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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0279711 |
| _version_ | 1797944038061506560 |
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| author | Yijun Zhao Yi Ding Hayet Chekired Ying Wu |
| author_facet | Yijun Zhao Yi Ding Hayet Chekired Ying Wu |
| author_sort | Yijun Zhao |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-10T20:33:26Z |
| format | Article |
| id | doaj.art-f73ee1cfd8e24f86b8f7d90402599911 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-10T20:33:26Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f73ee1cfd8e24f86b8f7d904025999112023-01-25T05:32:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011712e027971110.1371/journal.pone.0279711Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach.Yijun ZhaoYi DingHayet ChekiredYing WuThe 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.https://doi.org/10.1371/journal.pone.0279711 |
| spellingShingle | Yijun Zhao Yi Ding Hayet Chekired Ying Wu Student adaptation to college and coping in relation to adjustment during COVID-19: A machine learning approach. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0279711 |
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