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Educational Psychology Review

, Volume 30, Issue 3, pp 1199–1214 | Cite as

Cognitive Load Theory and Time Considerations: Using the Time-Based Resource Sharing Model

  • Sébastien Puma
  • Nadine Matton
  • Pierre-Vincent Paubel
  • André Tricot
Replication

Abstract

For a long time, Cognitive Load Theory has considered working memory models as tools to advance research on learning. It has used working memory capacity models, where working memory is viewed as being composed of a discrete number of slots (i.e., chunks) that can be kept active. However, recent results have shown that for a fixed quantity of information, the mere pace of information presentation can affect learning performance. Commonly used working memory models cannot explain such results. Here, we propose to use a new model in the field of Cognitive Load Theory, the Time-Based Resource Sharing model, which enables time to be taken into account when describing working memory solicitation. In two experiments, we tested hypotheses allowed by the model. Results showed that the Time-Based Resource Sharing model can assist the investigation of information presentation pace effects during learning, as long as prior knowledge is taken into account. Particularly, the results suggest a new interpretation of intrinsic and extrinsic load that could relate them to the time needed to process information.

Keywords

Cognitive Load Theory Time-Based Resource Sharing model Working memory Meaningful items Expertise Dynamic variations 

References

  1. Adams, J. W., & Hitch, G. J. (1997). Working memory and children’s mental addition. Journal of Experimental Child Psychology, 67, 21–38.CrossRefGoogle Scholar
  2. Anderson, J. R. (2010). Cognitive psychology and its implications. New York: Worth Publishers.Google Scholar
  3. Baddeley, A. D. (1986). Working memory. Oxford: Oxford University Press.Google Scholar
  4. Baddeley, A. D., & Hitch, G. (1974). Working memory. The Psychology of Learning and Motivation, 8, 47–89.CrossRefGoogle Scholar
  5. Barrouillet, P., Bernardin, S., & Camos, V. (2004). Time constraints and resource sharing in adults’ working memory spans. Journal of Experimental Psychology: General, 133, 83–100.CrossRefGoogle Scholar
  6. Barrouillet, P., Bernardin, S., Portrat, S., Vergauwe, E., & Camos, V. (2007). Time and cognitive load in working memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 570–585.Google Scholar
  7. Barrouillet, P., & Camos, V. (2014). On the proper reading of the TBRS model: reply to Oberauer and Lewandowsky. Frontiers in Psychology, 5, 1–3.CrossRefGoogle Scholar
  8. Barrouillet, P., & Camos, V. (2015). Working memory: loss and reconstruction. New York: Psychology Press.Google Scholar
  9. Broadbent, D. E. (1958). Perception and communication. New York: Pergamon Press Retrieved from: http://www.archive.org/details/perceptioncommunOObroa.CrossRefGoogle Scholar
  10. Bull, R., Espy, K. A., & Wiebe, S. A. (2008). Short-term memory, working memory, and executive functioning in preschoolers: longitudinal predictors of mathematical achievement at age 7 years. Developmental Neuropsychology, 33, 205–228.CrossRefGoogle Scholar
  11. Chanquoy, L., Tricot, A., & Sweller, J. (2007). La charge cognitive: Théorie et applications. Armand Colin.Google Scholar
  12. Chen, O., Kalyuga, S., & Sweller, J. (2015). The worked example effect, the generation effect, and element interactivity. Journal of Educational Psychology, 107, 689–704.CrossRefGoogle Scholar
  13. Chen, O., Kalyuga, S., & Sweller, J. (2016). The expertise reversal effect is a variant of the more general element interactivity effect. Educational Psychology Review, 29, 1–13.Google Scholar
  14. Chi, M. T. H. (2006). Two approaches to the study of experts’ characteristics. In K. A. Ericsson, N. Charness, P. J. Feltovitch, & R. R. Hoffman (Eds.), The Cambridge Handbook of Expertise and Expert Performance. Cambridge: Cambridge University Press.Google Scholar
  15. Chi, M. T. H., Glaser, R., & Farr, M. J. (1988). The nature of expertise. Hillsdale: Erlbaum.Google Scholar
  16. Cowan, N. (1995). Attention and memory: an integrated framework. Oxford Psychology Series, No. 26. New York: Oxford University Press (Paperback edition: 1997).Google Scholar
  17. Cowan, N. (2014). Working memory underpins cognitive development, learning, and education. Educational Psychology Review, 26, 197–223.CrossRefGoogle Scholar
  18. Dehaenne, S., Bossini, S., & Giraux, P. (1993). The mental representation of parity and number magnitude. Journal of Experimental Psychology: General, 122, 371–396.CrossRefGoogle Scholar
  19. De Jong, T. (2010). Cognitive Load Theory, educational research, and instructional design: some food for thought. Instructional Science, 38, 105–134.CrossRefGoogle Scholar
