Assessing COVID-19 Impacts on College Students via Automated Processing of Free-form Text

• URL: http://arxiv.org/abs/2012.09369v1
• Date: Thu, 17 Dec 2020 02:46:48 GMT
• Title: Assessing COVID-19 Impacts on College Students via Automated Processing of Free-form Text
• Authors: Ravi Sharma, Sri Divya Pagadala, Pratool Bharti, Sriram Chellappan, Trine Schmidt and Raj Goyal
• Abstract summary: We process free-form texts generated by college students via an app specifically designed to assess and improve their mental health. Our analysis reveals that topics like Education became noticeably less important to students post COVID-19, while Health became much more trending. We expect our study to have an impact on policy-makers in higher education across several spectra, including college administrators, teachers, parents, and mental health counselors.
• Score: 3.1569066974597293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we report experimental results on assessing the impact of COVID-19 on college students by processing free-form texts generated by them. By free-form texts, we mean textual entries posted by college students (enrolled in a four year US college) via an app specifically designed to assess and improve their mental health. Using a dataset comprising of more than 9000 textual entries from 1451 students collected over four months (split between pre and post COVID-19), and established NLP techniques, a) we assess how topics of most interest to student change between pre and post COVID-19, and b) we assess the sentiments that students exhibit in each topic between pre and post COVID-19. Our analysis reveals that topics like Education became noticeably less important to students post COVID-19, while Health became much more trending. We also found that across all topics, negative sentiment among students post COVID-19 was much higher compared to pre-COVID-19. We expect our study to have an impact on policy-makers in higher education across several spectra, including college administrators, teachers, parents, and mental health counselors.

Related papers

• Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews [51.453135368388686]
  We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM) Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level.
  arXiv  Detail & Related papers  (2024-03-11T21:51:39Z)
• 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)
• Exploring Social Media for Early Detection of Depression in COVID-19 Patients [44.76299288962596]
  Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. We managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression.
  arXiv  Detail & Related papers  (2023-02-23T14:13:52Z)
• Disadvantaged students increase their academic performance through collective intelligence exposure in emergency remote learning due to COVID 19 [105.54048699217668]
  During the COVID-19 crisis, educational institutions worldwide shifted from face-to-face instruction to emergency remote teaching (ERT) modalities. We analyzed data on 7,528 undergraduate students and found that cooperative and consensus dynamics among students in discussion forums positively affect their final GPA. Using natural language processing, we show that first-year students with low academic performance during high school are exposed to more content-intensive posts in discussion forums.
  arXiv  Detail & Related papers  (2022-03-10T20:23:38Z)
• Exploring COVID-19 Related Stressors Using Topic Modeling [0.0]
  This study aims to apply natural language processing (NLP) on social media data to identify psychosocial stressors during COVID-19 pandemic. We obtained dataset of 9266 Reddit posts from subreddit rCOVID19_support, from 14th Feb. 2020 to 19th July 2021. Our result presented a dashboard to visualize the trend of prevalence of topics about covid-19 related stressors being discussed on social media platform.
  arXiv  Detail & Related papers  (2022-01-12T20:22:43Z)
• Examining Older Adults' Information Exposure, Wellbeing, and Adherence to Protective Measures During the COVID-19 Pandemic [9.630865346003752]
  Older adults are at greater risk of experiencing negative physical and psychological impacts of the novel coronavirus 2019 (COVID-19) pandemic. This work investigates the potential association between information exposure and wellbeing as well as adherence to COVID-19 protective measures.
  arXiv  Detail & Related papers  (2021-12-17T15:33:13Z)
• Meta-research on COVID-19: An overview of the early trends [0.22843885788439797]
  pandemic has underlined the severity of known challenges in research and surfaced new ones. This review considers early trends emerging from meta-research on COVID-19.
  arXiv  Detail & Related papers  (2021-06-05T20:50:43Z)
• Are Top School Students More Critical of Their Professors? Mining Comments on RateMyProfessor.com [83.2634062100579]
  Student reviews and comments on RateMyProfessor.com reflect realistic learning experiences of students. Our study proves that student reviews and comments contain crucial information and can serve as essential references for enrollment in courses and universities.
  arXiv  Detail & Related papers  (2021-01-23T20:01:36Z)
• 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)
• Influence of COVID-19 confinement in students performance in higher education [0.0]
  The study used a field experiment of 458 students in Universidad Autonoma de Madrid (Spain) The results show that there is a significant positive effect of the COVID-19 confinement on students performance. The effect is also significative in activities that did not change their format when performed after confinement.
  arXiv  Detail & Related papers  (2020-04-20T18:06:15Z)
• A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow [69.5231754305538]
  Study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining. Findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data.
  arXiv  Detail & Related papers  (2020-04-18T08:19:46Z)

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.