Journal of Computing in Higher Education

, Volume 21, Issue 1, pp 31–61 | Cite as

Design factors for educationally effective animations and simulations

  • Jan L. Plass
  • Bruce D. Homer
  • Elizabeth O. Hayward
Article

Abstract

This paper reviews research on learning from dynamic visual representations and offers principles for the design of animations and simulations that assure their educational effectiveness. In addition to established principles, new and revised design principle are presented that have been derived from recent research. Our review focuses on the visual design and interaction design of these visualizations and presents existing research as well as questions for future inquiry.

Keywords

Simulation Animation Visualization Design Science Learning Cognition 

References

  1. Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198.Google Scholar
  2. Ainsworth, S., & VanLabeke, N. (2004). Multiple forms of dynamic representation. Learning and Instruction, 14, 241–255.Google Scholar
  3. Arnheim, R. (1969). Art and visual perception: A psychology of the creative eye. Berkeley: University of California Press.Google Scholar
  4. Ayres, P., & Sweller, J. (2005). The split-attention principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning. New York: Cambridge.Google Scholar
  5. Azevedo, R., & Bernard, R. M. (1995). A meta-analysis of the effects of feedback in computer-based instruction. Journal of Educational Computing Research, 13(2), 111–127.Google Scholar
  6. Barry, A. M. (1997). Visual intelligence: Perception, image, and manipulation in visual communication. Albany, NY: SUNY Press.Google Scholar
  7. Bertin, J. (1983). Semiology of graphics: Diagrams, networks, maps. Madison, WI: University of Wisconsin Press.Google Scholar
  8. Betrancourt, M. (2005). The animation and interactivity principles in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning. New York: Cambridge.Google Scholar
  9. Bodemer, D., Ploetzner, R., Bruchmüller, K., & Häcker, S. (2005). Supporting learning with interactive multimedia through active integration of representations. Instructional Science, 33, 73–95.Google Scholar
  10. Bodemer, D., Ploetzner, R., Feuerlein, I., & Spada, H. (2004). The active integration of information during learning with dynamic and interactive visualizations. Learning and Instruction, 14, 325–341.Google Scholar
  11. Brünken, R., Plass, J. L., & Leutner, D. (2004). Assessment of cognitive load in multimedia learning with dual-task methodology: Auditory load and modality effects. Instructional Science, 32(1), 115–132.Google Scholar
  12. Brünken, R., Steinbacher, S., Plass, J. L., & Leutner, D. (2002). Assessment of cognitive load in multimedia learning using dual-task methodology. Experimental psychology, 49(2), 109–119.Google Scholar
  13. Carlson, R., Chandler, P., & Sweller, J. (2003). Learning and understanding science instructional material. Journal of educational psychology, 95(3), 629–640.Google Scholar
  14. Carney, R. N., & Levin, J. R. (2002). Pictorial illustrations still improve students’ learning from text. Educational Psychology Review, 14(1), 5–26.Google Scholar
  15. Chandler, P. (2004). The crucial role of cognitive processes in the design of dynamic visualizations. Learning and Instruction, 14, 353–357.Google Scholar
  16. Clark, R. E. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459.Google Scholar
  17. Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21–29.Google Scholar
  18. Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3(3), 149–210.Google Scholar
  19. Cook, M. P. (2006). Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles. Science Education, 90(6), 1073–1091.Google Scholar
  20. Darabi, A. A., Nelson, D. W., & Palanki, S. (2007). Acquisition of troubleshooting skills in a computer simulation: Worked example vs. conventional problem solving instructional strategies. Computers in Human Behavior, 23(4), 1809–1819.Google Scholar
