of cueing by self-explanations when learning from a complex animation. Applied Cognitive Psychology, 25, 183–194. doi:10.1002/acp.1661.

    Article  Google Scholar 

  23. DeLoache, J. S. (1995). Early understanding and use of symbols: the model model. Current Directions in Psychological Science, 4, 109–113. doi:10.1111/1467-8721.ep10772408.

    Article  Google Scholar 

  24. Dreher, A., & Kuntze, S. (2015). Teachers facing the dilemma of multiple representations being aid and obstacle for learning: evaluations of tasks and theme-specific noticing. Journal für Mathematikdidaktik, 36, 23–44. doi:10.1007/s13138-014-0068-3.

  25. Fischer, S., Lowe, R. K., & Schwan, S. (2008). Effects of presentation speed of a dynamic visualization on the understanding of a mechanical system. Applied Cognitive Psychology, 22, 1126–1141. doi:10.1002/acp.1426.

    Article  Google Scholar 

  26. Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 15, 313–331. doi:10.1016/j.learninstruc.2005.07.001.

    Article  Google Scholar 

  27. Ginns, P. (2006). Integrating information: a meta-analysis of the spatial contiguity and temporal contiguity effects. Learning and Instruction, 16, 511–525. doi:10.1016/j.learninstruc.2006.10.001.

    Article  Google Scholar 

  28. Goldstone, R. L., & Son, J. Y. (2005). The transfer of scientific principles using concrete and idealized simulations. The Journal of the Learning Sciences, 14, 69–110. doi:10.1207/s15327809jls1401_4.

    Article  Google Scholar 

  29. Hannus, M., & Hyönä, J. (1999). Utilization of illustrations during learning of science textbook passages among low- and high-ability children. Contemporary Educational Psychology, 24, 95–123. doi:10.1006/ceps.1998.0987.

    Article  Google Scholar 

  30. Hatsidimitris, G., & Kalyuga, S. (2013). Guided self-management of transient information in animations through pacing and sequencing strategies. Educational Technology Research & Development, 61, 91–105. doi:10.1007/s11423-012-9276-z.

    Article  Google Scholar 

  31. Hegarty, M. (2005). Multimedia learning about physical systems. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 447–465). New York: Cambridge University Press.

    Google Scholar 

  32. Hegarty, M., & Just, M. A. (1993). Constructing mental models of machines from text and diagrams. Journal of Memory and Language, 32, 717–742. doi:10.1006/jmla.1993.1036.

    Article  Google Scholar 

  33. Hegarty, M., Canham, M. S., & Fabrikant, S. I. (2010). Thinking about the weather: how display salience and knowledge affect performance in a graphic inference task. Journal of Experimental Psychology Learning, Memory, and Cognition, 36, 37–53. doi:10.1037/a0017683.

    Article  Google Scholar 

  34. Höffler, T. N. (2010). Spatial ability: Its influence on learning with visualizations—a meta-analytic review. Educational Psychological Review, 22, 245–269. doi:10.1007/s10648-010-9126-7.

    Article  Google Scholar 

  35. Horz, H., Winter, C., & Fries, S. (2009). Differential benefits of situated instructional prompts. Computers in Human Behavior, 25, 818–828. doi:10.1016/j.chb.2008.07.001.

    Article  Google Scholar 

  36. Imhof, B., Scheiter, K., Edelmann, J., & Gerjets, P. (2012). How temporal and spatial aspects of presenting visualizations affect learning about locomotion patterns. Learning and Instruction, 22, 193–205. doi:10.1016/j.learninstruc.2011.10.006.

    Article  Google Scholar 

  37. Issa, N., Schuller, M., Santacaterina, S., Shapiro, M., Wang, E., Mayer, R. E., & DaRosa, D. A. (2011). Aplying multimedia design principles enhances learning in medical education. Medical Education, 45, 818–826. doi:10.1111/j.1365-2923.2011.03988.x.

    Article  Google Scholar 

  38. Jaeger, A. J., & Wiley, J. (2014). Do illustrations help or harm metacomprehension accuracy? Learning and Instruction, 34, 58–73. doi:10.1016/j.learninstruc.2014.08.002.

    Article  Google Scholar 

  39. Jamet, E. (2014). An eye-tracking study of cueing effects in multimedia learning. Computers in Human Behavior, 32, 47–53. doi:10.1016/j.chb.2013.11.013.

