Examining Postsecondary and Post-College Pathways of Engineering Students Who Start at Four-Year Colleges and Universities1
Sylvia Hurtado, University of California, Los Angeles
Bryce E. Hughes, Montana State University
M. Kevin Eagan, University of California, Los Angeles
Robert Paul, University of Washington
Paper Commissioned by the National Academy of Engineering
Increasing the number of individuals formally trained in STEM and expanding the pool of talent in the STEM workforce is a national priority (President’s Council of Advisors on Science and Technology, 2012). Engineers are especially in great demand as they play a critical role in technological innovation and contributing to the competiveness of the US economy (National Academy of Sciences, 2011). It is of national concern then that students initially intending to complete degrees in engineering are not persisting to degree completion in engineering. Indeed only about 50% of the students intending to major in engineering at college entry complete an engineering degree (Wulf & Fisher, 2002). Completion of an engineering degree guarantees neither entry into the engineering workforce nor advanced study in engineering after graduation (Landivar, 2013). For example, Lichtenstein et al. (2009) found only 42% of senior engineering majors “definitely” intended to pursue a career in engineering; another 44% were unsure and 14% were definitely not pursuing an engineering career. Engineering students’ graduate school intentions also diminish in college; Sheppard et al. (2010) found a greater proportion of seniors (31.1%) intended on not going to graduate school compared to freshmen (19.2%).
Meeting the U.S. need for engineering professionals will require both improving the capacity for institutions to produce engineering degrees and strengthening the pipeline from degree completion to entry into the engineering workforce. Further, more attention must be paid to diversifying the engineering workforce with respect to gender and race/ethnicity, as diversity in the engineering workforce increases the ability of the profession to creatively
1 This paper was not subject to NAE editing.
Contact first author: Sylvia Hurtado, Professor, UCLA Graduate School of Education and Information Studies, 3302c Moore Hall, Los Angeles, CA 90095-1521. Email: firstname.lastname@example.org
and swiftly solve 21st century problems. Meeting these objectives necessitates a better understanding of the factors that contribute to or hinder engineering degree completion and entry into graduate school or the engineering workforce. Understanding the educational trajectories and career paths of underrepresented racial minority students is of particular interest, as these groups continue to grow and become a larger share of the national population, yet are still highly underrepresented in engineering professional occupations. The purpose of this paper is to use a variety of national data sources to help understand engineering students’ pathways from initial aspirations upon college entry through degree completion and commencement of an engineering career. We begin by reviewing the literature, understanding that previous literature has had some limitations with regard to constructing a portrait of engineers relative to other career fields.
FACTORS AFFECTING ENGINEERING RETENTION AND GRADUATION
Previous research has identified several factors that affect retention and degree completion among engineering majors. Knowing someone who works in engineering is an important factor in students’ decision to choose engineering as a career, though URM students are less likely than White or Asian students to know an engineer (Trenor, Yu, Waight, Zerda, & Sha, 2008). Students from different racial backgrounds also cite different reasons for choosing engineering; for example, Trenor et al. (2008) found in a study of female engineering students White and Asian participants more often cited individual accomplishment whereas Black participants cited more altruistic reasons and Latinas cited a desire for social mobility. Pre-college academic preparation is one strong predictor of academic success in college; a single-institution study found pre-college academic achievement, in addition to college GPA and academic motivation, positively predicted engineering students’ persistence to the sixth semester (French, Immekus, & Oakes, 2005).
The type of institution a student attends can also affect engineering students’ academic and career trajectories, as differing institutional missions can shape the culture and environment, which then can support or undermine students’ educational goals. For instance, students attending a public technical school will be more likely to pursue careers in engineering than students enrolled at other types of institutions given that public technical schools often have a high proportion of students in STEM while also offering fewer alternative options outside STEM (Lichtenstein et al., 2009). Minority-serving institutions offer very unique, supportive environments that play a crucial role in diversifying the STEM workforce; for instance, Black engineering students attending HBCUs graduate at much higher rates than Black engineering students attending predominantly White institutions (Brown, Morning, & Watkins, 2005).
