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Applying MOOCocracy learning culture themes to improve digital course design and online learner engagement.

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Title: Applying MOOCocracy learning culture themes to improve digital course design and online learner engagement.
Authors: Akinkuolie, Babatunde (AUTHOR), Shortt, Mitchell (AUTHOR)
Source: Educational Technology Research & Development. Feb2021, Vol. 69 Issue 1, p369-372. 4p.
Subjects: Student engagement, Massive open online courses, Online education
Abstract: This piece is a short response to Loizzo and Ertmer's (2016) MOOCocracy: the learning culture of massive open online courses for the special issue: Shifting to Digital: Informing the rapid development, deployment, and future of teaching and learning. Key ideas, value, and future research implications are addressed in relation to MOOC design using the MOOCocracy themes. [ABSTRACT FROM AUTHOR]
Copyright of Educational Technology Research & Development is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: This piece is a short response to Loizzo and Ertmer's (2016) MOOCocracy: the learning culture of massive open online courses for the special issue: Shifting to Digital: Informing the rapid development, deployment, and future of teaching and learning. Key ideas, value, and future research implications are addressed in relation to MOOC design using the MOOCocracy themes. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Educational Technology Research & Development is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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