Journal
JOURNAL OF ENGINEERING EDUCATION
Volume 105, Issue 2, Pages 312-340Publisher
AMER SOC ENGINEERING EDUCATION
DOI: 10.1002/jee.20118
Keywords
critical engineering agency; identity; career choice; gender; structural equation modeling
Categories
Funding
- U.S. National Science Foundation [1036617]
- Graduate Research Fellowship
- Direct For Education and Human Resources
- Division Of Human Resource Development [1036617] Funding Source: National Science Foundation
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Background Prior to college, many students have no experience with engineering, but some ultimately choose an engineering career. Women choose engineering at lower rates than men. This article uses critical engineering agency (CEA) to understand first-year students' attitudes and self-beliefs to predict the choice of an engineering career. Purpose/Hypothesis We investigated how first-year students' math and physics identities and students' beliefs about the ability of science to improve the world predict choice of engineering as a career and whether these beliefs differ by gender. Design/Method The data were from the Sustainability and Gender in Engineering survey distributed during fall 2011 (N = 6,772). Structural equation modeling was used to understand first-year students' affective beliefs for predicting engineering career choice. Results Math and physics identities are important for predicting engineering choice at the beginning of college. Recognition from others and interest in a subject are positive predictors of physics and math identities. Students' performance/competence beliefs alone are negative predictors of engineering career choice but are mediated by interest and recognition from others. Student identities and agency beliefs are significant predictors of engineering career choice, explaining 20% of the variance. We also found gender differences in students' math and physics identities and agency beliefs. Conclusions This article emphasizes the importance of students' recognition beliefs and the importance of agency beliefs for women in predicting engineering career choice.
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