    Article  Google Scholar 

  40. Jamet, E., Gavota, M., & Quaireau, C. (2008). Attention guiding in multimedia learning. Learning and Instruction, 18, 135–145. doi:10.1016/j.learninstruc.2007.01.011.

    Article  Google Scholar 

  41. Jarodzka, H., Baslev, T., Holmqvist, K., Nyström, M., Scheiter, K., Gerjets, P., & Eika, B. (2012). Conveying clinical reasoning based on visual observation via eye-movement modeling examples. Instructional Science, 40, 813–827. doi:10.1007/s11251-012-9218-5.

    Article  Google Scholar 

  42. Jarodzka, H., van Gog, T., Dorr, M., Scheiter, K., & Gerjets, P. (2013). Learning to see: guiding students’ attention via a model’s eye movements fosters learning. Learning and Instruction, 25, 62–70. doi:10.1016/j.learninstruc.2012.11.004.

    Article  Google Scholar 

  43. Jeung, H.-J., Chandler, P., & Sweller, J. (1997). The role of visual indicators in dual sensory mode instruction. Educational Psychology, 17, 329–343. doi:10.1080/0144341970170307.

    Article  Google Scholar 

  44. Kalyuga, S. (2012). Instructional benefits of spoken words: a review of cognitive load factors. Educational Research Review, 7, 145–159. doi:10.1016/j.edurev.2011.12.002.

    Article  Google Scholar 

  45. Kalyuga, S., & Renkl, A. (2010). Expertise reversal effect and its instructional implications: introduction to the special issue. Instructional Science, 38, 209–215. doi:10.1007/s11251-009-9102-0.

    Article  Google Scholar 

  46. Kalyuga, S., Chandler, P., & Sweller, J. (1999). Managing split-attention and redundancy in multimedia instruction. Applied Cognitive Psychology, 13, 351–371. doi:10.1002/(SICI)1099-0720(199908)13:4<351::AID-ACP589>3.0.CO;2-6.

    Article  Google Scholar 

  47. Kombartzky, U., Ploetzner, R., Schlag, S., & Metz, B. (2010). Developing and evaluating a strategy for learning from animations. Learning and Instruction, 20, 424–433. doi:10.1016/j.learninstruc.2009.05.002.

    Article  Google Scholar 

  48. 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, 949–968. doi:10.1002/(SICI)1098-2736(199711)34:9<949::AID-TEA7>3.0.CO;2-U.

    Article  Google Scholar 

  49. Kriz, S., & Hegarty, M. (2007). Top-down and bottom-up influences on learning from animations. International Journal of Human-Computer Studies, 65, 911–930. doi:10.1016/j.ijhcs.2007.06.005.

    Article  Google Scholar 

  50. Kühl, T., Scheiter, K., Gerjets, P., & Gemballa, S. (2011). Can differences in learning strategies explain the benefits of learning from static and dynamic visualizations? Computers & Education, 56, 176–187. doi:10.1016/j.compedu.2010.08.008.

    Article  Google Scholar 

  51. Kühl, T., Scheiter, K., & Gerjets, P. (2012). Enhancing learning from dynamic and static visualizations by means of cueing. Journal of Educational Multimedia and Hypermedia, 21, 71–88.

    Google Scholar 

  52. Kuntze, S., & Dreher, A. (2014). PCK and the awareness of affective aspects reflected in teachers; views about learning opportunities—a conflict? In B. Pepin & B. Rösken-Winter (Eds.), From beliefs and affect to dynamic systems: (Exploring) a mosaic of relationships and interactions (pp. 295–318). New, York: Springer.

    Google Scholar 

  53. Leahy, W., & Sweller, J. (2011). Cognitive load theory, modality of presentation, and the transient information effect. Applied Cognitive Psychology, 25, 943–951. doi:10.1002/acp.1787.

    Article  Google Scholar 

  54. Lemarié, J., Lorch, R. F., Eyrolle, H., & Virbel, J. (2008). SARA: a text-based and reader-based theory of text signaling. Educational Psychologist, 43, 1–23. doi:10.1080/00461520701756321.

    Article  Google Scholar 

  55. Lewalter, D. (2003). Cognitive strategies for learning from static and dynamic visuals. Learning and Instruction, 13, 177–189. doi:10.1016/S0959-4752(02)00019-1.

    Article  Google Scholar 

  56. Lin, L., & Atkinson, R. K. (2013). Enhancing learning from different visualizations by self-explanation prompts. Journal of Educational Computing Research, 49, 83–110. doi:10.2190/EC.49.1.d.