  20. Ericsson, K. A. (2006). An introduction to Cambridge Handbook of Expertise and Expert Performance: its development, organization and content. In K. A. Ericsson, N. Charness, P. Feltovitch, & R. R. Hoffman (Eds.), Cambridge handbook of expertise and expert performance (pp. 3–19). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  21. Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245.CrossRefGoogle Scholar
  22. Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 15, 313–331.CrossRefGoogle Scholar
  23. Groen, G. J., & Parkman, J. M. (1972). A chronometric analysis of simple addition. Psychological Review, 79(4), 329–343.Google Scholar
  24. Gulbinaite, R., Johnson, A., de Jong, R., Morey, C. C., & van Rijn, H. (2014). Dissociable mechanisms underlying individual differences in visual working memory capacity. NeuroImage, 99, 197–206.CrossRefGoogle Scholar
  25. Howell, D. C. (2007). Statistical methods for psychology (7th ed.). Boston: Cengage Learning.Google Scholar
  26. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.CrossRefGoogle Scholar
  27. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–31.CrossRefGoogle Scholar
  28. Leahy, W., & Sweller, J. (2016). Cognitive load theory and the effects of transient information on the modality effect. Instructional Science, 44(1), 107–123.Google Scholar
  29. Moreno, R., & Valdez, A. (2005). Cognitive load and learning effects of having students organize pictures and words in multimedia environments: the role of student interactivity and feedback. Educational Technology Research & Development, 53, 35–45.CrossRefGoogle Scholar
  30. Oberauer, K., Farrell, S., Jarrold, C., & Lewandowsky, S. (2016). What limits working memory capacity? Psychological Bulletin, 142, 758–799.CrossRefGoogle Scholar
  31. Paas, F., Tuovinen, J. E., Tabbers, H., & van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance Cognitive Load Theory. Educational Psychologist, 38, 63–72.CrossRefGoogle Scholar
  32. Portrat, S., Camos, V., & Barrouillet, P. (2009). Working memory in children: a time constrained functioning similar to adults. Journal of Experimental Child Psychology, 102, 368–374.CrossRefGoogle Scholar
  33. Rahgubar, K. P., Barnes, M. A., & Hecht, S. A. (2010). Working memory and mathematics: a review of developmental, individual difference, and cognitive approaches. Learning and Individual Differences, 20, 110–122.CrossRefGoogle Scholar
  34. Salvucci, D. D., & Taatgen, N. A. (2010). The multitasking mind. Oxford: Oxford University Press.Google Scholar
  35. Schmidt-Weigand, F., Kohnert, A., & Glowalla, U. (2010). A closer look at split visual attention in system- and self-paced instruction in multimedia learning. Learning and Instruction, 20, 100–110.CrossRefGoogle Scholar
  36. Schneider, W. (1985). Training high performance skills: fallacies and guidelines. Human Factors, 27, 285–300.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. Sweller, J. (1988). Cognitive load during problem solving: effects on learning. Cognitive Science, 12, 257–285.CrossRefGoogle Scholar
  39. Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22, 123–138.CrossRefGoogle Scholar
  40. Sweller, J. (2011). Cognitive Load Theory. In J. Mestre & B. Ross (Eds.), The psychology of learning and motivation: cognition in education (Vol. 55, pp. 37–76). Oxford: Academic.Google Scholar
  41. Sweller, J., Van Merrienboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.CrossRefGoogle Scholar
  42. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: Springer.CrossRefGoogle Scholar
  43. Tindall-Ford, S., Chandler, P., & Sweller, J. (1997). When two sensory modes are better than one. Journal of Experimental Psychology: Applied, 3(4), 257–287.Google Scholar
  44. 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.CrossRefGoogle Scholar
  45. Vergauwe, E., Dewaele, N., Langerock, N., & Barrouillet, P. (2012). Evidence for a central pool of general resources in working memory. Journal of Cognitive Psychology, 24, 359–366.CrossRefGoogle Scholar
  46. Wilcox, R. R. (1987). New statistical procedures for the social sciences. Hillsdale: Erlbaum.Google Scholar
  47. Zbrodoff, N. J., & Logan, G. D. (2005). What everyone finds. The problem-size effect. In J. I. D. Campbell (Ed.), Handbook of mathematical cognition (pp. 331–345). New-York and Hove: Psychology Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.CLLE laboratoryUniversity of Toulouse & CNRS CLLE-LTC (UMR 5263)Toulouse Cedex 9France
  2. 2.PARAGRAPHE LaboratoryUniversity of Paris 8 (EA 349)Saint-DenisFrance
  3. 3.ENAC (École Nationale d’Aviation Civile)ToulouseFrance

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