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

Yijun Zhao, Yi Ding, Yangqian Shen, Samuel Failing and Jacqueline Hwang
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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

IJERPH, 2022, vol. 19, issue 4, 1-16

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.

Keywords: machine learning; association rule mining; COVID-19; coping patterns; university students (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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