A Meta-analysis of the Segmenting Effect

  • Günter Daniel ReyEmail author
  • Maik Beege
  • Steve Nebel
  • Maria Wirzberger
  • Tobias H. Schmitt
  • Sascha Schneider


The segmenting effect states that people learn better when multimedia instructions are presented in (meaningful and coherent) learner-paced segments, rather than as continuous units. This meta-analysis contains 56 investigations including 88 pairwise comparisons and reveals a significant segmenting effect with small to medium effects for retention and transfer performance. Segmentation also reduces the overall cognitive load and increases learning time. These four effects are confirmed for a system-paced segmentation. The meta-analysis tests different explanations for the segmenting effect that concern facilitating chunking and structuring due to segmenting the multimedia instruction by the instructional designer, providing more time for processing the instruction and allowing the learners to adapt the presentation pace to their individual needs. Moderation analyses indicate that learners with high prior knowledge benefitted more from segmenting instructional material than learners with no or low prior knowledge in terms of retention performance.


Multimedia learning Cognitive theory of multimedia learning Segmenting effect Interactivity Learner control 


Supplementary material

10648_2018_9456_MOESM1_ESM.docx (22 kb)
ESM 1 (DOCX 21 kb)
10648_2018_9456_MOESM2_ESM.docx (22 kb)
ESM 2 (DOCX 21 kb)
10648_2018_9456_MOESM3_ESM.docx (21 kb)
ESM 3 (DOCX 21 kb)
10648_2018_9456_MOESM4_ESM.docx (21 kb)
ESM 4 (DOCX 21 kb)
10648_2018_9456_MOESM5_ESM.docx (20 kb)
ESM 5 (DOCX 19.5 kb)


