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

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.

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Notes

  1. 1.

    The SNARC effect (Spatial Numerical Association of Response Code) shows an association between numbers and spatial position. Small numbers are associated with left or top part of the screen and larger number with right or lower part of the screen. It was originally found with manual responses, responses with the left hand being faster than the right hand for small numbers and conversely, but further extended to cross modal responses and to all associations of space and numbers.

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Correspondence to Sébastien Puma.

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Puma, S., Matton, N., Paubel, P. et al. Cognitive Load Theory and Time Considerations: Using the Time-Based Resource Sharing Model. Educ Psychol Rev 30, 1199–1214 (2018). https://doi.org/10.1007/s10648-018-9438-6

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Keywords

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