In college, engineering-related activities are strongly related to intentions to pursue an engineering career after college; involvement in internships or cooperative educational programs positively predicted students’ plans for engineering work (Sheppard et al., 2010). Sheppard et al. (2010) also found students with higher college GPAs were more likely to consider an engineering graduate program. Working collaboratively is associated with higher GPAs; collaboration leads to higher self-efficacy, which boosts students’ confidence in learning course material (Stump, Hilpert, Husman, Chung, & Kim, 2011). Stump et al. (2011) also found student impressions of faculty support also increased their academic confidence.
On the other hand, other factors can “pull” students away from their initial engineering aspirations, suggesting that either students’ interests changed or their aspirations may not have been as strong at the beginning of college as their peers who remained in engineering. For instance, students who had higher levels of interpersonal confidence or who preferred more critical ways of thinking were more likely to consider non-engineering work, as were students who had higher levels of participation in non-engineering activities (Bernold, Spurlin, & Anson, 2007; Sheppard et al., 2010).
GENDER IN ENGINEERING
Engineering is one of the fields with the lowest representation of women, a disparity that shows no signs of shifting. Much of that disparity originates from the low proportion of women who enter college with aspirations to study engineering (Lord et al., 2009). Felder, Felder, Mauney, Hamrin, and Dietz (1995) found, on average,
women entered chemical engineering with academic credentials equal to or better than those of men, but women experienced more of a decrease than men in their academic performance and confidence as they continued through the curriculum. Accordingly, Hughes, Garibay, Hurtado, and Eagan (2013) found although women were more likely than men to complete engineering degrees relative to not completing in six years, men were more likely than women to complete engineering degrees relative to switching into another field. In a separate study, Goodman et al. (2002) found roughly 45% of women who left engineering had a grade-point average above 3.0 in their engineering courses, suggesting women were leaving for reasons other than academic performance. Women in Goodman et al.’s study were most vulnerable to leaving the major during their first and second years; half cited dissatisfaction with grades, teaching style, academic workload, or pace of instruction. One-third of participants pointed to negative aspects of the engineering program’s climate, like competition, lack of support and discouragement from faculty and peers. On the other hand, involvement in social enrichment activities, especially study groups, led to more positive perceptions of the department and classroom environments which in turn were related to persistence in the major (Goodman et al., 2002). Tate and Linn (2005) also found women who participated in tutoring and study groups were more likely to persist in engineering. Other studies have found attending office hours, meeting with teaching assistants, and receiving mentoring positively affect women’s persistence in engineering (Tate & Linn, 2005), and that women engineering students use collaboration strategies more frequently than men (Stump et al., 2011).
Of those women who do persist in engineering, Goodman et al. (2002) found 80% of fourth- and fifth-year students expected to be working in the engineering field in seven years. As for graduate school aspirations, Felder et al. (1995) found in a longitudinal study of a cohort of chemical engineering students that 54% of men intended to go to graduate school but only 18% of women intended to do so; however, this discrepancy is likely isolated to this particular cohort. On the other hand, Sax (2001) found among students earning bachelors’ degrees in engineering, a majority of students chose engineering for their graduate field of study (54.2% of women, 57.2% of men). Other graduate fields selected by engineering undergraduates included business, medicine, math/computer science, and law.
Much of the extant literature is limited due to small sample sizes, especially from single classrooms or single institutions, the inclusion of only one cohort of students (primarily in cross-sectional studies), and assessing intentions or attitudes as opposed to behaviors (i.e., intentions to pursue graduate study versus enrolling in graduate school). We aim to address some of these limitations in order to provide a comprehensive overview of engineering pathways using longitudinal and trend data as well as data on those who entered careers in industry and academia. We address these key questions:
- Who is entering four-year institutions with aspirations to pursue an engineering career? How do these trends compare across gender and ethnicity?
- Which college experiences affect engineering retention? Which experiences contribute to students’ sense of academic or social self-concept?
- How do engineering degree completion rates differ by gender or ethnicity? How does degree completion among engineering aspirants compare to aspirants in other STEM fields?
- What educational and career pathways are pursued by engineering degree completers? How does the type of institution attended affect these pathways?
Data from the Higher Education Research Institute (HERI) helps to correct for some of the aforementioned limitations in that we collected data at multiple time points from hundreds of institutions and took care to sample diverse students (including giving special attention to underrepresented groups) attending a variety of institutions in pursuit of STEM degrees. This sampling strategy allowed us to understand diverse students’ pathways and comparisons with students entering (and exiting) engineering and other fields.
