Empowering educational policy management: understanding student emotions and sentiments

Abstract

Purpose

Using sentiment analysis (SA), this study aims to examine the impact of COVID-19 on mental health and virtual learning experiences among 1,125 students at a public Argentinean faculty.

Design/methodology/approach

A study was conducted during the COVID-19 pandemic, surveying 1,125 students to gather their opinions. The survey data was analysed using text mining tools and SA. SA was used to extract the students’ emotions, views and feelings computationally and identify co-occurrences and patterns in related words. The study also examines educational policies implemented after the pandemic.

Findings

The prevalent emotions expressed in the comments were trust, sadness, anticipation and fear. A combination of trust and fear resulted in submission. Negative comments often included the words “virtual”, “virtual classroom”, “virtual classes” and “professor”. Two significant issues were identified: teachers’ inexperience with virtual classes and inadequate server infrastructure, leading to frequent crashes. The most effective educational policies addressed vital issues related to the “virtual classroom”.

Practical implications

Text mining and SA are valuable tools for decision-making during uncertain times, such as the COVID-19 pandemic. They can also provide insights to recover quality assurance processes at universities impacted by health concerns or external shocks.

Originality/value

The paper makes two main contributions: it conducts a SA to gain insights from comments and analyses the relationship between emotions and sentiments to identify optimal educational policies. The study pioneers exploring the link between emotions, policies and the pandemic at a public university in Argentina. This area of research still needs to be explored.

Citation

Dip, J.A. (2024), "Empowering educational policy management: understanding student emotions and sentiments", Quality Assurance in Education, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/QAE-08-2023-0143