    Article  Google Scholar 

  57. Lin, L., Atkinson, R. K., Savenye, W. C., & Nelson, B. C. (2015). Effects of visual cues and self-explanation prompts: empirical evidence in a multimedia environment. Interactive Learning Environments. doi:10.1080/10494820.2014.924531.

    Google Scholar 

  58. Low, R., & Sweller, J. (2014). The modality principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 227–246). New York: Cambridge University Press.

    Google Scholar 

  59. Lowe, R. (2004). Interrogation of a dynamic visualization during learning. Learning and Instruction, 14, 257–274. doi:10.1016/j.learninstruc.2004.06.003.

    Article  Google Scholar 

  60. Lowe, R. (2008). Learning from animation: where to look, when to look. In R. Lowe & W. Schnotz (Eds.), Learning with animation: Research implications for design (pp. 49–68). New York: Cambridge University Press.

    Google Scholar 

  61. Lowe, R., Schnotz, W., & Rasch, T. (2011). Aligning affordances of graphics with learning task requirements. Applied Cognitive Psychology, 25, 452–459. doi:10.1002/acp.1712.

    Article  Google Scholar 

  62. 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. doi:10.1016/j.learninstruc.2012.07.002.

    Article  Google Scholar 

  63. 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. Computers & Education, 60, 95–109. doi:10.1016/j.compedu.2012.07.011.

    Article  Google Scholar 

  64. Mautone, P. D., & Mayer, R. E. (2007). Cognitive aids for guiding graph comprehension. Journal of Educational Psychology, 99, 640–652. doi:10.1037/0022-0663.99.3.640.

    Article  Google Scholar 

  65. Mayer, R. E. (Ed.). (2014). The Cambridge handbook of multimedia learning (2nd ed.). New York: Cambridge University Press.

    Google Scholar 

  66. Mayer, R. E., & Johnson, C. I. (2008). Revising the redundancy principle in multimedia learning. Journal of Educational Psychology, 100, 380–386. doi:10.1037/0022-0663.100.2.380.

    Article  Google Scholar 

  67. Mayer, R. E., Mathias, A., & Wetzel, K. (2002a). Fostering understanding of multimedia messages through pre-training: evidence for a two-stage theory of mental model construction. Journal of Experimental Psychology: Applied, 8, 147–154. doi:10.1037/1076-898X.8.3.147.

    Google Scholar 

  68. Mayer, R. E., Mautone, P., & Prothero, W. (2002b). Pictorial aids for learning by doing in a multimedia geology simulation game. Journal of Educational Psychology, 94, 171–185. doi:10.1037/0022-0663.94.1.171.

    Article  Google Scholar 

  69. Mayer, R. E., Dow, G. T., & Mayer, S. (2003). Multimedia learning in an interactive self-explaining environment: what works in the design of agent-based microworlds? Journal of Educational Psychology, 95, 806–812. doi:10.1037/0022-0663.95.4.806.

    Article  Google Scholar 

  70. Meyer, K., Rasch, T., & Schnotz, W. (2010). Effects of animation’s speed of presentation on perceptual processing and learning. Learning and Instruction., 20, 136–145. doi:10.1016/j.learninstruc.2009.02.016.

    Article  Google Scholar 

  71. Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: the role of modality and contiguity. Journal of Educational Psychology, 91, 358–368. doi:10.1037/0022-0663.91.2.358.

    Article  Google Scholar 

  72. 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, 32–47. doi:10.1037/a0021995.

    Article  Google Scholar 

  73. Nitz, S., Ainsworth, S. E., Nerdel, C., & Prechtl, H. (2014). Do student perceptions of teaching predict the development of representational competence and biological knowledge? Learning and Instruction, 31, 13–22. doi:10.1016/j.learninstruc.2013.12.003.

    Article  Google Scholar 

  74. Ozcelik, E., Karakus, T., Kursun, E., & Cagiltay, K. (2009). An eye-tracking study of how color coding affects multimedia learning. Computers & Education, 53, 445–453. doi:10.1016/j.compedu.2009.03.002.

    Article  Google Scholar 

  75. Ozcelik, E., Arslan-Ari, I., & Cagiltay, K. E. (2010). Why does signaling enhance multimedia learning? Evidence from eye movements. Computers in Human Behavior, 26, 110–117. doi:10.1016/j.chb.2009.09.001.

    Article  Google Scholar 

  76. Peeck, J. (1993). Increasing picture effects in learning from illustrated text. Learning and Instruction, 3, 227–238. doi:10.1016/0959-4752(93)90006-L.