  21. de Jong, T. (2005). The guided discovery principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 215–228). New York: Cambridge.Google Scholar
  22. de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201.Google Scholar
  23. de Koning, B., Tabbers, H., Rikers, R., & Paas, F. (2007). Attention cueing as a means to enhance learning from an animation. Applied Cognitive Psychology, 21, 731–746.Google Scholar
  24. Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222.Google Scholar
  25. Dwyer, F. M. (1972). A guide for improving visualized instruction. State College, PA: Learning Services.Google Scholar
  26. Dwyer, F. M. (1978). Strategies for improving visual learning. State College, PA: Learning Services.Google Scholar
  27. Dwyer, F. M., & Moore, D. M. (1991). Effect of color coding on visually oriented tests with students of different cognitive styles. Journal of Psychology: Interdisciplinary and Applied, 125(6), 677–680.Google Scholar
  28. Fletcher, J. D., & Tobias, S. (2005). The multimedia principle. New York, NY: Cambridge University Press.Google Scholar
  29. Garg, A. X., Norman, G., & Sperotable, L. (2001). How medical students learn spatial anatomy. The Lancet, 357, 363–364.Google Scholar
  30. Gibson, J. J. (1961). Ecological optics. Vision Research, 1(3–4), 253–262.Google Scholar
  31. Goldman, S. R. (2003). Learning in complex domains: When and why do multiple representations help? Learning and Instruction, 13, 239–244.Google Scholar
  32. Graesser, A. C., Millis, K. K., & Zwaan, R. A. (1997). Discourse comprehension. Annual Review of Psychology, 48, 163–189.Google Scholar
  33. Grèzes, J., Costes, N., & Decety, J. (1998). Top-down effect of strategy on the perception of human biological motion: A PET investigation. Cognitive Neuropsychology. Special Issue: Perception and action: Recent Advances in Cognitive Neuropsychology, 15(6), 553–582.Google Scholar
  34. Guthrie, J. T., Weber, S., & Kimmerly, N. (1993). Searching documents: Cognitive processes and deficits in understanding graphs, tables, and illustrations. Contemporary Educational Psychology, 18(2), 186–221.Google Scholar
  35. Hall, R. H., & Sidio-Hall, M. A. (1994a). The effect of color enhancement on knowledge map processing. Journal of Experimental Education, 62(3), 209–217.Google Scholar
  36. Hall, R. H., & Sidio-Hall, M. A. (1994b). The effect of student color coding of knowledge maps and test anxiety on student learning. Journal of Experimental Education, 62(4), 291–302.Google Scholar
  37. Harman, K. L., Humphrey, G. K., & Goodale, M. A. (1999). Active manual control of object views facilitates visual recognition. Current Biology, 9, 1315–1318.Google Scholar
  38. 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
  39. Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: A theory of cognitive interest in science learning. Journal of Educational Psychology, 90(3), 414–434.Google Scholar
  40. Hasler, B. S., Kersten, B., & Sweller, J. (2007). Learner control, cognitive load and instructional animation. Applied Cognitive Psychology, 21, 713–729.Google Scholar
  41. Hegarty, M. (2004). Dynamic visualizations and learning: Getting to the difficult questions. Learning and Instruction, 14(3), 343–351.Google Scholar
  42. Hegarty, M., & Just, M. A. (1993). Constructing mental models of machines from text and diagrams. Journal of Memory and Language, 32(6), 717–742.Google Scholar
  43. Helmuth, L. (2003). Cognitive neuroscience. Fear and trembling in the amygdala. Science, 300(5619), 568–569.Google Scholar
  44. Höffler, T., & Leutner, D. (2007). Instructional animation versus static pictures: A meta-analysis. Learning and Instruction, 17, 722–738.Google Scholar
  45. Isen, A. M., Daubman, K. A., & Nowicki, G. P. (1987). Positive affect facilitates creative problem solving. Journal of Personality and Social Psychology, 56, 1122–1131.Google Scholar
  46. Isen, A. M., & Patrick, R. (1983). The effect of positive feelings on risk-taking: When the chips are down. Organizational Behavior and Human Performance, 31, 194–202.Google Scholar
