Student Choice and Persistence in Aerospace Engineering

Abstract

This longitudinal multi-institution study examines student outcomes and demographics in aerospace engineering in the United States over the period of 1987 to 2010. This large sample allows adoption of an intersectional framework to study race/ethnicity and gender together. In this paper, the demographics of students who choose aerospace engineering, their six-year graduation rates, trajectories of students entering and leaving aerospace engineering, and the "stickiness" of the discipline are examined. Hispanic men and women starting in engineering choose aerospace engineering at the highest rates (13.3 and 12.0%, respectively). Aerospace engineering graduation rates lag other disciplines, at best, by nine percentage points among Hispanic females and, at worst, by 24 percentage points among Black females. Retention in aerospace engineering is low for all students, but it is particularly so for Black men and women (both less than 12%). The result is an average of one Black woman graduate per program every 12.5 years. Asian women also have abnormally low persistence rates in aerospace engineering compared with other engineering disciplines (18.8 versus 40.9%). Students who start in aerospace engineering are 1.7 times more likely to leave the institution than to earn an aerospace engineering degree in six years. Recommendations for improving student retention include implementing programs to build community, as well as mentoring and encouragement.

Beyond pipeline and pathways: Ecosystem metrics

“…Although more recent data are available in MIDFIELD, the most recent data used in this study are from 2011. The specific dataset used in this study was frozen in 2011 to permit a series of single‐discipline studies (Brawner et al, ; Brawner, Lord, et al, ; Lord et al, ; Lord, Layton, & Ohland, ; Lord, Ohland, & Layton, ; Ohland et al, ; Orr, Lord, Layton, & Ohland, ; Orr, Ramirez, Lord, Layton, & Ohland, ; Pilotte et al, ). Thus, using a consistent dataset makes it possible to consider those studies in relation to this multidisciplinary exploration.…”

“…While the complete MIDFIELD dataset is large and growing, frequently only a subset of it is used for a particular study. The data for this study were the same dataset used in a large number of other published studies that included only students ever enrolled in engineering and only data elements that have one value per student (Brawner et al, ; Brawner, Lord, et al, 2015; Lord et al, ; Lord, Layton, & Ohland, ; Lord, Ohland, & Layton, ; Orr et al, ; Orr et al, ; Ohland et al, ; Pilotte et al, ). That is, term‐by‐term data such as term GPA, courses taken, and other fields were omitted.…”

Using random forest analysis to identify student demographic and high school‐level factors that predict college engineering major choice

“…Most studies that have investigated engineering major choice have done so at the postsecondary level (e.g., Griffith & Main, 2019; Lord et al, 2019; Main et al, 2018; Main, Xu, et al, 2017). Other studies focused on engineering major choice and persistence have used data from the Multiple‐Institution Database for Investigating Engineering Longitudinal Data (2020); for example, see Lord et al (2019), Main et al (2015), Orr et al (2014, 2015), and Pilotte et al (2017). Orr et al (2015), for example, focused on major choice and persistence in aerospace engineering by gender and race/ethnicity, and reported that Black men and women as well as Asian women have particularly low persistence rates in engineering.…”

Board 95: Expanding Access to and Participation in MIDFIELD (Year 3)

“…This work has helped to change the conversation in engineering education, by demonstrating that the appearance of a particularly high rate of attrition is actually the result of a higher-than-typical retention rate, but a replacement rate that is much lower than other disciplines [2]. MIDFIELD results have shown that women are as likely as men to persist in engineering, that women follow similar pathways to men if they leave engineering, [3] and that student demographics and outcomes vary by engineering discipline, gender, and race [4,5,6,7,8,9,10,11]. Research with MIDFIELD has also shown that the way persistence is measured can result in a "systematic majority measurement bias" [12] and revealed that students who migrate into engineering disciplines are successful [13].…”