    Article  Google Scholar 

  77. Piburn, M. D., Reynolds, S. J., McAuliffe, C., Leedy, D. E., Birk, J. P., & Johnson, J. K. (2005). The role of visualization in learning from computer-based images. International Journal of Science Education, 27, 513–527. doi:10.1080/09500690412331314478.

    Article  Google Scholar 

  78. Pluchino, P., Tornatora, M. C., & Mason, L. (2013). Improving text and picture integration during reading through eye-movement modeling. Paper presented at the15th Biennal EARLI Conference. Munich, Germany.

  79. Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86. doi:10.1016/S0959-4752(01)00016-0.

    Article  Google Scholar 

  80. Reid, D. J., & Beveridge, M. (1986). Effects of text illustration in children’s learning of a school science topic. British Journal of Educational Psychology, 56, 294–303. doi:10.1111/j.2044-8279.1986.tb03042.x.

    Article  Google Scholar 

  81. Renkl, A. (2014). Towards an instructionally-oriented theory of example-based learning. Cognitive Science, 38, 1–37. doi:10.1111/cogs.12086.

    Article  Google Scholar 

  82. Rieber, L. P. (1994). Computers, graphics, and learning. Madison: Brown & Benchmark.

    Google Scholar 

  83. Rummer, R., Schweppe, J., Fürstenberg, A., Scheiter, K., & Zindler, A. (2011). The perceptual basis of the modality effect in multimedia learning. Journal of Experimental Psychology: Applied, 17, 159–173. doi:10.1037/a0023588.

    Google Scholar 

  84. 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, 647–658. doi:10.1037/0022-0663.76.4.647.

    Article  Google Scholar 

  85. Scheiter, K., & Eitel, A. (2015). Signals foster multimedia learning by supporting integration of highlighted text and diagram elements. Learning and Instruction, 36, 11–26. doi:10.1016/j.learninstruc.2014.11.002.

    Article  Google Scholar 

  86. Scheiter, K., Gerjets, P., Huk, T., Imhof, B., & Kammerer, Y. (2009). The effects of realism in learning with dynamic visualizations. Learning and Instruction, 19, 481–494. doi:10.1016/j.learninstruc.2008.08.001.

    Article  Google Scholar 

  87. Scheiter, K., Gerjets, P., & Schuh, J. (2010). The acquisition of problem-solving skills in mathematics: how animations can aid understanding of structural problem features and solution procedures. Instructional Science, 38, 487–502. doi:10.1007/s11251-009-9114-9.

    Article  Google Scholar 

  88. Scheiter, K., Schubert, C., Gerjets, P., & Stalbovs, K. (2015). Does a strategy training foster students’ ability to learn from multimedia? The Journal of Experimental Education, 83, 266–289. doi:10.1080/00220973.2013.876603.

    Article  Google Scholar 

  89. Schmidt-Weigand, F., Kohnert, A., & Glowalla, U. (2010). Explaining the modality and contiguity effects: new insights from investigating students’ viewing behavior. Applied Cognitive Psychology, 24, 226–237. doi:10.1002/acp.1554.

    Article  Google Scholar 

  90. Schubert, C., Scheiter, K., & Schüler, A. (2015). Learning from multimedia: eye movement modeling to support processing of text and pictures (manuscript submitted for publication).

  91. Schüler, A., Scheiter, K., & Schmidt-Weigand, F. (2011). Boundary conditions and constraints of the modality effect. German Journal of Educational Psychology, 25, 211–220. doi:10.1024/1010-0652/a000046.

    Google Scholar 

  92. Schüler, A., Scheiter, K., Rummer, R., & Gerjets, P. (2012). Explaining the modality effect in multimedia learning: is it due to a lack of temporal contiguity with written text and pictures? Learning and Instruction, 22, 92–102. doi:10.1016/j.learninstruc.2011.08.001.

    Article  Google Scholar 

  93. Schüler, A., Scheiter, K., & Gerjets, P. (2013). Is spoken text always better? Investigating the modality and redundancy effect with longer text presentation. Computers in Human Behavior, 29, 1590–1601. doi:10.1016/j.chb.2013.01.047.

    Article  Google Scholar 

  94. Schwartz, D. L. (1995). Reasoning about the referent of a picture versus reasoning about the picture as a referent: an effect of visual realism. Memory & Cognition, 23, 709–722. doi:10.3758/BF03200924.

