Capturing attrition decisions in engineering graduate students using longitudinal SMS data
- Conference
- Location
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Baltimore , Maryland
- Publication Date
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June 25, 2023
- Start Date
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June 25, 2023
- End Date
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June 28, 2023
- Conference Session
- Tagged Division
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Educational Research and Methods Division (ERM)
- Tagged Topic
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Diversity
- Page Count
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18
- DOI
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10.18260/1-2--43116
- Permanent URL
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https://strategy.asee.org/43116
- Download Count
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127
Paper Authors
Kyeonghun Jwa Pennsylvania State University
Kyeonghun Jwa is a Ph.D. candidate in the Department of Mechanical Engineering at The Pennsylvania State University. His research uses mixed methods techniques to investigate doctoral engineering attrition and to investigate international students’ academic literacy and adjustment experiences in the U.S. He earned his Bachelor’s degree and Master’s degree in Mechanical & Automotive Engineering from the University of Ulsan in South Korea. In his Master’s work, he investigated autoignition characteristics for alternative fuels. Prior to attending Penn State, He served as a visiting scholar of Engine Research Center at the University of Wisconsin-Madison to study the effect of various injection methods on gasoline compression ignition combustion.
Catherine G. P. Berdanier
Pennsylvania State University
orcid.org/0000-0003-3271-4836
Catherine G.P. Berdanier is an Assistant Professor in the Department of Mechanical Engineering at Pennsylvania State University. She earned her B.S. in Chemistry from The University of South Dakota, her M.S. in Aeronautical and Astronautical Engineering and her PhD in Engineering Education from Purdue University. Her research expertise lies in characterizing graduate-level attrition, persistence, and career trajectories; engineering writing and communication; and methodological development.
Abstract
This research paper reports results from a longitudinal SMS text message survey study that captured attrition decisions from engineering graduate students who decided to leave their Ph.D. program or change degree objectives from Ph.D. to MS (Master’s-level departure). While past research has investigated doctoral attrition across disciplines to identify various factors that influence students’ ideas of leaving (e.g., advisor, funding, lack of well-being), departure is often the result of a series of negative experiences that impact students over time, making it difficult to capture in retrospective interview-based studies. To overcome this issue, we captured the experiences of N = 142 current engineering Ph.D. students across the US over the course of a year, collecting data three times per week using SMS text message survey methods. After the first year of the study, we captured doctoral departure in a subset of our participants who decided to leave their Ph.D. programs while enrolled in our study. This study is the first to capture and show attrition decisions in action. It combines real-time understandings of stress and participants' decisions to depart. The results are transformative in gaining insight for the monitoring and understanding attrition in higher education.
Jwa, K., & Berdanier, C. G. P. (2023, June), Capturing attrition decisions in engineering graduate students using longitudinal SMS data Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43116
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