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August 2015Volume 11, Issue 8, Pages 355–360
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Using Cognitive Load Theory to Inform Simulation Design and Practice

Gabriel B. Reedy
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Gabriel B. Reedy
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, MEd, PhD, CPsychol'Correspondence information about the author MEd, PhD, CPsychol Gabriel B. Reedy
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Gabriel B. Reedy
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Correspondence

  • Corresponding author.
King's College London, London, SE1 8WA, UK
Open AccessUsing Cognitive Load Theory to Inform Simulation Design and Practice
DOI: http://dx.doi.org/10.1016/j.ecns.2015.05.004
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Highlights

  • •Cognitive load theory helps explain how people process information and how people learn.
  • •Cognitive overload can occur from too much new information, or poorly designed instruction.
  • •Simulation can optimize the efficiency of learning and minimize cognitive load.

Abstract

Cognitive science has long sought to explore the ways in which information is processed by the brain and to generate from this overarching constructs and models of thinking and learning. This article explores cognitive load theory, one approach to understanding learning, and articulates ways in which what is known about how people experience new learning environments can be used to create and optimize effective simulation learning environments. When designing and implementing simulation-based learning, extraneous load must be minimized by good design and the intrinsic load must be optimized for the learner. Doing so creates a more effective and valuable learning experience.

Keywords:

learning theory, simulation design, cognitive science, Cognitive load theory

The research was funded by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response at King's College London in partnership with Public Health England.

The views expressed are those of the author(s) and not necessarily those of the National Health Service (NHS), the National Institute for Health Research (NIHR), the Department of Health or Public Health England.

© 2015 International Nursing Association for Clinical Simulation and Learning. Published by Elsevier Inc.
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August 2015Volume 11, Issue 8, Pages 355–360
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