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Autor/inn/enYang, Junhe; Ding, Juncheng
TitelA Text Mining Analysis of Back-to-School Discussion during COVID-19
QuelleIn: Journal of Interactive Learning Research, 32 (2021) 2, S.147-167 (21 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1093-023X
SchlagwörterContent Analysis; Discourse Analysis; Social Media; Computer Mediated Communication; Discussion; Educational Change; Learning Analytics; School Closing; Educational Planning; Teaching Methods; Distance Education; Educational Strategies; Blended Learning; Educational Resources
AbstractPreparing for back-to-school is essential, and it is also complicated because it involves different groups of people, and they have different needs. Pandemics, like COVID-19, make the situation even more difficult. Better preparation during a pandemic needs a comprehensive understanding of the situation. However, few studies have provided such information, especially during a pandemic. Furthermore, traditional research methods can hardly address the problems. This paper used text mining approaches to interpret open Twitter discussions on back-to-school preparation during COVID-19 and generated an inclusive insight into the preparation. Specifically, we collected 26,219 related tweets and analyzed the user profile descriptions to determine who was involved in the preparation. We also examined the most frequently retweeted tweets and conducted a topic modeling analysis to find people's concerns. Our research provides a holistic view of preparation for back-to-school during the pandemic, and it could help better prepare for back-to-school in the future pandemic. (As Provided).
AnmerkungenAssociation for the Advancement of Computing in Education. P.O. Box 719, Waynesville, NC 28786. Tel: 828-246-9558; Fax: 828-246-9557; e-mail: info@aace.org; Web site: http://www.aace.org
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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