With the explosion of multimedia learning environments and the use of digital time-compression algorithms in common software and hardware, we need guidance on how to use this technology without sacrificing quality and learning outcomes. The purpose of the meta-analysis is to examine the existing empirical research literature to derive an overall integrated model for the use of digital time-compression algorithms in multimedia learning environments. We frame this research by coining the accelerated playback hypothesis, which posits that learning outcomes will not diminish with accelerated playback of multimedia learning materials until a threshold is reached. Systematically searching twelve academic databases and filtering the literature, we identified seven qualified manuscripts with k = 18 independent effect size comparisons using time-compression in the context of multimedia learning. The overall effect size for playback speed 1.4–1.5x is small and significant at g = −0.212 [95% CI = −0.378, −0.045]. The overall effect size for playback speed 1.8–2.0x is small and non-significant at g = −0.362 [95% CI = −0.743 to 0.019]. Discussion and implications for practice and research are provided.
*Indicate the manuscript was used in the meta-analysis.
2.
BarronA. E.KysilkaM. (1993). The effectiveness of digital audio in computer-based training. Journal of Research on Computing in Education, 15, 277–289.
3.
BeccueB.VilaL.WhitleyL. K. (2001). The effects of adding audio instructions to a multimedia computer based training environment. Journal of Educational Multimedia and Hypermedia, 10(1), 47–67.
4.
BenzC. R. (1971). Effects of time-compressed instruction upon the comprehension of a visualoriented television lecture [Unpublished doctoral dissertation]. Wayne State University.
5.
BorensteinM.HedgesL. V.HigginsJ. P. T.RothsteinH. R. (2009). Introduction to meta-analysis. Wiley.
6.
ChenC.YenP. (2019). Learner control, segmenting, and modality effects in animateddemonstrations used as the before-class instructions in the flipped classroom. Interactive Learning Environments.
7.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.
8.
CooperH. (2017). Research synthesis and meta-analysis: A step-by-step approach (5th ed.). Sage.
Duval, S., & Tweedie, R. (2000). Trim and fill: A simple funnel–plot -based method of testing and adjusting for publication bias in meta analysis. Biometrics, 56(2), 455–463.
11.
He, W., Zhang, C. H., & Wu, Y. B. (2021, June). Design of Multimedia Intelligent Classroom Interactive Teaching System Based on Internet of Things Technology. In International Conference on E-Learning, E-Education, and Online Training (pp. 442–452). Springer, Cham.
12.
Heo, M., & Toomey, N. (2020). Learning with multimedia: The effects of gender, type of multimedia learning resources, and spatial ability. Computers & Education, 146, 103747.
13.
Higgins, J., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta analysis. Statistics in Medicine, 21(11), 1539–1558.
14.
HuangX.LinJ.Demner-FushmanD. (2006). Evaluation of PICO as a knowledge representation for clinical questions. In AMIA annual symposium proceedings (Vol. 2006, p. 359). American Medical Informatics Association.
15.
Iskandar, A., Rizal, M., Kurniasih, N., Sutiksno, D. U., & Purnomo, A. (2018, November). The Effects of Multimedia Learning on Students Achievement in Terms of Cognitive Test Results. In Journal of Physics: Conference Series (Vol. 1114, No. 1, p. 012019). IOP Publishing.
16.
IssaN.SchullerM.SantacaterinaS.ShapiroM.WangE.MayerR. E.DaRosaD. A. (2011). Applying multimedia design principles enhances learning in medical education. Medical Education, 45(8), 818–826.
17.
JunorL. (1992). Teaching by tape: Some benefits, problems, and solutions. Distance Education, 13(1), 93–107.
18.
Kuba, R., Rahimi, S., Smith, G., Shute, V., & Dai, C. P. (2021). Using the first principles of instruction and multimedia learning principles to design and develop in-game learning support videos. Educational Technology Research and Development, 69(2), 1201–1220.
19.
Lipsey, M. W., & D. B. Wilson. (2001). Practical meta-analysis. Thousand Oaks, CA: Sage.
