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Title: Interaction pattern analysis in cMOOCs based on the connectivist interaction and engagement framework
Author: Wang, Zhijun
Anderson, Terry
Chen, Li
Barberà Gregori, Elena
Others: Jiangnan University
Athabasca University
Beijing Normal University
Universitat Oberta de Catalunya (UOC)
Keywords: MOOC
Issue Date: 18-Mar-2016
Publisher: British Journal of Educational Technology
Citation: Wang, Z., Anderson, T., Chen, L. & Barbera, E. (2017). Interaction pattern analysis in cMOOCs based on the connectivist interaction and engagement framework. British Journal of Educational Technology, 48(2), 683-699. doi: 10.1111/bjet.12433
Project identifier: info:eu-repo/grantAgreement/JUSRP51418B
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Abstract: Connectivist learning is interaction-centered learning. A framework describing interaction and cognitive engagement in connectivist learning was constructed using logical reasoning techniques. The framework and analysis was designed to help researchers and learning designers understand and adapt the characteristics and principles of interaction in connectivist learning contexts. In this study empirical evidence to support and further develop this framework is presented. This study analyzed 6 weeks of data harvested from the daily newsletter, Twitter, and a Facebook group in a well-known cMOOC led by George Siemens and Stephen Downes. These text transcripts were analyzed using a deductive approach of qualitative content analysis. This study revealed the main activity patterns of participants as they engage in four levels of interaction (operation interaction, wayfinding interaction, sensemaking interaction, and innovation interaction) during the MOOC. Generally the framework serves as a conceptual model to understand and to analyze the interaction in this cMOOC, although some implied interaction is hard to recognize and categorize. The relationship of the four levels of interaction and the role of each element in the framework were explored with the intent of offering the framework as a conceptual and analytic tool to guide both researchers and practitioners in designing and studying connectivist learning.
Language: English
ISSN: 0007-1013MIAR
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