  1. Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., et al. (2001). A taxonomy for learning, teaching, and assessing: a revision of Bloom’s taxonomy of educational objectives. New York: Longman.Google Scholar
  2. Antonenko, P., Paas, F., Grabner, R., & Van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438.CrossRefGoogle Scholar
  3. Baddeley, A. D. (1992). Working memory. Science, 255(5044), 556–559.CrossRefGoogle Scholar
  4. Baddeley, A. D. (1999). Human memory. Boston: Allyn & Bacon.Google Scholar
  5. Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637.CrossRefGoogle Scholar
  6. Begg, C. B., & Mazumdar, M. (1994). Operating characteristics of a rank correlation test for publication bias. Biometrics, 50(4), 1088–1101.CrossRefGoogle Scholar
  7. Bloom, B. S., & Krathwohl, D. R. (1956). Taxonomy of educational objectives. The classification of educational goals, handbook I: cognitive domain. New York: Longmans Green.Google Scholar
  8. Bloom, B. S., Madaus, G. F., & Hastings, J. T. (1981). Evaluation to improve learning. New York: McGraw-Hill.Google Scholar
  9. Chandler, P., & Sweller, J. (1992). The split-attention effect as a factor in the design of instruction. British Journal of Educational Psychology, 62(2), 233–246.CrossRefGoogle Scholar
  10. Chen, F., Zhou, J., Wang, Y., Yu, K., Arshad, S. Z., Khawaji, A., & Conway, D. (2016). Robust multimodal cognitive load measurement. Cham: Springer International Publishing.CrossRefGoogle Scholar
  11. Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3(3), 149–210.CrossRefGoogle Scholar
  12. Cook, A. E., & Wei, W. (2017). Using eye movements to study reading processes: methodological considerations. In C. A. Was, F. J. Sansoit, & B. J. Morris (Eds.), Eye tracking technology applications in educational research (pp. 27–47). Hershey: IGI Global.CrossRefGoogle Scholar
  13. Cooper, H., Hedges, L. V., & Valentine, J. C. (Eds.). (2009). The handbook of research synthesis and meta-analysis (2nd ed.). New York: Russell Sage Foundation.Google Scholar
  14. DeStefano, D., & LeFevre, J.-A. (2007). Cognitive load in hypertext reading: a review. Computers in Human Behavior, 23(3), 1616–1641.CrossRefGoogle Scholar
  15. Evans, C., & Gibbons, N. J. (2007). The interactivity effect in multimedia learning. Computers & Education, 49(4), 1147–1160.Google Scholar
  16. Field, A. P., & Gillett, R. (2010). How to do a meta-analysis. British Journal of Mathematical and Statistical Psychology, 63(3), 665–694.CrossRefGoogle Scholar
  17. Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 15(4), 313–331.CrossRefGoogle Scholar
  18. Ginns, P. (2006). Integrating information: a meta-analysis of the spatial contiguity and temporal contiguity effects. Learning and Instruction, 16(6), 511–525.CrossRefGoogle Scholar
  19. Grubbs, F. E. (1969). Procedures for detecting outlying observations in samples. Technometrics, 11, 1–21.Google Scholar
  20. Hattie, J. (2009). Visible learning: a synthesis of over 800 meta-analyses relating to achievement. London: Routledge.Google Scholar
  21. Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando: Academic.Google Scholar
  22. Hedges, L. V., & Vevea, J. L. (1998). Fixed- and random-effects models in meta-analysis. Psychological Methods, 3(4), 486–504.CrossRefGoogle Scholar
  23. Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39–65.CrossRefGoogle Scholar
  24. Hoaglin, D. C., Mosteller, F., & Tukey, J. W. (Eds.). (1983). Understanding robust and exploratory data analysis (Vol. 3). New York: Wiley.Google Scholar
  25. IBM Corp. (2017). IBM SPSS statistics for Windows (version 25.0). Armonk: IBM Corp.Google Scholar
  26. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509–539.CrossRefGoogle Scholar
  27. Kalyuga, S. (2009). Knowledge elaboration: a cognitive load perspective. Learning and Instruction, 19(5), 402–410.CrossRefGoogle Scholar
  28. Kalyuga, S., & Renkl, A. (2010). Expertise reversal effect and its instructional implications: introduction to the special issue. Instructional Science, 38(3), 209–215.CrossRefGoogle Scholar
  29. Kalyuga, S., Chandler, P., & Sweller, J. (1999). Managing split-attention and redundancy in multimedia instruction. Applied Cognitive Psychology, 13(4), 351–371.CrossRefGoogle Scholar
  30. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31.CrossRefGoogle Scholar
  31. Keehner, M., Hegarty, M., Cohen, C., Khooshabeh, P., & Montello, D. R. (2008). Spatial reasoning with external visualizations: what matters is what you see, not whether you interact. Cognitive Science, 32(7), 1099–1132.CrossRefGoogle Scholar
  32. Khooshabeh, P., & Hegarty, M. (2010). Inferring cross-sections: when internal visualizations are more important than properties of external visualizations. Human Computer Interaction, 25(2), 119–147.CrossRefGoogle Scholar
  33. Kurby, C. A., & Zacks, J. M. (2008). Segmentation in the perception and memory of events. Trends in Cognitive Sciences, 12(2), 72–79.CrossRefGoogle Scholar
  34. Lawless, K. A., & Brown, S. W. (1997). Multimedia learning environments: issues of learner control and navigation. Instructional Science, 25(2), 117–131.CrossRefGoogle Scholar
  35. Lipsey, M. W., & Wilson, D. B. (2001). Applied social research methods series; vol. 49. Practical meta-analysis. Thousand Oaks: Sage.Google Scholar
