Information Seeking and Communication among International Students on Reddit
- URL: http://arxiv.org/abs/2407.06506v1
- Date: Tue, 9 Jul 2024 02:24:32 GMT
- Title: Information Seeking and Communication among International Students on Reddit
- Authors: Chaeeun Han, Sangpil Youm, Sou Hyun Jang,
- Abstract summary: This study examines the impact of the COVID-19 pandemic on information-seeking behaviors among international students.
Our study indicates a considerable rise in the number of users posting more than one question during the pandemic.
Those asking recurring questions demonstrate more active involvement in communication, suggesting a continuous pursuit of knowledge.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study examines the impact of the COVID-19 pandemic on information-seeking behaviors among international students, with a focus on the r/f1visa subreddit. Our study indicates a considerable rise in the number of users posting more than one question during the pandemic. Those asking recurring questions demonstrate more active involvement in communication, suggesting a continuous pursuit of knowledge. Furthermore, the thematic focus has shifted from questions about jobs before COVID-19 to concerns about finances, school preparations, and taxes during COVID-19. These findings carry implications for support policymaking, highlighting the importance of delivering timely and relevant information to meet the evolving needs of international students. To enhance international students' understanding and navigation of this dynamic environment, future research in this field is necessary.
Related papers
- How to Engage Your Readers? Generating Guiding Questions to Promote Active Reading [60.19226384241482]
We introduce GuidingQ, a dataset of 10K in-text questions from textbooks and scientific articles.
We explore various approaches to generate such questions using language models.
We conduct a human study to understand the implication of such questions on reading comprehension.
arXiv Detail & Related papers (2024-07-19T13:42:56Z) - Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries [91.70689724416698]
We present NatQuest, a collection of 13,500 naturally occurring questions from three diverse sources.
Our analysis reveals a significant presence of causal questions (up to 42%) within the dataset.
arXiv Detail & Related papers (2024-05-30T17:55:28Z) - EIT: Earnest Insight Toolkit for Evaluating Students' Earnestness in
Interactive Lecture Participation Exercises [2.6794462297854627]
Earnest Insight Toolkit (EIT) is a tool designed to assess students' engagement within interactive lecture participation exercises.
Our objective is to equip educators with valuable means of identifying at-risk students for enhancing intervention and support strategies.
arXiv Detail & Related papers (2023-10-31T07:05:00Z) - A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual
Learning [76.47138162283714]
Forgetting refers to the loss or deterioration of previously acquired information or knowledge.
Forgetting is a prevalent phenomenon observed in various other research domains within deep learning.
Survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases.
arXiv Detail & Related papers (2023-07-16T16:27:58Z) - Tracking the State and Behavior of People in Response to COVID-1 19
Through the Fusion of Multiple Longitudinal Data Streams [2.477349483168562]
We describe a rich panel dataset of active and passive data from U.S. residents collected between August 2020 and July 2021.
Such a dataset allows important research questions to be answered; for example, to determine the factors underlying the heterogeneous behavioral responses to COVID-19 restrictions imposed by local governments.
arXiv Detail & Related papers (2022-09-23T18:49:23Z) - 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) - A Dataset of Information-Seeking Questions and Answers Anchored in
Research Papers [66.11048565324468]
We present a dataset of 5,049 questions over 1,585 Natural Language Processing papers.
Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text.
We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers.
arXiv Detail & Related papers (2021-05-07T00:12:34Z) - The COVID19 infodemic. The role and place of academics in science
communication [1.2691047660244335]
Academics and scientists have a key role to play in the solutions to the infodemic challenge.
This paper outlines the key advantages to be had from greater engagement with COVID19 discussions.
arXiv Detail & Related papers (2020-11-17T17:30:10Z) - Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic
with Natural Language Processing (NLP) [8.281080540533559]
Natural language processing can be applied to address many of the information needs made urgent by the COVID-19 pandemic.
This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic.
arXiv Detail & Related papers (2020-10-09T22:10:43Z) - Challenges in Combating COVID-19 Infodemic -- Data, Tools, and Ethics [36.203933386216534]
We present three key challenges in this fight against the COVID-19 infodemic where researchers and practitioners instinctively want to contribute and help.
We demonstrate that these three challenges can and will be effectively addressed by collective wisdom, crowdsourcing, and collaborative research.
arXiv Detail & Related papers (2020-05-27T22:41:02Z) - 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.