利用机器学习分析新冠疫情期间远程教育中的心理健康状况:一项来自墨西哥大学生的观点研究

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

作者信息

Melendez-Armenta Roberto Angel, Luna Chontal Giovanni, Garcia Aburto Sandra Guadalupe

机构信息

División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/Instituto Tecnológico Superior de Misantla, Misantla, Veracruz, Mexico.

出版信息

PeerJ Comput Sci. 2024 Aug 8;10:e2241. doi: 10.7717/peerj-cs.2241. eCollection 2024.

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.

摘要

在因新冠疫情而实施封锁期间,人们发现一些学生无法投入足够的时间用于学习。他们表现出沮丧的迹象,甚至对辍学表现出冷漠。此外,由于社会尚未能够适应新的生活方式,恐惧、焦虑、绝望和抑郁情绪也随之出现。因此,本文分析了墨西哥米桑特拉高等技术学院的大学生在新冠疫情期间使用远程教育工具时的感受。结果表明,由于疫情形势导致的隔离产生了高度的焦虑和抑郁。此外,封锁产生的情绪与使用电子学习平台时的学业成绩之间存在关联。本研究的结果反映了学生的感受,这些有用信息可能会促使制定和实施教学策略,以提高学生的学业成绩。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a3a/11323079/bc6d8dce73cc/peerj-cs-10-2241-g001.jpg

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