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Using cognitive load theory to structure computer-based learning including MOOCs

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journal contribution
posted on 31.03.2020, 12:18 authored by Ouhao ChenOuhao Chen, G Woolcott, J Sweller
© 2017 John Wiley & Sons Ltd A massive, open, online course (MOOC) is a form of computer-based learning that offers open access, internet-based education for unlimited numbers of participants. However, the general quality and utility of MOOCs has been criticized. Most MOOCs have been structured with minimal consideration of relevant aspects of human cognitive architecture and instructional design principles. This paper suggests cognitive load theory, with its roots embedded in our knowledge of human cognitive architecture and evolutionary educational psychology, is ideally placed to provide instructional design principles for all forms of computer-based learning, including MOOCs. The paper outlines the theory and indicates instructional design principles that could be used to structure online learning and to provide an appropriate base for instructional design when using computer-based learning.

History

School

  • Science

Department

  • Mathematics Education Centre

Published in

Journal of Computer Assisted Learning

Volume

33

Issue

4

Pages

293 - 305

Citation

Chen, Ouhao; Woolcott, G; Sweller, J (2017): Using cognitive load theory to structure computer-based learning including MOOCs. Journal of Computer Assisted Learning, 33(4), pp. 293-305.

Publisher

Wiley Online Library

Version

AM (Accepted Manuscript)

Rights holder

© Wiley

Publisher statement

This is the peer reviewed version of the following article: Chen, Ouhao; Woolcott, G; Sweller, J (2017): Using cognitive load theory to structure computer-based learning including MOOCs. Journal of Computer Assisted Learning, 33(4), pp. 293-305, which has been published in final form at https://doi.org/10.1111/jcal.12188. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions

Acceptance date

12/02/2017

Publication date

2017-03-20

Copyright date

2017

ISSN

0266-4909

eISSN

1365-2729

Language

en

Depositor

Dr Ouhao Chen Deposit date: 31 March 2020