Trend data. Analyses for this study relied upon several national data sources. All of the trend analyses and analyses focused on the characteristics of students who intend to pursue engineering, other STEM, and non-STEM majors at college entry examined nationally weighted data collected from the Cooperative Institutional Research Program’s (CIRP) annual Freshman Survey (TFS). Each year CIRP’s TFS surveys hundreds of thousands of first-time, full-time entering freshmen at four-year colleges and universities nationwide. NSF relies on data provided by CIRP’s Freshman Survey in its annual Science and Engineering Indicators report. Freshman Survey data are weighted within institution and within institutional type by gender, and the weighted data represent characteristics of the national population of first-time, full-time freshmen in nonprofit four-year colleges and universities in the U.S.
Longitudinal college survey data. Data for analysis of college experiences was collected from a longitudinal dataset matching students who completed the 2004 TFS at college entry and later completed the 2008 CIRP College Senior Survey (CSS) four years after college entry. As fewer students complete the CSS than the TFS, student responses were weighted to account for nonresponse bias to ensure parameter estimates reflected the larger sample of students who completed the TFS. The final longitudinal dataset included 6224 students, of which 979 were students who initially intended to major in engineering.
Degree completion data. To examine degree completion rates, this study analyzed data from 241,801 students who answered the 2004 CIRP TFS and had matched enrollment/completion data from the National Student Clearinghouse (NSC). NSC provided term-to-term enrollment data as well as information about the type of degree (i.e., A.A., B.A., B.S.) students completed as well as the academic discipline or field of that degree. The timeframe for the NSC data ranged from August 2004 through June 2010, which allowed for analyses regarding four-, five-, and six-year degree completion for students who entered a four-year college or university as a first-time, full-time freshman. The TFS-NSC dataset also has been weighted by gender within institution and within institutional type to make this sample of first-time, full-time freshman representative of the national population of first-time, full-time students who entered college in the fall of 2004.
Longitudinal early career survey data. Post-college pathways were analyzed using data from the 2011 Post-Baccalaureate Survey (PBS), administered to students who completed the 2004 TFS. Responses from the PBS were then matched to initial responses on the TFS to produce a longitudinal dataset, and the final longitudinal sample included 13,671 respondents. Similar to the CSS, responses were weighted to account for nonresponse bias and to ensure parameter estimates reflected the initial sample of students who completed the TFS. The survey helped capture post-college outcomes seven years after college entry, and the sample included 1,956 engineering degree holders.
Faculty survey data. Finally, data on engineering faculty were taken from two administrations of the HERI Faculty Survey. CIRP administers a survey of faculty every three years to capture the experiences, attitudes, and teaching activities of faculty in colleges across the nation. For this study, we combined faculty responses from the 2010 and 2014 administrations of the Faculty Survey to ensure adequate representation of engineering faculty in the sample, and none of the participants were included more than once in the sample. Together, 56,122 engineering, other STEM, and non-STEM faculty were included, of which 1,581 were engineering faculty.
The CIRP Freshman Survey includes more than 250 variables representing student characteristics, pre-college experiences, and educational and career goals. To identify characteristics of students intending to pursue STEM majors upon college entry, we primarily relied upon student demographic characteristics, intended major, and pre-college academic preparation. These same student variables and institutional characteristics were merged with STEM completion data to understand differences in STEM completion rates across types of students and institutions. Additionally, both the CSS and the PBS include items pertaining to student experiences in college, and the PBS also asked students about graduate school and labor force experiences.
We primarily relied upon descriptive analyses. Drawing from trend data as well as single-year administrations of the CIRP Freshman Survey, we ran a series of frequencies and crosstabs to understand how the characteristics of students interested in pursuing engineering, other STEM, and non-STEM majors have shifted over time. All of the descriptive analyses of TFS data were weighted such that the findings represent the population of first-time, full-time entering college freshmen in the U.S. We also ran descriptive analysis on CSS, Faculty Survey, NSC, and PBS data to highlight some of the important descriptive differences in four-year, completion, and post-college outcomes among students. In addition to descriptive analysis, we developed regression models to predict changes in self-efficacy over four years, retention in engineering after four years, and post-college pathways after seven years. Continuous outcomes like self-efficacy were analyzed using multiple regression techniques, dichotomous outcomes like retention were analyzed using logistic regression techniques, and categorical outcomes like postcollege pathway were analyzed using multinomial logistic regression techniques.