  36. Mayer, R. E. (1997). Multimedia learning: are we asking the right questions? Educational Psychologist, 32(1), 1–19.CrossRefGoogle Scholar
  37. Mayer, R. E. (2014a). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 43–71). Cambridge: Cambridge University Press.Google Scholar
  38. Mayer, R. E. (2014b). Introduction to multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 1–24). Cambridge: Cambridge University Press.Google Scholar
  39. Mayer, R. E., & Pilegard, C. (2014). Principles for managing essential processing in multimedia learning: segmenting, pre-training, and modality principles. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 316–344). Cambridge: Cambridge University Press.Google Scholar
  40. Mayer, R. E., Moreno, R., Boire, M., & Vagge, S. (1999). Maximizing constructivist learning from multimedia communications by minimizing cognitive load. Journal of Educational Psychology, 91(4), 638–643.CrossRefGoogle Scholar
  41. Mura, K., Petersen, N., Huff, M., & Ghose, T. (2013). IBES: a tool for creating instructions based on event segmentation. Frontiers in Psychology, 4(994).
  42. Oksa, A., Kalyuga, S., & Chandler, P. (2010). Expertise reversal effect in using explanatory notes for readers of Shakespearean text. Instructional Science, 38(3), 217–236.CrossRefGoogle Scholar
  43. Paas, F. (1992). Training strategies for attaining transfer of problem solving skill in statistics: a cognitive-load approach. Journal of Educational Psychology, 84(4), 429–434.CrossRefGoogle Scholar
  44. Paivio, A. (1986). Mental representations: a dual coding approach. New York: Oxford University Press.Google Scholar
  45. Rustenbach, S. J. (2003). Metaanalyse: Eine anwendungsorientierte Einführung. Bern: Huber.Google Scholar
  46. Scharinger, C., Kammerer, Y., & Gerjets, P. (2015). Pupil dilation and EEG alpha frequency band power reveal load on executive functions for link-selection processes during text reading. PLoS One, 10(6), e0130608.CrossRefGoogle Scholar
  47. Scheiter, K., & Gerjets, P. (2007). Learner control in hypermedia environments. Educational Psychology Review, 19(3), 285–307.CrossRefGoogle Scholar
  48. Schneider, S., Beege, M., Nebel, S., & Rey, G. D. (2018a). A meta-analysis of how signaling affects learning with media. Educational Research Review, 23, 1–24.CrossRefGoogle Scholar
  49. Schneider, S., Wirzberger, M., & Rey, G. D. (2018b). The moderating role of arousal on the seductive detail effect. Applied Cognitive Psychology.
  50. Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19(4), 469–508.CrossRefGoogle Scholar
  51. Schnotz, W., & Lowe, R. (2008). A unified view of learning from animated and static graphics. In R. Lowe & W. Schnotz (Eds.), Learning with animations: research implications for design (pp. 304–356). New York: Cambridge University Press.Google Scholar
  52. Skuballa, I. T., Schwonke, R., & Renkl, A. (2012). Learning from narrated animations with different support procedures: working memory capacity matters. Applied Cognitive Psychology, 26(6), 840–847.CrossRefGoogle Scholar
  53. 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(4), 411–423.CrossRefGoogle Scholar
  54. Sterne, J. A., Becker, B. J., & Egger, M. (2005). The funnel plot. In H. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta-analysis: prevention, assessment and adjustments (pp. 75–98). West Sussex: Wiley.Google Scholar
  55. Sweller, J. (1988). Cognitive load during problem solving: effects on learning. Cognitive Science, 12(2), 257–285.CrossRefGoogle Scholar
  56. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springer.CrossRefGoogle Scholar
  57. Tobias, S. (1976). Achievement treatment interactions. Review of Educational Research, 46(1), 61–74.CrossRefGoogle Scholar
  58. Vygotski, L. S. (1963). Learning and mental development at school age. In B. Simon & J. Simon (Eds.), Educational psychology in the U.S.S.R (pp. 21–34). London: Routledge & Kegan Paul.Google Scholar
  59. Wickens, C. D., Hollands, J. G., Banbury, S., & Parasuraman, R. (2013). Engineering psychology and human performance (4th ed.). Boston: Pearson Education.Google Scholar
  60. Wilson, D. B. (2001). Practical meta-analysis effect size calculator. Retrieved from Accessed 07/06/2018
  61. Wilson, D. B. (2010). Meta-analysis macros for SAS, SPSS, and Stata. Retrieved from
  62. Wirzberger, M., Herms, R., Esmaeili Bijarsari, S., Eibl, M., & Rey, G. D. (2018). Schema-related cognitive load influences performance, speech, and physiology in a dual-task setting: a continuous multi-measure approach. Cognitive Research: Principles and Implications, 3, 46.
  63. Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S., & Reynolds, J. R. (2007). Event perception: a mind–brain perspective. Psychological Bulletin, 133(2), 273–293.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Günter Daniel Rey
    • 1
    Email author
  • Maik Beege
    • 1
  • Steve Nebel
    • 1
  • Maria Wirzberger
    • 2
  • Tobias H. Schmitt
    • 3
  • Sascha Schneider
    • 1
  1. 1.Psychology of Learning with Digital Media, Institute for Media Research, Faculty of HumanitiesChemnitz University of TechnologyChemnitzGermany
  2. 2.Max Planck Research Group “Rationality Enhancement”Max Planck Institute for Intelligent SystemsTübingenGermany
  3. 3.Department of PhilosophyGoethe University FrankfurtFrankfurtGermany

Personalised recommendations