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

TL;DR

This study uses NLP to analyze free-text responses from college students to assess how COVID-19 affected their interests and sentiments, revealing shifts in topics and increased negativity post-pandemic.

Contribution

It introduces an NLP-based method to analyze student-generated texts for understanding COVID-19 impacts on student mental health and interests.

Findings

Interest in Education decreased post COVID-19

Health topics became more prominent after COVID-19

Negative sentiment increased across all topics post COVID-19

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.