While the longitudinal assessment of engineering outcomes is extremely useful, several limitations are in order. First, CIRP Freshman Survey data includes students’ major intentions, and these intentions or aspirations may differ from the major students subsequently declare. This limitation is particularly important with respect to engineering, as many institutions require students to seek additional admission to engineering majors beyond admission to the university. Thus, students responding to the CIRP Freshman Survey may have been less sure about their major and based their major intentions on positive experiences they had in certain classes in high school.
As we relied on survey data, a second limitation of our analyses pertains to a typical limitation of survey data. Students completing surveys, especially four and seven years after college entry, may interpret prompts somewhat differently than we intended, and may recall their experiences during college with dissimilar levels of accuracy. In general, survey data is reliable, but how students respond may differ somewhat from their actual experiences and behaviors in college. A third limitation is that the 2010-11 NCS data did not capture students’ term-to-term academic major. NCS is beginning to collect such information now, which will allow for improved accuracy of understanding the mobility and sustained commitment to STEM among students in higher education.
Finally, we are limited by the data included on the surveys. Although the academic major codes are broad (90 different categories), not all majors classified as engineering may be represented. For example, engineering technology is not something addressed as its own option on the instrument, so these students may select “other engineering” or simply chose “other” when reporting their intended major. Additionally, although we have a number of pre-college preparation measures, our list is nowhere near exhaustive.
Characteristics of Students Intending to Major in Engineering
Figure C-1 presents more than 40 years’ worth of data on students’ intentions to major in any engineering field. In 1971, 6.5% of incoming first-time, full-time students intended to major in engineering. The percentage first peaked in 1982 when 11.9% of students indicated they intended to major in engineering, but recently intentions have increased again with 11.2% of students in 2011 indicating an intention to major in engineering. Women’s intentions to major in engineering are at their highest since 1971; whereas in 1971 only a fraction of a percentage of women intended to major in engineering, in 2012 4% of women planned to major in engineering. Although women’s intentions to major in engineering have increased, their aspirations for engineering have consistently remained low. URM intentions to major in engineering have tracked closer to overall intentions, peaking at 12.3% in 1993 but recently increasing from 6.7% in 2007 to 9.8% in 2012.
Figure C-2 selects incoming first-time, full-time students who started college in the fall of 2012 and examines engineering career aspirations across a number of characteristics. Nearly 8% of first-time, full-time freshmen in
2012 planned a career in engineering, but the biggest disparity in these intentions is by sex. More than 15% of incoming first-year college men planned to enter an engineering career whereas only 3.2% of first-year college women indicated the same plans. The differences between students of different racial backgrounds are less discrepant; Asian American students indicated the highest rate of intention to pursue an engineering career (11.6%) and Black students the lowest (6%). All other racial/ethnic groups had engineering career aspirations ranging from 7.7% (multiracial) to 8.7% (White). Although men and women differ immensely in terms of their aspirations to become engineers, differences by race are much smaller.
Demographic Composition of Students Intending to Major in Engineering
Table C-1 provides the demographic breakdown of first-time, full-time students in fall 2012, both in total and disaggregated into engineering major, other STEM major, and non-STEM major aspirations. Not surprisingly, women are more highly represented in non-STEM (56.3%) and other STEM (62.4%) fields than in engineering (20.6%), though women are better represented in other STEM than non-STEM fields. The “other STEM” category contains biological sciences and nursing, which are fields with large numbers of students and are overrepresented by women. Asian American students are most highly represented among engineering students (13%), but White students most highly represented among non-STEM majors (63.2%) followed by engineering majors (60.8%). Latino/a students are slightly more represented among engineering majors (9.1%) than other STEM majors (8.9%), but Black and American Indian students are least represented among engineering majors. These demographics contribute to these underrepresented groups together being least represented among engineering majors (20.5%) than their non-STEM (25.1%) and other STEM peers (24.2%).
