Stewart Martin
A critical analysis of the theoretical construction and empirical measurement of cognitive load
Martin, Stewart
Authors
Contributors
Robert Z. Zheng
Editor
Abstract
The application of cognitive load theory has been at the forefront of work in cognition and learning for some time. Cognitive load theory seeks to explain how and why some material is more difficult to learn and is based on the proposition that the human brain uses two types of memory: short-term (working) and long-term (storage) memory. Cognitive load measurements are relative, transient, and subject to a significant number of empirical and time-sensitive factors. Measures of cognitive load should therefore be treated with caution and differentiation between germane, extraneous, and intrinsic load remains challenging for all instruments. Overall mental performance is a multidimensional construct, and cognitive load theory does not take account of factors such as the influence of individual goals, or beliefs on cognitive performance. It has been proposed that cognitive load theory implies that the physical integration of multiple sources of information is generally beneficial for learners, as happens when, for example, text and images are combined.
Citation
Martin, S. (2017). A critical analysis of the theoretical construction and empirical measurement of cognitive load. In R. Z. Zheng (Ed.), Cognitive load measurement and application: A theoretical framework for meaningful research and practice (29-44). Routledge. https://doi.org/10.4324/9781315296258-3
Publication Date | Nov 22, 2017 |
---|---|
Deposit Date | Jun 10, 2019 |
Publisher | Routledge |
Pages | 29-44 |
Book Title | Cognitive load measurement and application: A theoretical framework for meaningful research and practice |
ISBN | 9781315296258 |
DOI | https://doi.org/10.4324/9781315296258-3 |
Public URL | https://hull-repository.worktribe.com/output/1963271 |
Publisher URL | https://www.taylorfrancis.com/books/e/9781315296258/chapters/10.4324/9781315296258-3 |
Contract Date | Jun 1, 2017 |
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