  47. James, K. H., Humphrey, G. K., Vilis, T., Corrie, B., Baddour, R., & Goodale, M. A. (2002). “Active” and “passive” learning of three-dimensional object structure within an immersive virtual reality environment. Behavior Research Methods, Instruments, & Computers, 34, 383–390.Google Scholar
  48. Jeung, H., Chandler, P., & Sweller, J. (1997). The role of visual indicators in dual sensory mode instruction. Educational Psychology, 17, 329–343.Google Scholar
  49. Kalyuga, S. (2007). Enhancing instructional efficiency of interactive e-learning environments: A cognitive load perspective. Educational Psychology Review, 19(3), 387–399.Google Scholar
  50. Kalyuga, S., Chandler, P., & Sweller, J. (1999). Managing split-attention and redundancy in multimedia instruction. Applied Cognitive Psychology, 13(4), 351–371.Google Scholar
  51. Keller, T., Gerjets, P., Scheiter, K., & Garsoffky, B. (2006). Information visualizations for knowledge acquisition: The impact of dimensionality and color coding. Computers in Human Behavior, 22(1), 43–65.Google Scholar
  52. Kennedy, G. E. (2004). Promoting cognition in multimedia interactivity research. Journal of Interactive Learning Research, 15(1), 43–61.Google Scholar
  53. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.Google Scholar
  54. Kosslyn, S. M. (1989). Understanding charts and graphs. Applied Cognitive Psychology, 3(3), 185–225.Google Scholar
  55. Kosslyn, S. M. (1994). Image and brain: The resolution of the imagery debate. Cambridge, MA: The MIT Press.Google Scholar
  56. Kosslyn, S. M., & Koenig, O. (1992). Wet mind: The new cognitive neuroscience (p. 548). New York: Free Press.Google Scholar
  57. 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.Google Scholar
  58. Kulhavy, R. W., Stock, W. A., & Kealy, W. A. (1993). How geographic maps increase recall of instructional text. Educational Technology Research and Development, 41(4), 47–62.Google Scholar
  59. Kulhavy, R. W., Stock, W. A., & Peterson, S. E. (1992). Using maps to retrieve text: A test of conjoint retention. Contemporary Educational Psychology, 17, 56–70.Google Scholar
  60. Lavie, N. (2005). Distracted and confused? Selective attention under load. Trends in Cognitive Sciences, 9(2), 75–82.Google Scholar
  61. LeDoux, J. (2003). The emotional brain, fear, and the amygdala. Cellular and molecular neurobiology, 23, 727–738.Google Scholar
  62. 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
  63. Levie, W. H., Houghton, H. A., & Willows, D. M. (1987). Research on pictures: A guide to the literature. In D. M. Willows & H. A. Houghton (Eds.), The psychology of illustration: Vol. I. Basic research (pp. 1–50). New York: Springer-Verlag.Google Scholar
  64. Levin, J. R. (1989). A transfer-appropriate-processing perspective of pictures in prose. In H. Mandl & J. R. Levin (Eds.), Knowledge acquisition from text and pictures (p. 58). Amsterdam: Elsevier Science Publishers.Google Scholar
  65. Levin, J. R., Anglin, G. J., & Carney, R. R. (1987). On empirically validating functions of pictures in prose. In D. M. Willows & H. A. Houghton (Eds.), The psychology of illustration: Vol. I. Basic research (pp. 51–85). New York: Springer.Google Scholar
  66. Logie, R. H., & Della Sala, S. (2005). Disorders of visuospatial working memory. In P. Shah & A. Miyake (Eds.), Cambridge handbook of visuospatial thinking (pp. 81–120). New York: Cambridge.Google Scholar
  67. Lorch, R. F., Jr. (1989). Text signaling devices and their effects on reading and memory processes. Educational Psychology Review, 1, 209–234.Google Scholar
  68. Low, R., & Sweller, J. (2005). The modality principle in multimedia learning. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 147–158). New York: Cambridge.Google Scholar
  69. Lowe, R. K. (2003). Animation and learning: selective processing of information in dynamic graphics. Learning and Instruction, 13, 157–176.Google Scholar
