Detection of university student stress based on deep neural networks, in the covid-19 pandemic

Patilla H.J.; Patricia D.L.C.O.K. · RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao (2023)

Resumen

In March 2020, the Peruvian government declared quarantine due to the increase in COVID-19 infections, to carry out educational activities again, virtual classes were implemented; This situation caused changes in the routines of university students who, adapting to virtual teaching in a context of social isolation, were affected. Analyzing this context, it was observed that at the university level in most universities there is no proper monitoring of student mental health. The main objective of this research is to use deep neural networks and with the help of these, determine university student stress, in the covid-19 pandemic, in Peru. The research data is based on the survey carried out on various undergraduate students during the last semester of 2021.

Instituciones: Universidad Nacional de San Cristóbal de Huamanga