Engineering students also appear to come from higher socioeconomic status backgrounds. More engineering aspirants than students who aspire to other majors report their mother earned a bachelor’s degree or higher (61.6%). Similarly, engineering aspirants are twice as likely as students who intend to major in other fields to report that either of their parents works as an engineer (16.5% versus 9.5% for “other STEM” majors and 6.8% for non-STEM majors).
|All students (%)||Non-STEM (%)||Other STEM (%)||Engineering (%)|
|Two or more race/ethnicity||10.18||10.55||9.90||9.43|
|Socioeconomic status||Mother’s level of education: College or more||56.18||55.85||55.30||61.61|
|Either parent employed as engineer||8.69||6.81||9.50||16.51|
Note. All: n=192,912; other STEM: n=60,067; engineering: n=18,128
Source: 2012 CIRP Freshman Survey, Higher Education Research Institute, UCLA
Academic Preparation Prior to College
Table C-2 compares the level of academic preparation of first-time, full-time students in fall 2012 by intended major groups. Engineering students’ average SAT scores were significantly higher than the other groups (F=2153.303; p<0.001), and they were more likely to report higher high school grade point averages than the other groups (χ2=4446.208; p<0.001). Engineering majors also completed more years of high school math and physics than the other groups, though the other groups completed more years of high school biology.
Changes over Four Years of College
Self-concept. Next, we examined how students’ self-reported sense of academic and social self-concept changed from their first year in college (2004) to their fourth year in college (2008). Figure C-3 compares aver-
|All students (%)||Non-STEM (%)||Other STEM (%)||Engineering (%)|
|4 or more years HS math||87.58||85.32||89.60||94.32|
|2 or more years HS physics||61.39||59.26||61.90||71.62|
|2 or more years HS biology||50.64||46.51||60.80||42.15|
|HS GPA: A- or better||49.55||44.55||56.00||62.05|
|Average SAT score||1143||1125||1148||1231|
Note. All: n=192,912; other STEM: n=60,067; engineering: n=18,128. Numbers for average SAT scores represent means rather than percentages.
Source: 2012 CIRP Freshman Survey, Higher Education Research Institute, UCLA.
age scores on the two self-concept constructs measured on HERI surveys among all, non-STEM, other STEM, and engineering students both at college entry and at the fourth year. Table C-3 provides a list of the survey items included within each construct. At college entry and at the fourth year, engineering students report the highest sense of academic self-concept and are significantly stronger on this domain than the other two groups at both college entry and students’ senior year (p<0.001 for each comparison).
In terms of social self-concept, only non-STEM students scored higher than engineering students at college entry (p<0.05); the difference at college entry between engineering and other STEM students was not significant. After four years of college, the difference in social self-concept between engineering and non-STEM students remains nonsignificant whereas the difference between engineering and other STEM students becomes significant (p<0.01). This finding suggests engineering students gain the most in social self-concept among the three groups and that academic self-concept does not appear to shift too dramatically.
We also tested for significant differences in the change in academic and social self-concept from college entry to the fourth year among the three groups. Engineering students’ change in academic self-concept was not significantly different from either non-STEM or other STEM students, but their change in social self-concept was higher and significantly different in comparison to non-STEM (p<0.001) and other STEM (p<0.05) students. It appears engineering students gain the most in terms of social self-concept relative to their peers during college.
In addition to these comparisons, we also tested regression models predicting academic and social self-concept change in students who had been retained in engineering to their fourth year. These models are presented in Appendix A. In order to isolate the relationship between various college experiences and change in self-concept, we included the self-concept scores at college entry as a pretest and also included several pre-college and institutional factors that might contribute to differences in self-concept. The final models demonstrate that several college experiences relate to change in self-concept. The frequency with which students met with a counselor or advisor about one’s career plans correlated with gains in academic self-concept, which may also suggest those students with higher academic self-concept were also more serious about planning for their engineering careers. This relationship was also observed in the social self-concept model, which again could suggest those students with higher social self-concept were more likely to seek out opportunities to discuss their career plans. This interpretation is supported by the finding that higher scores on the CIRP faculty support and mentoring construct also correlated with gains in social self-concept.