  70. Lowe, R. (2004). Interrogation of a dynamic visualization during learning. Learning and Instruction, 14, 257–274.Google Scholar
  71. Mandl, H., & Levin, J. R. (1989). Knowledge acquisition from text and pictures. New York: North-Holland.Google Scholar
  72. Mautone, P. D., & Mayer, R. E. (2001). Signaling as a cognitive guide in multimedia learning. Journal of Educational Psychology, 93(2), 377–389.Google Scholar
  73. Mayer, R. E. (1989). Models for understanding. Review of Educational Research, 59(1), 43–64.Google Scholar
  74. Mayer, R. E. (2001). Multimedia learning. New York: Cambridge.Google Scholar
  75. Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59(1), 14–19.Google Scholar
  76. Mayer, R. E. (Ed.). (2005a). Cambridge handbook of multimedia learning. New York: Cambridge.Google Scholar
  77. Mayer, R. E. (2005b). Principles for managing essential processing in multimedia learning: Segmenting, pretraining, and modality principles. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 169–182). New York: Cambridge.Google Scholar
  78. Mayer, R. E. (2005c). Principles for reducing extraneous processing in multimedia learning: Coherence, signaling, redundancy, spatial contiguity and temporal contiguity principles. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 183–200). New York: Cambridge.Google Scholar
  79. 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(1), 64–73.Google Scholar
  80. Mayer, R. E., & Chandler, P. (2001). When learning is just a click away: Does simple user interaction foster deeper understanding of multimedia messages? Journal of Educational Psychology, 93(2), 390–397.Google Scholar
  81. Mayer, R. E., Deleeuw, K. E., & Ayres, P. (2007). Creating retroactive and proactive interference in multimedia learning. Applied Cognitive Psychology. Special Issue: A Cognitive Load Approach to the Learning Effectiveness of Instructional Animation, 21(6), 795–809.Google Scholar
  82. 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(4), 806–812.Google Scholar
  83. Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82(4), 715–726.Google Scholar
  84. Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93(1), 187–198.Google Scholar
  85. Mayer, R. E., & Moreno, R. (1998). A split-attention effect in multimedia learning: Evidence for dual processing systems in working memory. Journal of Educational Psychology, 90(2), 312–320.Google Scholar
  86. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.Google Scholar
  87. Mayer, R., & Moreno, R. E. (in press). Techniques that reduce extraneous cognitive load and manage intrinsic cognitive load during multimedia learning. In J. L. Plass, R. Moreno, & R. Brünken (Eds.), Cognitive load: Theory and application. New York: Cambridge University Press.Google Scholar
  88. 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(1), 31–43.Google Scholar
  89. Mesulam, M. M. (1998). From sensation to cognition. Brain, 121, 1013–1052.Google Scholar
  90. Moreno, R. (2004). Decreasing cognitive load for novice students: Effects of explanatory versus corrective feedback in discovery-based multimedia. Instructional Science, 32(1–2), 99–113.Google Scholar
  91. Moreno, R. (2006). Learning in high-tech and multimedia environments. Current Directions in Psychological Science, 15(2), 63–67.Google Scholar
  92. Moreno, R. (2007). Optimising learning from animations by minimising cognitive load: Cognitive and affective consequences of signaling and segmentation methods. Applied Cognitive Psychology, 21, 765–781.Google Scholar
  93. Moreno, R., & Durán, R. (2004). Do multiple representations need explanations? The role of verbal guidance and individual differences in multimedia mathematics learning. Journal of Educational Psychology, 96(3), 492–503.Google Scholar
  94. Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91(2), 358–368.Google Scholar
  95. Moreno, R., & Mayer, R. E. (2000). A coherence effect in multimedia learning: The case for minimizing irrelevant sounds in the design of multimedia instructional messages. Journal of Educational Psychology, 92(1), 117–125.Google Scholar
