A quantitative study of intended post-graduation plans of undergraduate biomedical engineering students : assessing self-efficacy, value, and identity beliefs

There has been a consistent call to action to attract talented individuals to help bolster the STEM workforce. Yet, the lack of diversity of students attracted to STEM and the inability to retain them in the profession persists. Among STEM fields, engineering is a prime discipline for examining this challenge. However, treating engineering as one monolithic profession is both inaccurate and misleading as there are over 28 accredited engineering programs in the United States alone with varying levels of diversity based on student demographics. Nonetheless, engineering programs remain male-dominated; however, biomedical engineering (BME) is one such discipline with nearly equal proportions of men and women. BME is a unique case in which to study the intended post-graduation plans of undergraduate engineering students as degree holders have been cited to go on to work in a variety of careers in and outside of the engineering workforce. My aim in this dissertation is to address gaps in the biomedical engineering/engineering education literature on undergraduate women’s intended career choices and related implications. In doing so I problematize the binary and often deficit view of “stay or leave” as related to persistence in engineering and instead further contextualize choice by capturing the potentialities of students’ intended post-graduation plans. Drawing from Eccles’ Expectancy Value Theory and models of STEM Identity in engineering education, I investigate this issue. Using quantitative research methodologies, I explore the structural relationships between student gender and the motivational engineering attitudes of academic self-efficacy, interest, utility value, attainment value, and professional identity. Data was gathered from n=716 undergraduate biomedical engineering students from a large public research institution in the Southwestern United States. Using hierarchical agglomerative cluster analysis, the results revealed students form five clusters of intended post-graduation plans: Engineering, Job, Non-engineering, All, and School. I further examined the composition of these clusters by student gender and classification; gender differences in engineering attitudes between clusters; and gender differences in engineering attitudes within clusters followed by structural equation models to assess the fit of gender and engineering attitudes as related to cluster membership. Implications and areas of future research are discussed.