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Article

Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning

1
College of Informatics, Korea University, Seoul 02841, Korea
2
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
*
Authors to whom correspondence should be addressed.
Academic Editors: Emanuele Cannizzaro, Tiziana Ramaci, Massimiliano Barattucci and Fulvio Plescia
Sustainability 2021, 13(9), 4986; https://doi.org/10.3390/su13094986
Received: 18 March 2021 / Revised: 21 April 2021 / Accepted: 26 April 2021 / Published: 29 April 2021
(This article belongs to the Special Issue Working during the COVID-19 Global Pandemic)
In this study, we qualitatively and quantitatively examine the effects of COVID-19 on classrooms, students, and educators. Using a new Twitter dataset specific to South Korea during the pandemic, we sample the sentiment and strain on students and educators using applied machine learning techniques in order to identify various topical pain points emerging during the pandemic. Our contributions include a novel and open source geo-fenced dataset on student and educator opinion within South Korea that we are making available to other researchers as well. We also identify trends in sentiment and polarity over the pandemic timeline, as well as key drivers behind the sentiments. Moreover, we provide a comparative analysis of two widely used pre-trained sentiment analysis approaches with TextBlob and VADER using statistical significance tests. Ultimately, we analyze how public opinion shifted on the pandemic in terms of positive sentiments about accessing course materials, online support communities, access to classes, and creativity, to negative sentiments about mental fatigue, job loss, student concerns, and overwhelmed institutions. We also initiate initial discussions about the concept of actionable sentiment analysis by overlapping polarity with the concept of trigger management to assist users in coping with negative emotions. We hope that insights from this preliminary study can promote further utilization of social media datasets to evaluate government messaging, population sentiment, and multi-dimensional analysis of pandemics. View Full-Text
Keywords: COVID-19; students; educators; sentiment analysis; machine learning COVID-19; students; educators; sentiment analysis; machine learning
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MDPI and ACS Style

Aiyanyo, I.D.; Samuel, H.; Lim, H. Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning. Sustainability 2021, 13, 4986. https://doi.org/10.3390/su13094986

AMA Style

Aiyanyo ID, Samuel H, Lim H. Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning. Sustainability. 2021; 13(9):4986. https://doi.org/10.3390/su13094986

Chicago/Turabian Style

Aiyanyo, Imatitikua D.; Samuel, Hamman; Lim, Heuiseok. 2021. "Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning" Sustainability 13, no. 9: 4986. https://doi.org/10.3390/su13094986

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