  96. Moreno, R., & Mayer, R. E. (2005). Role of guidance, reflection, and interactivity in an agent-based multimedia game. Journal of Educational Psychology, 97(1), 117–128.Google Scholar
  97. Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments: Special issue on interactive learning environments: Contemporary issues and trends. Educational Psychology Review. Special Issue: Interactive Learning Environments: Contemporary Issues and Trends, 19(3), 309–326.Google Scholar
  98. Morris, C. D., Bransford, J. D., & Franks, J. J. (1977). Levels of processing versus transfer appropriate processing. Journal of Verbal Learning and Verbal Behavior, 16, 519–533.Google Scholar
  99. Morrison, G. R., Ross, S. M., Gopalakrishnan, M., & Casey, J. (1995). The effects of feedback and incentives in achievement in computer-based instruction. Contemporary Educational Psychology, 20, 32–50.Google Scholar
  100. Mousavi, S. Y., Low, R., & Sweller, J. (1995). Reducing cognitive load by mixing auditory and visual presentation modes. Journal of Educational Psychology, 87(2), 319–334.Google Scholar
  101. Niemiec, R., Sikorski, C., & Walberg, H. J. (1996). Learner-control effects: A review of reviews and a meta-analysis. Journal of Educational Computing Research, 15(2), 157–174.Google Scholar
  102. Norman, D. (2003). Emotional design: Why we love (or hate) everyday things. New York: Basic Books.Google Scholar
  103. Oliveri, M., Turriziani, P., Carlesimo, G. A., Koch, G., Tomaiuolo, F., Panella, M., et al. (2001). Parieto-frontal interactions in visual-object and visual-spatial working memory: Evidence from transcranial magnetic stimulation. Cerebral Cortex, 11(7), 606–618.Google Scholar
  104. Paivio, A. (1971). Imagery and verbal processes (p. 596). Oxford, England: Holt, Rinehart & Winston.Google Scholar
  105. Paivio, A. (1991). Images in mind: The evolution of a theory. New York: Harvester Wheatsheaf.Google Scholar
  106. Paivio, A., & Csapo, K. (1973). Picture superiority in free recall: Imagery or dual coding? Cognitive psychology, 5(2), 176–206.Google Scholar
  107. Peirce, C. S. (1955). Logic as semiotic: The theory of signs. In J. Buchler (Ed.), The philosophical writings of Peirce (pp. 98–110). New York: Dover Books.Google Scholar
  108. Penney, C. G. (1989). Modality effects and the structure of short-term verbal memory. Memory & Cognition, 17(4), 398–422.Google Scholar
  109. 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(1), 25–36.Google Scholar
  110. Plass, J. L., Chun, D. M., Mayer, R. E., & Leutner, D. (2003). Cognitive load in reading a foreign language text with multimedia aids and the influence of verbal and spatial abilities. Computers in Human Behavior, 19(2), 221–243.Google Scholar
  111. Plass, J. L., Hamilton, H., & Wallen, E. (2004, April). The effects of three types of multimedia aids on three cognitive learning outcomes in the comprehension of scientific texts. Paper presented at the Annual Meeting of the American Educational Research Association (AERA) in San Diego, CA.Google Scholar
  112. Plass, J. L., Homer, B. D., Milne, C., Jordan, T., Kalyuga, S., Kim, M., et al. (2009). Design factors for effective science simulations: Representation of information. International Journal of Gaming and Computer-Mediated Simulations, 1(1), 16–35.Google Scholar
  113. Plass, J. L., Homer, B. D., Milne, C., Jordan, T., Kim, M., & Barrientos, J. (2007). Representational mode and cognitive load: Optimizing the instructional design of science simulations. Featured Research Paper presented at the annual convention of the Association for Educational Communication and Technology (AECT) in October, 2007 in Anaheim, CA.Google Scholar
  114. Reijnen, E., Wallach, D., Stöcklin, M., Kassuba, T., & Opwis, K. (2007). Color similarity in visual search. Swiss Journal of Psychology, 66(4), 191–199.Google Scholar
  115. Renkl, A. (2005). The worked-out examples principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 229–245). New York: Cambridge.Google Scholar