    Article  Google Scholar 

  95. 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, 1227–1243. doi:10.1002/acp.1526.

    Article  Google Scholar 

  96. Schworm, S., & Renkl, A. (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology, 99, 285–296. doi:10.1037/0022-0663.99.2.285.

    Article  Google Scholar 

  97. Serra, M. J., & Dunlosky, J. (2010). Metacomprehension judgements reflect the belief that diagrams improve learning from text. Memory, 18, 698–711. doi:10.1080/09658211.2010.506441.

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  100. Singh, A. M., Marcus, N., & Ayres, P. (2012). The transient information effect: investigating the impact of segmentation on spoken and written text. Applied Cognitive Psychology, 26, 848–853. doi:10.1002/acp.2885.

    Article  Google Scholar 

  101. Skuballa, I. T., Schwonke, R., & Renkl, A. (2012). Learning from narrated animations with different support procedures: working memory capacity matters. Applied Cognitive Psychology, 26, 840–847. doi:10.1002/acp.2884.

    Article  Google Scholar 

  102. Skuballa, I. T., Fortunski, C., & Renkl, A. (2015). An eye movement pre-training fosters the comprehension of processes and functions in technical systems. Frontiers in Psychology, 6, 598. doi:10.3389/fpsyg.2015.00598.

    Article  Google Scholar 

  103. Son, L. K., & Metcalfe, J. (2000). Metacognitive and control strategies in study-time allocation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 204–221. doi:10.1037/0278-7393.26.1.204.

    Google Scholar 

  104. Spanjers, I. A. E., van Gog, T., & van Merriënboer, J. J. G. (2010). A theoretical analysis of how segmentation of dynamic visualizations optimizes students’ learning. Educational Psychology Review, 22, 411–423. doi:10.1007/s10648-010-9135-6.

    Article  Google Scholar 

  105. Stalbovs, K., Scheiter, K., & Gerjets, P. (2015). Implementation intentions during multimedia learning: using if-then plans to facilitate cognitive processing. Learning and Instruction, 35, 1–15. doi:10.1016/j.learninstruc.2014.09.002.

    Article  Google Scholar 

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

    Article  Google Scholar 

  107. Sweller, J. (2006). The worked example effect and human cognition. Learning and Instruction, 16, 165–169. doi:10.1016/j.learninstruc.2006.02.005.

    Article  Google Scholar 

  108. Tabbers, H. K., & De Koeijer, B. (2010). Learner control in animated multimedia instructions. Instructional Science, 38, 441–453. doi:10.1007/s11251-009-9119-4.

    Article  Google Scholar 

  109. Thillmann, H., Künsting, J., Wirth, J., & Leutner, D. (2009). Is it merely a question of “what” to prompt or also “when” to prompt? German Journal of Eductional Psychology, 23, 105–115. doi:10.1024/1010-0652.23.2.105.

    Google Scholar 

  110. Tibus, M., Heier, A., & Schwan, S. (2013). Do films make you learn? Inference processes in expository film comprehension. Journal of Educational Psychology, 105, 329–340. doi:10.1037/a0030818.

    Article  Google Scholar 

  111. 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, 352–402. doi:10.1037/a0028446.

    Article  Google Scholar 

  112. 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, 411–423. doi:10.1111/j.1365-2729.2011.00411.x.

    Article  Google Scholar 

  113. Van Gog, T. (2014). The signaling (or cueing) principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 263–278). New York: Cambridge University Press.

    Google Scholar 

  114. Van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: literature review and synthesis. Educational Psychology Review, 17, 285–325. doi:10.1007/s10648-005-8136-3.

  115. Wong, A., Leahy, W., Marcus, N., & Sweller, J. (2012). Cognitive load theory, the transient information effect and e-learning. Learning and Instruction, 22, 449–457. doi:10.1016/j.learninstruc.2012.05.004.

    Article  Google Scholar 

  116. Zacks, J. M., Tversky, B., & Iyer, G. (2001). Perceiving, remembering, and communicating structure in events. Journal of Experimental Psychology: General, 130, 29–58. doi:10.1037/0096-3445.130.1.29.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Alexander Renkl.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Renkl, A., Scheiter, K. Studying Visual Displays: How to Instructionally Support Learning. Educ Psychol Rev 29, 599–621 (2017). https://doi.org/10.1007/s10648-015-9340-4

Download citation

Keywords

  • Learning from pictures
  • Multimedia learning
  • Multiple representations
  • Instructional support