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Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach

The COVID-19 pandemic has presented unprecedented challenges for university students, creating uncertainties for their academic careers, social lives, and mental health. Our study utilized a machine learning approach to examine the degree to which students’ college adjustment and coping styles impac...

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Detalles Bibliográficos
Autores principales: Zhao, Yijun, Ding, Yi, Chekired, Hayet, Wu, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9803197/
https://www.ncbi.nlm.nih.gov/pubmed/36584087
http://dx.doi.org/10.1371/journal.pone.0279711
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author Zhao, Yijun
Ding, Yi
Chekired, Hayet
Wu, Ying
author_facet Zhao, Yijun
Ding, Yi
Chekired, Hayet
Wu, Ying
author_sort Zhao, Yijun
collection PubMed
description The COVID-19 pandemic has presented unprecedented challenges for university students, creating uncertainties for their academic careers, social lives, and mental health. Our study utilized a machine learning approach to examine the degree to which students’ college adjustment and coping styles impacted their adjustment to COVID-19 disruptions. More specifically, we developed predictive models to distinguish between well-adjusted and not well-adjusted students in each of five psychological domains: academic adjustment, emotionality adjustment, social support adjustment, general COVID-19 regulations response, and discriminatory impact. The predictive features used for these models are students’ individual characteristics in three psychological domains, i.e., Ways of Coping (WAYS), Adaptation to College (SACQ), and Perceived Stress Scale (PSS), assessed using established commercial and open-access questionnaires. We based our study on a proprietary survey dataset collected from 517 U.S. students during the initial peak of the pandemic. Our models achieved an average of 0.91 AUC score over the five domains. Using the SHAP method, we further identified the most relevant risk factors associated with each classification task. The findings reveal the relationship of students’ general adaptation to college and coping in relation to their adjustment during COVID-19. Our results could help universities identify systemic and individualized strategies to support their students in coping with stress and to facilitate students’ college adjustment in this era of challenges and uncertainties.
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spelling pubmed-98031972022-12-31 Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach Zhao, Yijun Ding, Yi Chekired, Hayet Wu, Ying PLoS One Research Article The COVID-19 pandemic has presented unprecedented challenges for university students, creating uncertainties for their academic careers, social lives, and mental health. Our study utilized a machine learning approach to examine the degree to which students’ college adjustment and coping styles impacted their adjustment to COVID-19 disruptions. More specifically, we developed predictive models to distinguish between well-adjusted and not well-adjusted students in each of five psychological domains: academic adjustment, emotionality adjustment, social support adjustment, general COVID-19 regulations response, and discriminatory impact. The predictive features used for these models are students’ individual characteristics in three psychological domains, i.e., Ways of Coping (WAYS), Adaptation to College (SACQ), and Perceived Stress Scale (PSS), assessed using established commercial and open-access questionnaires. We based our study on a proprietary survey dataset collected from 517 U.S. students during the initial peak of the pandemic. Our models achieved an average of 0.91 AUC score over the five domains. Using the SHAP method, we further identified the most relevant risk factors associated with each classification task. The findings reveal the relationship of students’ general adaptation to college and coping in relation to their adjustment during COVID-19. Our results could help universities identify systemic and individualized strategies to support their students in coping with stress and to facilitate students’ college adjustment in this era of challenges and uncertainties. Public Library of Science 2022-12-30 /pmc/articles/PMC9803197/ /pubmed/36584087 http://dx.doi.org/10.1371/journal.pone.0279711 Text en © 2022 Zhao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhao, Yijun
Ding, Yi
Chekired, Hayet
Wu, Ying
Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach
title Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach
title_full Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach
title_fullStr Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach
title_full_unstemmed Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach
title_short Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach
title_sort student adaptation to college and coping in relation to adjustment during covid-19: a machine learning approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9803197/
https://www.ncbi.nlm.nih.gov/pubmed/36584087
http://dx.doi.org/10.1371/journal.pone.0279711
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