Using machine learning to analyze mental health in distance education during the COVID-19 pandemic: an opinion study from university students in Mexico

Abstract

In times of lockdown due to the COVID-19 pandemic, it has been detected that some students are unable to dedicate enough time to their education. They present signs of frustration and even apathy towards dropping out of school. In addition, feelings of fear, anxiety, desperation, and depression are now present because society has not yet been able to adapt to the new way of living. Therefore, this article analyzes the feelings that university students of the Instituto Tecnológico Superior de Misantla present when using long distance education tools during COVID-19 pandemic in Mexico. The results suggest that isolation, because of the pandemic situation, generated high levels of anxiety and depression. Moreover, there are connections between feelings generated by lockdown and school performance while using e-learning platforms. The findings of this research reflect the students’ feelings, useful information that could lead to the development and implementation of pedagogical strategies that allow improving the students’ academic performance results.

Keywords

• Machine learning
• Factor analysis
• Mental health
• Distance education
• COVID-19 pandemic