Artificial Intelligence-Based Analytics for Impacts of COVID-19 and Online Learning on College Students' Mental Health

• URL: http://arxiv.org/abs/2202.07441v3
• Date: Mon, 5 Sep 2022 19:43:46 GMT
• Title: Artificial Intelligence-Based Analytics for Impacts of COVID-19 and Online Learning on College Students' Mental Health
• Authors: Mostafa Rezapour, Scott K. Elmshaeuser
• Abstract summary: COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. The virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This paper seeks to understand how the COVID-19 pandemic and increase in online learning impact college students' emotional wellbeing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security.

Related papers

• A case study of university student networks and the COVID-19 pandemic using a social network analysis approach in halls of residence [0.32985979395737786]
  The aim of our research is to describe the structural behaviour of students in university residences during the COVID-19 pandemic. The leadership on the university residence was measured using centrality measures. Students with higher social reputations experience higher levels of pandemic contagion in relation to COVID-19 infection.
  arXiv  Detail & Related papers  (2024-02-14T14:57:25Z)
• Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
  Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths. The pandemic has impacted and even changed human behavior in almost every aspect. Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
  arXiv  Detail & Related papers  (2023-03-23T17:19:26Z)
• "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation [60.268682953952506]
  We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models. We provide a strategy to combat social media posts that are likely to cause widespread harm.
  arXiv  Detail & Related papers  (2022-06-12T19:41:01Z)
• Investigating the impact of COVID-19 on Online Learning-based Web Behavior [0.0]
  The study specifically focused on investigating Google Search-based web behavior data as Google is the most popular search engine globally. The impact of COVID-19 related to online learning-based web behavior on Google was studied for the top 20 worst affected countries in terms of the total number of COVID-19 cases.
  arXiv  Detail & Related papers  (2022-04-27T01:38:10Z)
• Identifying pandemic-related stress factors from social-media posts -- effects on students and young-adults [2.198430261120653]
  The COVID-19 pandemic has thrown natural life out of gear across the globe. Strict measures are deployed to curb the spread of the virus that is causing it, and the most effective of them have been social isolation. This has led to wide-spread gloom and depression across society but more so among the young and the elderly.
  arXiv  Detail & Related papers  (2020-12-01T08:42:27Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic [66.80677233314002]
  Pandemic of the novel Coronavirus Disease 2019 (COVID-19) has presented governments with ultimate challenges. In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President. This paper aims to discover the social implications of this unprecedented disruption in our interactive society by mining people's opinions on social media.
  arXiv  Detail & Related papers  (2020-04-21T13:02:38Z)
• Mining Coronavirus (COVID-19) Posts in Social Media [3.04585143845864]
  World Health Organization (WHO) characterized the novel coronavirus (COVID-19) as a global pandemic on March 11th, 2020. In this article we report the preliminary results of our study on automatically detecting the positive reports of COVID-19 from social media user postings using state-of-the-art machine learning models.
  arXiv  Detail & Related papers  (2020-03-28T23:38:50Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.