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

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-08-08 DOI:10.7717/peerj-cs.2241
Roberto Angel Meléndez-Armenta, Giovanni Luna Chontal, Sandra Guadalupe Garcia Aburto
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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.
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利用机器学习分析 COVID-19 大流行期间远程教育中的心理健康:墨西哥大学生意见研究
在 COVID-19 大流行导致的封锁期间,发现一些学生无法投入足够的时间学习。他们表现出挫败感,甚至对辍学持冷漠态度。此外,由于社会尚未能适应新的生活方式,恐惧、焦虑、绝望和抑郁的情绪也开始出现。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 COVID-19 大流行期间使用远程教育工具时的感受。研究结果表明,由于大流行病造成的与世隔绝的状况,使学生产生了高度的焦虑和抑郁。此外,在使用电子学习平台时,封锁产生的情绪与学校成绩之间也存在联系。这项研究的结果反映了学生的感受,这些有用的信息有助于制定和实施教学策略,从而提高学生的学习成绩。
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CiteScore
7.20
自引率
4.30%
发文量
567
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