Artificial intelligence-based analytics for impacts of COVID-19 and online learning on college students’ mental health

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 the 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.

Machine learning-based analytics of the impact of the Covid-19 pandemic on alcohol consumption habit changes among United States healthcare workers

“…Using Machine learning, in one of our previous works, we utilized several machine learning models to analyze the impacts the COVID-19 pandemic has had on the mental health of frontline workers in the United States 2 . In another previous work, we used machine learning methods and statistical tests to investigate the relationship between the COVID-19 vaccines and boosters and the total case count for the Coronavirus across multiple states in the USA as well as the relationship between several selected underlying health conditions with COVID-19 35 . Moreover, we have discussed the negative impacts of the Covid-19 pandemic on students' mental health decline 36 .…”

Artificial intelligence in university education: Systematic review

“…The acceptance of ChatGPT arti cial intelligence by university students provides relevant information for faculty to make decisions and redesign their teaching and training; it also has implications for the learning process, research, and educational practice in higher education. (Rezapour & Elmshaeuser, 2022) Universities should focus on providing face-to-face and synchronous classes supported with asynchronous components to facilitate healthy learning for students, as well as working on the nancial aspect of the students to avoid economic stress for them. (Afzaal et al, 2023) The algorithm-based approach (dashboard) provided advance information on students' progress and performance, which allowed planning and monitoring students' studies, providing information on learning achievements, self-regulated learning skills and positively affected students' performance.…”

The Design of College Students’ Mental Health Analysis System Based on Human-Computer Interaction

“…The multidimensional OLAP model revolves around multidimensional data cubes and improves query performance by precomputing and storing aggregated data. The OLAP server provides fast data analysis and query capabilities, supporting user-friendly operations such as data slicing, drilling, and pivoting (Rezapour M & Elmshaeuser S K., 2022).…”