Authors:
Anna Weigand
1
;
2
;
Maj Jacob
1
;
Maria Rauschenberger
1
and
Maria José Escalona Cuaresma
2
Affiliations:
1
Faculty of Technology, University of Applied Sciences Emden/Leer, Emden, Germany
;
2
Department of Computer Languages and Systems, University of Seville, Seville, Spain
Keyword(s):
Machine Learning, Data Set, Topic Modeling, Twitter, X, Twitterlehrerzimmer, Twlz, Covid, ChatGPT.
Abstract:
This study examines the shifting discussions of teachers within the #twlz community on Twitter across three phases of the COVID-19 pandemic – before school closures and during the first and second school closures. We analyzed tweets from January 2020 to May 2021 to identify topics related to education, digital transformation, and the challenges of remote teaching. Using machine learning and ChatGPT, we categorized discussions that transitioned from general educational content to focused dialogues on online education tools during school closures. Before the pandemic, discussions were generally focused on education and digital transformation. During the first school closures, conversations shifted to more specific topics, such as online education and tools to adapt to distance learning. Discussions during the second school closures reflected more precise needs related to fluctuating pandemic conditions and schooling requirements. Our findings reveal a consistent increase in the specifi
city and urgency of the topics over time, particularly regarding digital education.
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