Cognitive constraints on multimedia learning: When presenting more material results in less understanding.
Abstract
In 4 experiments, college students viewed an animation and listened to concurrent narration explaining the formation of lightning. When students also received concurrent on-screen text that summarized (Experiment 1) or duplicated (Experiment 2) the narration, they performed worse on tests of retention and transfer than did students who received no on-screen text. This redundancy effect is consistent with a dual-channel theory of multimedia learning in which adding on-screen text can overload the visual information-processing channel, causing learners to split their visual attention between 2 sources. Lower transfer performance also occurred when the authors added interesting but irrelevant details to the narration (Experiment 1) or inserted interesting but conceptually irrelevant video clips within (Experi-ment 3) or before the presentation (Experiment 4). This coherence effect is consistent with a seductive details hypothesis in which the inserted video and narration prime the activation of inappropriate prior knowledge as the organizing schema for the lesson. Multimedia scientific explanations—in CD-ROMs, on the World Wide Web, and in classroom demonstrations—are po-tentially valuable instructional tools. For example, Figure 1 shows selected slides from a 140-s multimedia explanation intended to help students understand how lightning storms develop. The multimedia explanation uses animation to depict the steps in lightning formation along with corresponding narration to describe them. Our research has documented that well designed multimedia explanations formatted like the one in Figure 1 can be highly effective in promoting students' understanding, as indicated by their ability to generate accept-able answers to open-ended transfer questions (Mayer, 1997,
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The same explanation may induce extraneous cognitive load for the high-ability student because the information is already known to that student and is therefore redundant.
“Simultaneously, this explanation may induce extraneous cognitive load for the high-ability student, because this student is already aware of this information (i.e., it is redundant; see Mayer et al. 2001).”
From Making the Black Box of Collaborative Learning Transparent: Combining Process-Oriented and Cognitive Load Approaches (2010) · cites this paper as Mayer et al. 2001
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Tare, M., Chiong, C., Ganea, P. A., & DeLoache, J. S. (2010) · Journal of Applied Developmental Psychology · doi:10.1016/j.appdev.2010.06.005 · Cited by 141 · see citing works
Mayer, R. E. (2013) · Springer eBooks · doi:10.1007/978-1-4614-3185-5_31 · Cited by 154 · see citing works
Self-citationMultimedia Learning with Animated Pedagogical Agents
Moreno, R. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816819.032 · Cited by 140 · see citing works
Diao, Y., & Sweller, J. (2007) · Learning and Instruction · doi:10.1016/j.learninstruc.2006.11.007 · Cited by 139 · see citing works
Memorable Audiovisual Narratives Synchronize Sensory and Supramodal Neural Responses
Cohen, S. S., & Parra, L. C. (2016) · eneuro · doi:10.1523/eneuro.0203-16.2016 · Cited by 138 · see citing works
Split-attention and redundancy effects on mobile learning in physical environments
Liu, T., Lin, Y.-C., Tsai, M., & PAAS, F. G. W. C. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.08.007 · Cited by 138 · see citing works
Cognitive Theory of Multimedia Learning
Mayer, R. E. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108894333.008 · Cited by 169 · see citing works
Self-citationEffects of segmenting, signalling, and weeding on learning from educational video
Ibrahim, M. M., Antonenko, P., Greenwood, C., & Wheeler, D. (2011) · Learning Media and Technology · doi:10.1080/17439884.2011.585993 · Cited by 135 · see citing works
Student Learning: What Has Instruction Got to Do With It?
Lee, H. S., & Anderson, J. R. (2012) · Annual Review of Psychology · doi:10.1146/annurev-psych-113011-143833 · Cited by 134 · see citing works
Subtitles and Eye Tracking: Reading and Performance
Kruger, J., & Steyn, F. (2013) · Reading Research Quarterly · doi:10.1002/rrq.59 · Cited by 133 · see citing works
Wang, F., Li, W., Mayer, R. E., & Liu, H. (2017) · Journal of Educational Psychology · doi:10.1037/edu0000221 · Cited by 136 · see citing works
Self-citationAustin, K. A. (2009) · Comput. Educ. · doi:10.1016/j.compedu.2009.06.017 · Cited by 145 · see citing works
- Authors
- Mayer, R. E., Heiser, J., & Lonn, S.
- Affiliations
- University of California, Santa Barbara (US)
- ORCID
- Mayer, Richard E.
- Venue
- Journal of Educational Psychology
- Published
- 2001-03
- Volume
- 93
- Issue
- 1
- Pages
- 187-198
- ISSN
- 0022-0663 (ISSN-L)
- 1939-2176
- OpenAlex
- W2000885037
- Semantic Scholar
- c17e261a20efe4601b5b7e0e3e47c833271068c0
- Cited by
- 1,029
- References
- 33
- Citations by year
- 2026: 36 · 2025: 25 · 2024: 27 · 2023: 39 · 2022: 45 · 2021: 47 · 2020: 45 · 2019: 52 · 2018: 47 · 2017: 47 · 2016: 55 · 2015: 58 · 2014: 62 · 2013: 66 · 2012: 54
- FWCI
- 20.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Psychology (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Online and Blended Learning
- Keywords
- multimedia learning, redundancy effect, coherence effects, seductive details, visual attention, screen text, narration, animation, retention, transfer, prior knowledge, lightning formation, instructional design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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