  116. Renkl, A., Atkinson, R., Maier, U., & Staley, R. (2002). From example study to problem solving: Smooth transitions help learning. Journal of Experimental Education, 70, 293–315.Google Scholar
  117. Rieber, L. P. (1989). A review of animation research in CBI. In Proceedings of selected research papers presented at the Annual Meeting of the Association for Educational Communication and Technology (pp. 370–389). Dallas, TX.Google Scholar
  118. Rieber, L. P. (1990). Using computer animated graphics with science instruction with children. Journal of Educational Psychology, 82(1), 135–140.Google Scholar
  119. Rieber, L. P. (1991). Effects of visual grouping strategies of computer-animated presentations on selective attention in science. Educational Technology Research and Development, 39, 5–15.Google Scholar
  120. Rieber, L. P. (1996). Animation as feedback in a computer-based simulation: Representation matters. Educational Technology Research and Development, 44, 5–22.Google Scholar
  121. Rieber, L. P. (2005). Multimedia learning in games, simulations, and microworlds. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning. New York: Cambridge.Google Scholar
  122. Rieber, L. P., & Parmley, M. W. (1995). To teach or not to teach? Comparing the use of computer-based simulations in deductive versus inductive approaches to learning with adults in science. Journal of Educational Computing Research, 13(4), 359–374.Google Scholar
  123. Rieber, L. P., Smith, M., Al-Ghafry, S., Strickland, B., Chu, G., & Spahi, F. (1996). The role of meaning in interpreting graphic textual feedback during a computer-based simulation. Computers Education, 27(1), 45–58.Google Scholar
  124. Rieber, L. P., Tzeng, S., & Tribble, K. (2004). Discovery learning, representation, and explanation within a computer-based simulation: Finding the right mix. Learning and Instruction, 14, 307–323.Google Scholar
  125. Schnotz, W. (2002). Commentary: Towards an integrated view of learning from text and visual displays. Educational Psychology Review, 14(1), 101–120.Google Scholar
  126. Schnotz, W. (2005). An integrated model of text and picture comprehension. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 49–70). New York: Cambridge.Google Scholar
  127. Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13, 141–156.Google Scholar
  128. Schnotz, W., Böckheler, J., & Grzondziel, H. (1999). Individual and co-operative learning with interactive animated pictures. European Journal of Psychology of Education, 14, 245–265.CrossRefGoogle Scholar
  129. Schnotz, W., & Kulhavy, R. W. (1994). Comprehension of graphics. Amsterdam, Netherlands: North-Holland/Elsevier Science Publishers.Google Scholar
  130. 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. Special Issue: Research on Cognitive Load Theory and Its Design Implications for E-Learning, 53(3), 47–58.Google Scholar
  131. Schwan, S., & Riempp, R. (2004). The cognitive benefits of interactive videos: Learning to tie nautical knots. Learning and Instruction, 14, 293–305.Google Scholar
  132. Serences, J. T., & Yantis, S. (2006). Selective visual attention and perceptual coherence. Trends in Cognitive Sciences, 10(1), 38–45.Google Scholar
  133. Seufert, T., & Brünken, R. (2006). Cognitive load and the format of instructional aids for coherence formation. Applied Cognitive Psychology, 20(3), 321–331.Google Scholar
  134. Shah, P., & Carpenter, P. A. (1995). Conceptual limitations in comprehending line graphs. Journal of Experimental Psychology: General, 124(1), 43–61.Google Scholar
  135. Shah, P., & Hoeffner, J. (2002). Review of graph comprehension research: Implications for instruction. Educational Psychology Review, 14(1), 47–69.Google Scholar
  136. Stieff, M., & Wilensky, U. (2003). Connected chemistry: Incorporating interactive simulations into the chemistry curriculum. Journal of Science Education and Technology, 12, 285–302.Google Scholar
