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

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 rel...

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Publicado en:Journal of Computer Assisted Learning Vol. 33; no. 4; pp. 293 - 306
Autores principales: Chen, O., Woolcott, G., Sweller, J.
Formato: Journal Article
Publicado: Wiley-Blackwell Aug2017
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1111/jcal.12188
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        atl: Using cognitive load theory to structure computer-based learning including MOOCs.
      aug:
        au:
          Chen, O.
          Woolcott, G.
          Sweller, J.
        affil: School of Education, Southern Cross University, Sydney Australia
      sug:
        subj:
          Education, Non-Traditional
          Cognition
          Learning Methods
          Learning Theory
          Curriculum Development
          Psychology, Educational
      ab: 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.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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