20.
MayerR. E. (2001). Multimedia learning. Cambridge University Press.
21.
MayerR. E. (2017). Using multimedia for e-learning. Journal of Computer Assisted Learning, 33(5), 403–423.
22.
MayerR. E. (2009). Multimedia learning (2nd ed). Cambridge University Press.
23.
MayerR. E. (2014). The Cambridge handbook of multimedia learning. Cambridge University Press.
24.
MayerR. E. (2019). How multimedia can improve learning and instruction. In DunloskyJ.RawsonK. A. (Eds.), The Cambridge handbook of cognition and education (pp.460–479). Cambridge University Press.
25.
MonroeA. H.EhningerD. (1974). Principles and types of speech compression. Scott Foreman.
26.
NicholsR. G.StevensL. A. (1957). Are you listening? McGraw-Hill.
27.
NoyesJ. M.GarlandK. J. (2008). Computer-vs. paper-based tasks: Are they equivalent?Ergonomics, 51(9), 1352–1375.
28.
OrrD. B.FriedmanH. L. (1967). The effect of listening aids on the comprehension of time-compressed instruction. Journal of Communication, 17(3), 223–227.
29.
*PastoreR. S. (2010). The effects of diagrams and time-compressed instruction on learning and learners’ perceptions of cognitive load. Educational Technology Research and Development, 58(5), 485–505.
30.
*PastoreR. (2012). The effects of time compressed instruction and redundancy on learning and learners’ perceptions of cognitive load. Computers & Education, 58(1), 641–651.
31.
PastoreR.RitzhauptA. (2015). Using time-compression to make multimedia learning more efficient: Current research and practice. TechTrends, 59(2), 66–74.
32.
Pastore, R., Asino, T., & Briskin, J. (2019). The effects of the multimedia, modality, and redundancy principles in a computer-based environment on adult learners. Journal of Multimedia Processing and Technologies, 10(2), 49–61.
33.
*PerezN. (2017). Comprehension of an audio versus an audiovisual lecture at 50% time-compression [Unpublished dissertation]. Florida Atlantic University.
34.
*RitzhauptA. D.BarronA. (2008). Effects of time-compressed narration and representational adjunct images on cued-recall, content recognition, and learner satisfaction. Journal of Educational Computing Research, 39(2), 161–184.
35.
RitzhauptA. D.BarronA. E.KealyW. A. (2011). Conjoint processing of time compressed narration in multimedia instruction: The effects on recall, but not recognition. Journal of Educational Computing Research, 44(2), 203–217.
36.
*RitzhauptA. D.GomesN. D.BarronA. E. (2008). The effects of time-compressed audio and verbal redundancy on learner performance and satisfaction. Computers in Human Behavior, 24(5), 2434–2445.
37.
*RitzhauptA.PastoreR.DavisR. (2015). Effects of captions and time-compressed video on learner performance and satisfaction. Computers in Human Behavior, 59, 222–227.
38.
*SongK.ChakrabortyA.DawsonM.DuganA.AdkinsB.DotyC. (2018). Does the podcast video playback speed affect comprehension for novel curriculum delivery? A randomized trial. Western Journal of Emergency Medicine, 19(1), 101.
39.
Stern, J. A., & Egger, M. (2001). Funnel plots for detecting bias in meta-analysis: guidelines on choice of axis. Journal of Clinical Epidemiology, 54(10), 1046–1055.
40.
Sung, E., & Mayer, R. E. (2012). When graphics improve liking but not learning from online lessons. Computers in Human Behavior, 28(5), 1618–1625.
41.
Taylor, S. E., Frackenpohl, H., & Pettee, J. L. (1960). Grade level norms for the components of the fundamental reading skills. EDL Information Research Bulletin# 3. Huntington, NY: Educational Development Laboratories.
42.
TaylorS. E. (1965). Eye movements in reading: Facts and fallacies. American Educational Research Journal, 2(4), 187–202.
