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26 - The Animation Composition Principle in Multimedia Learning

from Part VI - Principles Based on Social and Affective Features of Multimedia Learning

Published online by Cambridge University Press:  19 November 2021

By
Richard K. Lowe ,
Wolfgang Schnotz  and
Jean-Michel Boucheix
Edited by
Richard E. Mayer  and
Logan Fiorella
Richard E. Mayer
Affiliation:
University of California, Santa Barbara
Logan Fiorella
Affiliation:
University of Georgia
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Summary

Research has shown that animated graphics are not the educational magic bullet that many expected them to be. They are neither necessarily superior to static graphics nor intrinsically effective in their own right. The Animation Composition Principle characterizes learning from animation as a hierarchical relation-building process by which mental models of the depicted subject matter are progressively and cumulatively constructed from discrete information primitives. It helps explain the limited success of previous attempts to improve animation’s effectiveness that took no account of their fundamental design. By giving due consideration to both perceptual and cognitive aspects of animation processing, the Animation Processing Model that embodies this Principle opens the door to novel, more effective compositional design options. These compositional animations significantly improve learning outcomes.

Keywords

animationanimation composition principleanimation processing modelcompositional animations
Type
Chapter
Information
The Cambridge Handbook of Multimedia Learning , pp. 313 - 323
Publisher: Cambridge University Press
Print publication year: 2021

