Ethnic Differences in Response to COVID-19: A Study of American-Asian and Non-Asian College Students.

Asian American students have experienced additional physical and emotional hardships associated with the COVID-19 pandemic due to increased xenophobic and anti-Asian discrimination. This study investigates different coping patterns and risk factors affecting Asian and non-Asian college students in response to COVID-19 challenges by studying the differences in their responses within four domains after the onset of the pandemic: academic adjustment, emotional adjustment, social support, and discriminatory impacts related to COVID-19. We first employed a machine learning approach to identify well-adjusted and poorly adjusted students in each of the four domains for the Asian and non-Asian groups, respectively. Next, we applied the SHAP method to study the principal risk factors associated with each classification task and analyzed the differences between the two groups. We based our study on a proprietary survey dataset collected from U.S. college students during the initial peak of the pandemic. Our findings provide insights into the risk factors and their directional impact affecting Asian and non-Asian students' well-being during the pandemic. The results could help universities establish customized strategies to support these two groups of students in this era of uncertainty. Applications for international communities are discussed.

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 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.

Understanding interaction rituals: The impact of interaction ritual chains of the live broadcast on people's wellbeing.

With the global pandemic of COVID-19, it has been striking psychological burdens on individuals. Under this background, more and more people get wellbeing by watching live broadcasts. However, the psychological mechanism behind this phenomenon is still a black box. This study finds that when people watch a live broadcast and interact with anchors and other people, an interaction ritual chain is formed, and emotional energy is generated, thus making people experience and understand the meaning of the live interaction ritual chains. Under the effect of the meaning transfer model, people will generate wellbeing. Specifically, the basic meaning of live interaction (emotional meaning and functional meaning) drives people's generation of wellbeing. The meanings of self-participation, self-display, self-concept, and self-renewal play a role in mediation in enhancing people's wellbeing with the basic meaning of live broadcast interaction.

Gender Difference in Psychological, Cognitive, and Behavioral Patterns Among University Students During COVID-19: A Machine Learning Approach.

The COVID-19 pandemic affects all population segments and is especially detrimental to university students because social interaction is critical for a rewarding campus life and valuable learning experiences. In particular, with the suspension of in-person activities and the adoption of virtual teaching modalities, university students face drastic changes in their physical activities, academic careers, and mental health. Our study applies a machine learning approach to explore the gender differences among U.S. university students in response to the global pandemic. Leveraging a proprietary survey dataset collected from 322 U.S. university students, we employ association rule mining (ARM) techniques to identify and compare psychological, cognitive, and behavioral patterns among male and female participants. To formulate our task under the conventional ARM framework, we model each unique question-answer pair of the survey questionnaire as a market basket item. Consequently, each participant's survey report is analogous to a customer's transaction on a collection of items. Our findings suggest that significant differences exist between the two gender groups in psychological distress and coping strategies. In addition, the two groups exhibit minor differences in cognitive patterns and consistent preventive behaviors. The identified gender differences could help professional institutions to facilitate customized advising or counseling for males and females in periods of unprecedented challenges.

Different Coping Patterns among US Graduate and Undergraduate Students during COVID-19 Pandemic: A Machine Learning Approach.

COVID-19 caused unprecedented disruptions to regular university operations worldwide. Dealing with 100% virtual classrooms and suspension of essential in-person activities resulted in significant stress and anxiety for students coping with isolation, fear, and uncertainties in their academic careers. In this study, we applied a machine learning approach to identify distinct coping patterns between graduate and undergraduate students when facing these challenges. We based our study on a large proprietary dataset collected from 517 students in US professional institutions during an early peak of the pandemic. In particular, we cast our problem under the association rule mining (ARM) framework by introducing a new method to transform survey data into market basket items and customer transactions in which students' behavioral patterns were analogous to customer purchase patterns. Our experimental results suggested that graduate and undergraduate students adopted different ways of coping that could be attributed to their different maturity levels and lifestyles. Our findings can further serve as a focus of attention (FOA) tool to facilitate customized advising or counseling to address the unique challenges associated with each group that may warrant differentiated interventions.