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

arXiv: https://arxiv.org/abs/2202.07441 · HTML: https://mlsanity.com/paper/2202.07441/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 105 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Fully Connected Deep Structured Networks](https://mlsanity.com/paper/1503.02351/) (2015)
- [On Solving L-SR1 Trust-Region Subproblems](https://mlsanity.com/paper/1506.07222/) (2015)
- [A Machine Learning Analysis of COVID-19 Mental Health Data](https://mlsanity.com/paper/2112.00227/) (2021)
- [A New Multipoint Symmetric Secant Method with a Dense Initial Matrix](https://mlsanity.com/paper/2107.06321/) (2021)
- [A machine learning analysis of the relationship between some underlying medical conditions and COVID-19 susceptibility](https://mlsanity.com/paper/2112.12901/) (2021)

## Built on by (most cited first, 1 of 1)

- [Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries](https://mlsanity.com/paper/2309.15990/) (2023)

## Abstract (arXiv)

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.