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References

Alpizar, D., Adesope, O. O., & Wong, R.M. (2020). A meta-analysis of signaling principle in multimedia learning environments. Education Technology Research and Development, 68, 2095–2119.CrossRefGoogle Scholar
Berney, S., & Bétrancourt, M. (2016). Does animation enhance learning? A meta-analysis. Computers & Education, 101, 150-167.Google Scholar
Biard, N., Cojean, S., & Jamet, E. (2018). Effects of segmentation and pacing on procedural learning by video. Computers in Human Behavior, 89, 411–417.Google Scholar
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.Google Scholar
Boucheix, J.-M., Lowe, R. K., Putri, D. K., & Groff, J. (2013). Cueing animations: Dynamic signaling aids information extraction and comprehension. Learning and Instruction, 25, 71–84.CrossRefGoogle Scholar
Castro-Alonzo, J. C., Wong, M., Adesope, O. O., & Ayres, P. (2019). Gender imbalance in instructional dynamic versus static visualizations: A meta-analysis. Educational Psychology Review, 31, 361–387.Google Scholar
de Koning, B. B., & Tabbers, H. K. (2013). Gestures in instructional animations: A helping hand to understanding non-human movements? Applied Cognitive Psychology, 1(27), 683–689.Google Scholar
de Koning, B. B., Tabbers, H. K., Rijkers, R. M. P. J., & Paas, F. (2010a). Attention cueing in an instructional animation: The role of presentation speed. Computers in Human Behavior, 27, 41–45.CrossRefGoogle Scholar
de Koning, B. B., Tabbers, H. K., Rijkers, R. M. J. P., & Paas, F. (2010b). Attention guidance in learning from a complex animation: Seeing is understanding? Learning and Instruction, 20, 111–122.Google Scholar
de Koning, B. B., Tabbers, H. K., Rijkers, 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.Google Scholar
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.Google Scholar
Höffler, T. N., & Leutner, D. (2007). Instructional animation versus static pictures: A meta-analysis. Learning and Instruction, 17, 722–738.Google Scholar
Lowe, R. K., & Boucheix, J.-M. (2008). Learning from animated diagrams: How are mental models built? In Stapleton, G., Howse, J., & Lee, J. (eds.), Diagrammatic Representation and Inference (pp. 266–281). Berlin: Springer.Google Scholar
Lowe, R. K., & Boucheix, J.-M. (2010). Manipulatable models for investigating processing of dynamic diagrams. In Goel, A. K., Jamnik, M., & Narayanan, N. H. (eds.), Diagrammatic Representation and Inference (pp. 319–321). Berlin: Springer.Google Scholar
Lowe, R. K., & Boucheix, J.-M. (2011). Cueing complex animation: Does direction of attention foster learning processes? Learning and Instruction, 21, 650–663.Google Scholar
Lowe, R. K., & Boucheix, J.-M. (2012a). Dynamic diagrams: A composition alternative. In Cox, P., Plimmer, B., & Rogers, P. (eds.), Diagrammatic Representation and Inference (pp. 233–240). Berlin: Springer.Google Scholar
Lowe, R. K., & Boucheix, J-M. (2012b). Addressing challenges of biological animations. In de Vries, E., & Scheiter, K. (eds.), Proceedings of the Meeting of the EARLI Special Interest Group on Comprehension of Text and Graphics (pp. 217–129). Grenoble: University of Grenoble.Google Scholar
Lowe, R. K., & Boucheix, J. M. (2016). Principled animation design improves comprehension of complex dynamics. Learning and Instruction, 45, 72–84.Google Scholar
Lowe, R.K., & Boucheix, J-M. (2020). Improving animations: Compositional anti-cueing makes conventional designs more effective. Paper presented at EARLI SIG2, Comprehension of Text and Graphics. Charles University, Prague, Czech Republic. Online Conference, August 31–September 2.Google Scholar
Lowe, R. K., Boucheix, J. M., & Menant, M. (2018). Perceptual processing and the comprehension of relational information in dynamic diagrams. In Chapman, P., Stapleton, G., Moktefi, A., Perez-Kriz, S., & Bellucci, F. (eds.), Diagrammatic Representation and Inference, LNAI, Lecture Notes in Artificial Intelligence, 10871 (pp. 470–483). Cham: Springer.Google Scholar
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.Google Scholar
Mierowsky, R., Marcus, N., & Ayres, P. (2019). Using mimicking gestures to improve observational learning from instructional videos. Educational Psychology, 40, 1–20.Google Scholar
Neisser, U. (1976). Cognition and Reality. San Francisco, CA: Freeman.Google Scholar
Ploetzner, R., Berney, S., & Bétrancourt, M. (2020). A review of learning demands in instructional animations: The educational effectiveness of animations unfolds if the features of change need to be learned. Journal of Computer Assisted Learning, 36(6), 838–860.CrossRefGoogle Scholar
Ploetzner, R., & Fillisch, B. (2017). Not the silver bullet: Learner-generated drawings make it difficult to understand broader spatiotemporal structures in complex animations. Learning and Instruction, 47, 13–24.Google Scholar
Post, L. S., van Gog, T., Paas, F., & Zwaan, R. A. (2013). Effects of simultaneously observing and making gestures while studying grammar animations on cognitive load and learning. Computers in Human Behavior, 29, 1450–1455.CrossRefGoogle Scholar
Rey, G. D., Beege, M., Nebel, S., Wirzberger, M., Schmitt, T. H., & Schneider, S. (2019). A meta-analysis of the segmenting effect. Educational Psychology Review, 31, 389–419.Google Scholar
Richter, J., Scheiter, K., & Eitel, A. (2016). Signaling text-picture relations in multimedia learning: A comprehensive meta-analysis. Educational Research Review, 17, 19–36.Google Scholar
Scheiter, K. (2014). The learner control principle in multimedia learning. In Mayer, R. E. (ed.), The Cambridge Handbook of Multimedia Learning (2nd ed., pp. 487–512). New York: Cambridge University Press.Google Scholar
Schneider, S., Beege, M., Nebel, S., & Rey, G.D. (2018). A meta-analysis of how signaling affects learning with media. Educational Research Review, 23, 1–24.Google Scholar
Schwan, S., & Riempp, R. (2004). The cognitive benefits of interactive videos: Learning to tie nautical knots. Learning and Instruction, 14, 293–305.Google Scholar
Sepp, S., Agostinho, S., Tindall-Ford, S., & Paas, F (in press). To trace or not to trace? Meaningful gesture for learning geometry using touch-based multimedia learning materials.Google Scholar
Spanjers, I. A. E., van Gog, T., & van Merrienboer, J. J. G. (2010). A theoretical analysis of how segmentation of dynamic visualizations optimizes students’ learning. Educational Psychology Review, 22, 411–423.Google 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(1), 46–52.Google Scholar
Stull, A. T., Gainer, M. J., & Hegarty, M. (2018). Learning by enacting: The role of embodiment in chemistry education. Learning and Instruction, 55, 80–92.Google Scholar
Ullman, S. (1984). Visual routines. Cognition, 18, 97–159.Google Scholar
van Gog, T., Paas, F., Marcus, N., Ayres, P., & Sweller, J. (2009). The mirror neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review, 21, 21–30.Google Scholar
van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: Literature review and synthesis. Educational Psychology Review, 17, 285–325.Google Scholar
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.Google Scholar
Zacks, J. M., & Tversky, B. (2001). Event structure in perception and conception. Psychological Bulletin, 127, 3–21.CrossRefGoogle ScholarPubMed

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