Please use this identifier to cite or link to this item: http://hdl.handle.net/10497/24207
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDu, Hanxiangen
dc.contributor.authorXing, Wanlien
dc.contributor.authorZhu, Gaoxiaen
dc.date.accessioned2022-06-23T08:29:11Z-
dc.date.available2022-06-23T08:29:11Z-
dc.date.issued2022-
dc.identifier.citationDu, H., Xing, W., & Zhu, G. (2022). Mining teacher informal online learning networks: Insights from massive educational chat tweets. Journal of Educational Computing Research. Advance online publication. https://doi.org/10.1177/07356331221103764en
dc.identifier.issn0735-6331 (print)-
dc.identifier.issn1541-4140 (online)-
dc.identifier.urihttp://hdl.handle.net/10497/24207-
dc.description.abstractSocial-media-based teacher learning networks have the affordance to grant flexibility of time and space for teachers’ professional learning, support the development and sustainability of social networking, and meet their just-in-time needs for exchanging knowledge, negotiating meaning and accessing resources. However, most existing research on teacher online learning networks relies on qualitative methods and self-report data. There is a lack of study using quantitative methods to study large networks, especially using authentic data from social media. This work adds to the literature through mining teacher informal online learning networks using authentic data retrieved from Twitter. Specifically, we collected around half a million tweets and developed a network with the data. Then, various social network analysis techniques were utilized to explore the network structure and characteristics, participants’ behavioral patterns and how individuals connected with each other. We found that members of massive teacher informal online learning networks tended to communicate more with others of similar characteristics forming homogeneous communities, while hub participants connected many small communities which are significantly from one another, and hence, are the key to degree heterogeneity in a large network.en
dc.language.isoenen
dc.relation.ispartofJournal of Educational Computing Researchen
dc.titleMining teacher informal online learning networks: Insights from massive educational chat tweetsen
dc.typeArticleen
dc.description.versionAccepted versionen
dc.identifier.doi10.1177/07356331221103764-
local.message.claim2022-06-23T16:29:48.603+0800|||rp00114|||submit_approve|||dc_contributor_author|||None*
dc.subject.keywordTeacher professional developmenten
dc.subject.keywordInformal learningen
dc.subject.keywordSocial mediaen
dc.subject.keywordLearning communitiesen
dc.subject.keywordSocial network analysisen
dc.subject.keywordOnline communityen
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextWith file-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextOpen-
Appears in Collections:Journal Articles
Files in This Item:
File Description SizeFormat 
JECR-2022-103764.pdf857.25 kBAdobe PDFThumbnail
View/Open
Show simple item record

Page view(s)

4
checked on Jun 25, 2022

Download(s)

2
checked on Jun 25, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.