Noncognitive Predictors of Engineering Persistence for C-in-Math Students: Exploring the Generalizability of Lasso Regression
- Conference
- Location
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Minneapolis, MN
- Publication Date
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August 23, 2022
- Start Date
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June 26, 2022
- End Date
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June 29, 2022
- Conference Session
- Page Count
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11
- DOI
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10.18260/1-2--40695
- Permanent URL
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https://strategy.asee.org/40695
- Download Count
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178
Paper Authors
Campbell Bego University of Louisville
Dr. Campbell Bego researches engineering learning and persistence as an Assistant Professor in the Department of Engineering Fundamentals at the University of Louisville's Speed School of Engineering. Prior to entering academia, she obtained a BS in Mechanical Engineering from Columbia University, worked in tunnel ventilation (CFD modeling) at Mott MacDonald and AECOM, and received a Professional Engineering license in the State of New York. She draws on these experiences as well as her MS and PhD in Cognitive Science from the University of Louisville to construct meaningful activities in her first-year engineering course. She aims to improve the number of engineering graduates as well as the quality and diversity of the engineering workforce using evidence-based practices and applied theory in the classroom.
Jeffrey Hieb University of Louisville
Patricia Ralston University of Louisville
Thomas Tretter
Jason Immekus
Jody Zhong University of Louisville
Jody is a doctoral student in the Counseling Psychology department at the University of Louisville College of Education & Human Development. Jody's interests include studying how students from underserved communities navigate academic and professional environments, and the formation of intersectional identities amongst undergraduate and graduate students.
Abstract
In this full-length paper, we present new research on engineering persistence for students who received a C in their first-semester math course. We implemented the least absolute shrinkage and selection operator (“lasso”) method, a regularization technique, to consider the relative importance of several noncognitive variables known to impact engineering persistence. In addition to being a powerful tool for reducing dimensionality, lasso regression avoids model overfitting, a difficulty intrinsic to ordinary least squares linear regression, and therefore can reveal more generalizable results.
Our results indicated that students’ perceived effort cost was the only predictive noncognitive factor in the fall 2019 cohort. Although this differed from our previous findings, both sets of data point towards situated expectancy-value theory as a powerful framework to explore engineering students’ persistence. We also conclude that lasso regression is a useful tool in this domain, and additional studies are needed.
Our results indicated that students’ perceived effort cost was the only predictive noncognitive factor in the fall 2019 cohort. Although this differed from our previous findings, both sets of data point toward situated expectancy-value theory as a powerful framework to explore engineering students’ persistence. We also conclude that lasso regression is a useful tool in this domain, and additional studies are needed.
Bego, C., & Hieb, J., & Ralston, P., & Tretter, T., & Immekus, J., & Zhong, J. (2022, August), Noncognitive Predictors of Engineering Persistence for C-in-Math Students: Exploring the Generalizability of Lasso Regression Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40695
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