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

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  • Yijun Zhao

    (Computer and Information Sciences Department, Fordham University, New York, NY 10023, USA)

  • Yi Ding

    (Graduate School of Education, Fordham University, New York, NY 10023, USA)

  • Yangqian Shen

    (Graduate School of Education, Fordham University, New York, NY 10023, USA)

  • Samuel Failing

    (Computer and Information Sciences Department, Fordham University, New York, NY 10023, USA)

  • Jacqueline Hwang

    (Graduate School of Education, Fordham University, New York, NY 10023, USA)

Abstract

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.

Suggested Citation

  • Yijun Zhao & Yi Ding & Yangqian Shen & Samuel Failing & Jacqueline Hwang, 2022. "Different Coping Patterns among US Graduate and Undergraduate Students during COVID-19 Pandemic: A Machine Learning Approach," IJERPH, MDPI, vol. 19(4), pages 1-16, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2430-:d:753700
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    References listed on IDEAS

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    1. Wei Hong & Ru-De Liu & Yi Ding & Jacqueline Hwang & Jia Wang & Yi Yang, 2021. "Cross-Country Differences in Stay-at-Home Behaviors during Peaks in the COVID-19 Pandemic in China and the United States: The Roles of Health Beliefs and Behavioral Intention," IJERPH, MDPI, vol. 18(4), pages 1-14, February.
    2. repec:cdl:cshedu:qt80k5d5hw is not listed on IDEAS
    3. Asanov, Igor & Flores, Francisco & McKenzie, David & Mensmann, Mona & Schulte, Mathis, 2021. "Remote-learning, time-use, and mental health of Ecuadorian high-school students during the COVID-19 quarantine," World Development, Elsevier, vol. 138(C).
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    Cited by:

    1. Rimsha Asad & Saud Altaf & Shafiq Ahmad & Haitham Mahmoud & Shamsul Huda & Sofia Iqbal, 2023. "Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan," Sustainability, MDPI, vol. 15(6), pages 1-24, March.
    2. Chiara Buizza & Clarissa Ferrari & Giulio Sbravati & Jessica Dagani & Herald Cela & Giuseppe Rainieri & Alberto Ghilardi, 2023. "Impact of COVID-19 Pandemic on Well-Being, Social Relationships and Academic Performance in a Sample of University Freshmen: A Propensity Score Match Evaluation Pre- and Post-Pandemic," IJERPH, MDPI, vol. 20(15), pages 1-19, July.

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