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 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.

arXiv comment41 pages, 31 Figures

References in corpus (9)

• Scikit-learn: Machine Learning in Python
• XGBoost: A Scalable Tree Boosting System
• SMOTE: Synthetic Minority Over-sampling Technique
• Deep Learning in Neural Networks: An Overview
• An overview of gradient descent optimization algorithms
• Hyperparameters and Tuning Strategies for Random Forest
• CatBoost: gradient boosting with categorical features support
• CatBoost: unbiased boosting with categorical features
• A Machine Learning Analysis of COVID-19 Mental Health Data