Retention in Engineering
In addition to observing changes in self-concept from college entry to the fourth year, we also developed a regression model predicting retention in engineering at the fourth year for all students who intended to major in
|Drive to achieve|
|Public speaking ability|
Note: Item stem is “Compared to the average person your age, how would you rate your: (ability items). Response scale: 1=Lowest 10% to 5=Highest 10%.
engineering at college entry. This model is presented in Appendix B. Since the overall sample is of all students who intended to major in engineering at college entry, retention is defined as also indicating engineering as their major at the fourth year. In this sample, 66.7% of initial aspirants were retained in engineering after four years. The model tests a set of predictors that include background characteristics, pre-college academic preparation, institutional characteristics, and college experiences to identify which significantly predict retention in engineering after four years.
Only one pre-college factor significantly predicted retention in the final model. Students’ average high school GPA positively correlated with staying in the engineering major at their initial college. The proportion of undergraduates who are enrolled in STEM majors significantly and positively correlated with student persistence in engineering majors. This relationship may signal that a higher proportion of STEM majors contributes to a peer normative culture that supports students’ ability to persist in STEM majors. Additionally, this institution-level variable may be serving as a proxy for technical colleges and universities that have very high proportions of students in STEM due to very few non-STEM alternatives.
Two engineering-related activities positively predicted retention in engineering. Participation in an internship program and in a major-related club or organization both positively correlated with retention in engineering. Unfortunately, as we are unable to determine the timing of either experience, we cannot definitively state whether either or both contributed to a students’ increased likelihood to persist as an engineering major. It may be the case that students who stayed in engineering long enough were the ones able to seek out these experiences. Undergraduate research did not significantly predict retention in engineering, but in another model we found research experience predicted choosing an engineering career pathway after college, so we do not want to discount the effect of undergraduate research with this finding. Figures C-4 and C-5 graphically represent the relationship between retention and both internships and club participation.
Two academic support factors also significantly predicted retention in engineering. In fact, the strongest predictor in the model was the frequency that students reported studying together. Students who more frequently studied with their peers were significantly more likely to persist in engineering through their senior year. On the other hand, mentorship from faculty negatively correlated with students’ persistence in engineering, which may suggest that students who left engineering majors were able to receive better mentorship in their new discipline.
Three other college experiences related negatively to retention in engineering. Taking a women’s studies course, spending more time commuting, and hearing faculty express racial stereotypes in class all negatively predicted retention in engineering. In terms of taking a women’s studies course, it also is fairly likely that students who are not retained in engineering are more likely to take a women’s studies course, though their interest in critical topics could be a contributing factor to their decision to change majors. Students who spend more time commuting, however, often commute because they live at home to save money to attend college, and commuting can cut into necessary time for studying and homework. Most alarming, though, is the relationship between hearing stereotypes in class and retention. Nearly twice as many retained students strongly disagree that they have heard faculty express stereotypes than students who were not retained, and more students who were not retained either agree or strongly agree than those who were retained, which suggests negative classroom climates may discourage students from persisting in their engineering majors.
Engineering Faculty Development
As engineering faculty are most directly tasked with the professional preparation of engineering graduates, an important issue in understanding the educational trajectories of engineering students is the preparation and professional development of engineering faculty members in their teaching and learning activities. Figure C-6 displays faculty self-ratings regarding how well they felt graduate school prepared them for the responsibilities of being a faculty member. Engineering faculty rated their graduate preparation the lowest compared to other STEM and non-STEM faculty in terms of preparation to be a faculty member and second lowest in terms of preparation to mentor new faculty colleagues. By contrast, the proportions for all three groups are relatively high.
Figure C-7 displays the differences between faculty in engineering, other STEM, and non-STEM fields in terms of the importance of several teaching goals for undergraduates. Engineering faculty were more likely to rate preparation for post-college employment, preparation for graduate study, mastery of knowledge in a particular
discipline, and the development of creative capacities as important or essential teaching goals for their undergraduate courses relative to their colleagues in other disciplines.
Other evidence suggests engineering faculty may not be as prepared to realize their commitment to these student learning outcomes through their practice in the classroom. Both engineering and other STEM faculty were significantly less likely to report employing student-centered teaching strategies, and these findings are shown in
Figure C-8. The activities included within the overall student-centered pedagogy construct are listed in the figure. In addition, engineering faculty were the least likely to participate in faculty development activities, as shown in Figure C-9. Encouragingly, though, engineering faculty were the most likely to involve undergraduates in research, an important factor contributing to students’ choice of engineering as a career after completing college.
