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

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.

Analysis Use of Application and Social Media Appropriate in Online Learning in Universities Indonesia

“…Research in South Korea states that the covid-19 widespread encompasses a positive impact on getting to materials, online communities, get to classes, inventiveness. On the other side, there are adverse effects such as mental weakness, course students [47]. Interest about the COVID-19 pandemic in Siam some college have cancelled workshops, conferences and sports activities.…”

Competing as the First Out Transgender Female Olympian: A Twitter Network Analysis of Laurel Hubbard During the 2020 Tokyo Games

“…Specifically, the dimension of polarity measures how positive or negative a given entity is, with assigning a score ranging from À1.0 (very negative) to 1.0 (very positive) to each examined entity; similarly, the dimension of subjectivity measures how subjective or objective a given entity is, with assigning a score ranging from 0.0 (very objective) to 1.0 (very subjective) to each examined entity (Text Blob Tutorial, 2021). TextBlob, as a pre-trained approach to assessing sentiments in text, has been frequently applied in education (e.g., Aiyanyo et al, 2021), marketing (e.g., Micu et al, 2017), health communication (e.g., Nemes & Kiss, 2021), and so on.…”

Comparison of Sentiment Analysis Methods Used to Investigate the Quality of Teaching Aids Based on Virtual Simulators of Embedded Systems

“…Several authors were using different methods of sentiment analysis connected with remote and distance learning that were especially used during the COVID-19 pandemic [23][24][25][26].…”