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

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 i...

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Main Authors: Imatitikua D. Aiyanyo, Hamman Samuel, Heuiseok Lim
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/13/9/4986
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spelling doaj-fdf40c89a6be457280a6448266bbde162021-04-29T23:02:23ZengMDPI AGSustainability2071-10502021-04-01134986498610.3390/su13094986Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine LearningImatitikua D. Aiyanyo0Hamman Samuel1Heuiseok Lim2College of Informatics, Korea University, Seoul 02841, KoreaDepartment of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, CanadaCollege of Informatics, Korea University, Seoul 02841, KoreaIn 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.https://www.mdpi.com/2071-1050/13/9/4986COVID-19studentseducatorssentiment analysismachine learning
collection DOAJ
language English
format Article
sources DOAJ
author Imatitikua D. Aiyanyo
Hamman Samuel
Heuiseok Lim
spellingShingle Imatitikua D. Aiyanyo
Hamman Samuel
Heuiseok Lim
Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning
Sustainability
COVID-19
students
educators
sentiment analysis
machine learning
author_facet Imatitikua D. Aiyanyo
Hamman Samuel
Heuiseok Lim
author_sort Imatitikua D. Aiyanyo
title Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning
title_short Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning
title_full Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning
title_fullStr Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning
title_full_unstemmed Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning
title_sort effects of the covid-19 pandemic on classrooms: a case study on foreigners in south korea using applied machine learning
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2021-04-01
description 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.
topic COVID-19
students
educators
sentiment analysis
machine learning
url https://www.mdpi.com/2071-1050/13/9/4986
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AT heuiseoklim effectsofthecovid19pandemiconclassroomsacasestudyonforeignersinsouthkoreausingappliedmachinelearning
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