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

Imatitikua D Aiyanyo,Heuiseok Lim,Hamman Samuel

doi:10.3390/su13094986

Imatitikua D Aiyanyo, Heuiseok Lim + Show 1 more

Open Access

https://doi.org/10.3390/su13094986

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Journal: Sustainability	Publication Date: Apr 29, 2021
Citations: 8	License type: CC BY 4.0

Affiliation: Korea University, University of Alberta

Abstract

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.

Highlights

The COVID-19 pandemic has affected several industries worldwide and especially the educational sector as most countries temporarily closed their institutions
The overarching methodology of this research involves data collection and sentiment analysis. This is followed by quantitative analysis of emotional polarity and associated topics using applied Machine Learning (ML) for sentiment analysis using two unsupervised and pre-trained libraries, namely TextBlob and Valence Aware Dictionary for sEntiment Reasoning (VADER), as well as qualitative analysis of anecdotal tweets in order to understand and provide insights on the overall emotions being expressed by South Korean English speaking tweeters
We have examined the effects of COVID-19 on students and educators by analyzing and summarizing data collected from Twitter using machine learning with sentiment analysis to analyze emotions and identified key topics via qualitative analysis

Summary

Introduction

The COVID-19 pandemic has affected several industries worldwide and especially the educational sector as most countries temporarily closed their institutions. With the educational institutes closed, learners and educators have had to find ways to continue learning. Some countries were fortunate enough to have the proper infrastructures in place to move the classrooms online from the start. One of these countries is South Korea, as even before the pandemic, South Korea had been developing K-MOOCs and had launched their 5G internet [1]. 99.7% of South Korean households have access to the Internet, and 99.9% of South Korean teenagers use the Internet for education purposes [2].

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