How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC

In this research paper, the authors analyse the collected data output during a 36 week cMOOC. Six-week data streams from blogs, Twitter, a Facebook group, and video conferences were tracked from the daily newsletter and the MOOCs’ hashtag (#Change 11). This data was analysed using content analysis a...

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Autores principales:	Zhijun Wang, Terry Anderson, Li Chen
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2018
Materias:	participation pattern cMOOCs social network analysis connectivist learning connectivism interaction Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/5cb146202eef4ddaa59d6f647b0b0253
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spelling	oai:doaj.org-article:5cb146202eef4ddaa59d6f647b0b02532021-12-02T18:03:00ZHow Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC10.19173/irrodl.v19i1.32691492-3831https://doaj.org/article/5cb146202eef4ddaa59d6f647b0b02532018-02-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/3269https://doaj.org/toc/1492-3831In this research paper, the authors analyse the collected data output during a 36 week cMOOC. Six-week data streams from blogs, Twitter, a Facebook group, and video conferences were tracked from the daily newsletter and the MOOCs’ hashtag (#Change 11). This data was analysed using content analysis and social network analysis within an interpretative research paradigm. The content analysis was used to examine the technology learners used to support their learning while the social network analysis focused on the participant in different spaces and their participation patterns in connectivist learning. The findings from this research include: 1) A variety of technologies were used by learners to support their learning in this course; 2) Four types of participation patterns were reveled, including unconnected floaters, connected lurkers, connected participants, and active contributors. The participation of learners displays the participation inequality typical of social media, but the ratio of active contributors is much higher than xMOOCs; 3) There were five basic structures of social networks formed in the learning; and 4) The interaction around topics and topic generation supports the idea of learning as network creation after the analysis of participation patterns that are based on some deep interactive topic. The aim of this study is to gain insight into the behaviors of learners in a cMOOC in an open and distributed online environment, so that future MOOCs designers and facilitators can understand, design and facilitate more effective MOOCs for learners. Zhijun WangTerry AndersonLi ChenAthabasca University Pressarticleparticipation patterncMOOCssocial network analysisconnectivist learningconnectivisminteractionSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 19, Iss 1 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	participation pattern cMOOCs social network analysis connectivist learning connectivism interaction Special aspects of education LC8-6691
spellingShingle	participation pattern cMOOCs social network analysis connectivist learning connectivism interaction Special aspects of education LC8-6691 Zhijun Wang Terry Anderson Li Chen How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC
description	In this research paper, the authors analyse the collected data output during a 36 week cMOOC. Six-week data streams from blogs, Twitter, a Facebook group, and video conferences were tracked from the daily newsletter and the MOOCs’ hashtag (#Change 11). This data was analysed using content analysis and social network analysis within an interpretative research paradigm. The content analysis was used to examine the technology learners used to support their learning while the social network analysis focused on the participant in different spaces and their participation patterns in connectivist learning. The findings from this research include: 1) A variety of technologies were used by learners to support their learning in this course; 2) Four types of participation patterns were reveled, including unconnected floaters, connected lurkers, connected participants, and active contributors. The participation of learners displays the participation inequality typical of social media, but the ratio of active contributors is much higher than xMOOCs; 3) There were five basic structures of social networks formed in the learning; and 4) The interaction around topics and topic generation supports the idea of learning as network creation after the analysis of participation patterns that are based on some deep interactive topic. The aim of this study is to gain insight into the behaviors of learners in a cMOOC in an open and distributed online environment, so that future MOOCs designers and facilitators can understand, design and facilitate more effective MOOCs for learners.
format	article
author	Zhijun Wang Terry Anderson Li Chen
author_facet	Zhijun Wang Terry Anderson Li Chen
author_sort	Zhijun Wang
title	How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC
title_short	How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC
title_full	How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC
title_fullStr	How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC
title_full_unstemmed	How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC
title_sort	how learners participate in connectivist learning: an analysis of the interaction traces from a cmooc
publisher	Athabasca University Press
publishDate	2018
url	https://doaj.org/article/5cb146202eef4ddaa59d6f647b0b0253
work_keys_str_mv	AT zhijunwang howlearnersparticipateinconnectivistlearningananalysisoftheinteractiontracesfromacmooc AT terryanderson howlearnersparticipateinconnectivistlearningananalysisoftheinteractiontracesfromacmooc AT lichen howlearnersparticipateinconnectivistlearningananalysisoftheinteractiontracesfromacmooc
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How Learners Participate in Connectivist Learning: An Analysis of the Interaction Traces From a cMOOC

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