A Combined Model for Predicting Engineering Identity in Undergraduate Students

Anita Patrick; Maura J. Borrego; Carolyn Conner Seepersad

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Conference: 2018 ASEE Annual Conference & Exposition
Location: Salt Lake City, Utah
Publication Date: June 23, 2018
Start Date: June 23, 2018
End Date: July 27, 2018
Conference Session: Motivation, Identity, and Belongingness
Tagged Division: Educational Research and Methods
Tagged Topic: Diversity
Page Count: 14
DOI: 10.18260/1-2--29660
Permanent URL: https://peer.asee.org/29660
Download Count: 860

Paper Authors

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Anita Patrick University of Texas, Austin

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Anita Patrick is a STEM Education doctoral student and Graduate Research Assistant in the Department of Mechanical Engineering and College of Liberal Arts at UT Austin's Population Research Center. She received her BS in Bioengineering from Clemson University where she tutored undergraduate mathematics and science courses, and mentored undergraduate engineering majors. Prior to coming to UT, she independently tutored K12 and undergraduate mathematics and science. Her research interests include engineering education, identity and equity. Address: Engineering Training Center II (ETC) 204 East Dean Keeton Street Austin, TX 78712 Email: apatrick@utexas.edu

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Maura J. Borrego University of Texas, Austin

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Maura Borrego is Professor of Mechanical Engineering and STEM Education at the University of Texas at Austin. She previously served as a Program Director at the National Science Foundation, on the board of the American Society for Engineering Education, and as an associate dean and director of interdisciplinary graduate programs. Her research awards include U.S. Presidential Early Career Award for Scientists and Engineers (PECASE), a National Science Foundation CAREER award, and two outstanding publication awards from the American Educational Research Association for her journal articles. Dr. Borrego is Deputy Editor for Journal of Engineering Education. All of Dr. Borrego’s degrees are in Materials Science and Engineering. Her M.S. and Ph.D. are from Stanford University, and her B.S. is from University of Wisconsin-Madison.

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Carolyn Conner Seepersad University of Texas, Austin

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Associate Professor of Mechanical Engineering

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Abstract

Several recent studies have focused on measures of student attitudes and beliefs to predict outcomes such as career choice, integration, persistence, and identity in engineering. The body of research on identity in engineering education has converged around a framework based on three factors: performance/competence (i.e., ability or beliefs that one can perform well or understands concepts), interest in the subject matter, and recognition by others (i.e., peers, family members, teachers) as the type of person who can understand/complete the subject matter. Prior studies have shown math and physics identity factors to be predictive of engineering major choice in first-year undergraduates. However, these studies have not included engineering identity factors. The first aim of this paper is to test a combined model for predicting engineering identity. The combined model includes previously established factors of math and physics identity and newly established engineering factors of the same kind. The second aim is to compare this combined model to a model using only engineering factors to investigate the usefulness of these factors as stand-alone predictors of engineering identity. The study draws on data collected from 1202 undergraduate engineering students in three majors across two public institutions in the southwestern United States. Using linear regression, the results show that all three domains (math, physics, and engineering) individually account for a significant proportion of the variance in engineering identity after controlling for student demographic variables. The combined model explained a total of 29.1% of the variance in engineering identity. Of the non-engineering factors, only math performance/competence was a significant predictor. However, all three engineering factors were significant predictors in that model. Comparatively, the stand-alone model using just the engineering factors explained nearly the same proportion of variance in engineering identity as the combined model, 28.9%. These findings indicate that while students’ math and physics beliefs are important to predicting engineering identity, their engineering beliefs provide equivalent explanatory power. Future research would be better informed through an understanding of how these three domain areas contribute to our understanding of identity and other outcomes.

Citation
Format

Patrick, A., & Borrego, M. J., & Seepersad, C. C. (2018, June), A Combined Model for Predicting Engineering Identity in Undergraduate Students Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--29660

TY - CPAPER
AB - Several recent studies have focused on measures of student attitudes and beliefs to predict outcomes such as career choice, integration, persistence, and identity in engineering. The body of research on identity in engineering education has converged around a framework based on three factors: performance/competence (i.e., ability or beliefs that one can perform well or understands concepts), interest in the subject matter, and recognition by others (i.e., peers, family members, teachers) as the type of person who can understand/complete the subject matter. Prior studies have shown math and physics identity factors to be predictive of engineering major choice in first-year undergraduates. However, these studies have not included engineering identity factors. The first aim of this paper is to test a combined model for predicting engineering identity. The combined model includes previously established factors of math and physics identity and newly established engineering factors of the same kind. The second aim is to compare this combined model to a model using only engineering factors to investigate the usefulness of these factors as stand-alone predictors of engineering identity.
The study draws on data collected from 1202 undergraduate engineering students in three majors across two public institutions in the southwestern United States. Using linear regression, the results show that all three domains (math, physics, and engineering) individually account for a significant proportion of the variance in engineering identity after controlling for student demographic variables. The combined model explained a total of 29.1% of the variance in engineering identity. Of the non-engineering factors, only math performance/competence was a significant predictor. However, all three engineering factors were significant predictors in that model. Comparatively, the stand-alone model using just the engineering factors explained nearly the same proportion of variance in engineering identity as the combined model, 28.9%. These findings indicate that while students’ math and physics beliefs are important to predicting engineering identity, their engineering beliefs provide equivalent explanatory power. Future research would be better informed through an understanding of how these three domain areas contribute to our understanding of identity and other outcomes.

AU - Anita Patrick
AU - Maura J. Borrego
AU - Carolyn Conner Seepersad
CY - Salt Lake City, Utah
DA - 2018/06/23
PB - ASEE Conferences
TI - A Combined Model for Predicting Engineering Identity in Undergraduate Students
UR - https://peer.asee.org/29660
DO - 10.18260/1-2--29660
ER -