  137. Swaak, J., & de Jong, T. (2001). Learner vs. system control in using online support for simulation-based discovery learning. Learning Environments Research, 4, 217–241.Google Scholar
  138. Swaak, J., de Jong, T., & van Joolingen, W. R. (2004). The effects of discovery learning and expository instruction on the acquisition of definitional and intuitive knowledge. Journal of Computer Assisted Learning, 20(4), 225–234.Google Scholar
  139. Sweller, J. (1999). Instructional design. Camberwell, Vic: ACER Press.Google Scholar
  140. Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 19–30). New York: Cambridge.Google Scholar
  141. Sweller, J., Chandler, P., Tierney, P., & Cooper, M. (1990). Cognitive load as a factor in the structuring of technical material. Journal of Experimental Psychology: General, 119(2), 176–192.Google Scholar
  142. Tabbers, H., Martens, R., & van Merriënboer, J. J. G. (2004). Multimedia instructions and cognitive load theory: Effects of modality and cueing. British Journal of Educational Psychology, 74, 71–81.Google Scholar
  143. Tarmizi, R. A., & Sweller, J. (1988). Guidance during mathematical problem solving. Journal of Educational Psychology, 80(4), 424–436.Google Scholar
  144. Tversky, B., Morrison, J. B., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human-Computer Studies, 57(4), 247–262.Google Scholar
  145. Um, E., Song, H., & Plass, J. L. (2007). The effect of positive emotions on multimedia learning. Paper presented at the World Conference on Educational Multimedia, Hypermedia & Telecommunications (ED-MEDIA 2007) in Vancouver, Canada, June 25–29, 2007.Google Scholar
  146. Ungerleider, L. G., & Mishkin, M. (1982). Two cortical visual systems. In D. G. Ingle, M. A. Goodale, & R. J. Q. Mansfield (Eds.), Analysis of visual behavior (pp. 549–586). Cambridge, MA: MIT Press.Google Scholar
  147. van der Meij, J., & de Jong, T. (2004). Learning with multiple representations. Paper presented at the 2004 Annual Meeting of the American Educational Research Association, San Diego, CA.Google Scholar
  148. 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, 199–212.Google Scholar
  149. Weiss, R. E., Knowlton, D. S., & Morrison, G. R. (2002). Principles for using animation in computer-based instruction: Theoretical heuristics for effective design. Computers in Human Behavior, 18, 465–477.Google Scholar
  150. White, B. Y., & Fredriksen, J. R. (1990). Causal model progression as a foundation for intelligent learning environments. Artificial Intelligence, 42, 99–157.Google Scholar
  151. Willows, D. M., & Houghton, H. A. (Eds.). (1987). The psychology of illustration—Vol. I. Basic research. New York: Springer-Verlag.Google Scholar
  152. Winn, W. (1994). Contributions of perceptual and cognitive processes to the comprehension of graphics. Amsterdam, Netherlands: North-Holland/Elsevier Science Publishers.Google Scholar
  153. Winn, W., Li, T.-Z., & Schill, D. (1991). Diagrams as aids to problem solving: Their role in facilitating search and computation. Educational Technology Research and Development, 39(1), 17–29.Google Scholar
  154. Wouters, P., Tabbers, H. K., & Paas, F. (2007). Interactivity in video-based models. Educational Psychology Review, 19(3), 327–342.Google Scholar
  155. Wu, H., Krajcik, J. S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38, 821–842.Google Scholar
  156. Zhang, J., Chen, Q., Sun, Y., & Reid, D. J. (2004). Triple scheme of learning support design for scientific discovery learning based on computer simulation: Experimental research. Journal of Computer Assisted Learning, 20(4), 269–282.Google Scholar

Copyright information

© US Government  2009

Authors and Affiliations

  • Jan L. Plass
    • 1
  • Bruce D. Homer
    • 2
  • Elizabeth O. Hayward
    • 1
  1. 1.CREATE–Consortium for Research and Evaluation of Advanced Technologies in EducationNew York UniversityNew YorkUSA
  2. 2.Graduate CenterThe City University of New YorkNew YorkUSA

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