Overall, nearly 16% of engineering aspirants complete an engineering degree in four years, 35% in five years, and 40% in six years. Previous research has also demonstrated that not only do engineering students tend to finish in five years but engineering programs also are shifting toward five-year programs that either allow students to simultaneously complete their bachelor’s degrees with a master’s degree, or require a greater amount of courses than other undergraduate majors. It is no surprise then that the biggest increase in degree completion rates happens between four-year and five-year completion. In addition, 20% of engineering aspirants completed a degree in any STEM field in four years, 44% in five years, and slightly more than half in six years.
Figure C-10 displays both overall engineering degree completion and completion disaggregated by status as an underrepresented racial/ethnic minority. Figure C-11 displays this similar disaggregation for STEM completion, and Figure C-12 disaggregates engineering completion by sex. In general, White and Asian American students complete engineering and STEM degrees at much higher rates than their URM peers, but women enjoy higher completion rates than their male peers. These patterns mirror broader patterns in degree completion in higher education in the United States and point to the persistent disparities in URM students’ pursuit of engineering degrees. In other words, as we established earlier that there are very small differences between URM and White/Asian American students in terms of their aspirations to engineering degrees and careers, those aspirations do not translate into equitable outcomes after even six years of college. On the other hand, although women are severely underrepresented in engineering, women who start in engineering are more likely to earn a degree in engineering relative to their male peers.
In comparison to two other STEM fields, physical sciences and biomedical sciences, students who start in engineering are much more likely to earn a bachelor’s degree in their originally intended field. Figure C-13 displays the various completion outcomes of students who aspired to engineering, physical sciences, and biomedical sciences majors. The proportion of engineering students who completed a non-STEM degree is lowest among the three groups. Engineering aspirants are slightly more likely to drop out (30%) than complete in a non-STEM field (20%), which may be an indication of attending colleges specialized in engineering with fewer options available, as this is distinctly different from students in other STEM fields.
Table C-4 provides more insight into these four academic outcomes of engineering students. Women were most represented among engineering aspirants who completed in other STEM fields and least represented among students who did not complete a degree. URM students, however, were most represented among students who did not complete a degree, and least represented among students who completed an engineering degree. Students whose mothers had higher levels of education, students with higher GPAs, higher SAT scores, and who had taken more years of math and physics in high school were most represented among those who completed engineering degrees. Of those who chose engineering, the students who completed engineering degrees tended to be those students with the highest pre-college academic preparation. In other words, considering that many programs require separate admission into engineering programs, introductory coursework can also serve to exclude students whose pre-college academic preparation was not comparable to peers with college-educated parents and focus advantages associated with high school resources.
Figure C-14 displays the highest planned degrees of engineering degree holders one to three years after completing their engineering degrees (seven years after entering college). A plurality of engineering degree holders aspire to a master’s degree as the highest degree they intend to pursue; more than 40% of engineering degree holders aspire to a master’s degree while slightly more than 30% plan to seek no further education beyond their
|Demographics||Six-year outcome (%)|
|Engineering||Other STEM||Non-STEM||No degree|
|Non-Native English Speaker||10.17||10.5||8.25||11.13|
|Mother has at least a college degree Academic Preparation||64.46||60.7||57.95||45.37|
|High school GPA at least A-||77.58||66.56||58.93||37.36|
|At least 4 years of math in HS||96.10||94.50||93.59||87.00|
|At least 2 years of physics in HS||73.59||70.11||66.16||61.60|
|Average SAT score***||1268||1224||1186||1118|
Note. n=16,298; *** difference from engineering is significant at p<0.001; sources: 2004 Freshman Survey, Cooperative Institutional Research Program, Higher Education Research Institute, UCLA; National Student Clearinghouse, 2010.
bachelor’s degrees. Slightly less than 12% aspire each to a doctoral degree and a business graduate degree, and very small percentages aspire to medical, law, or other terminal degrees. Disaggregated by gender, somewhat higher proportions of women aspire to doctoral and graduate business degrees, but higher proportions of men aspire to master’s degrees, displayed in Figure C-15. When examined by underrepresented status, nearly equivalent proportions of URM and White/Asian American engineering degree holders aspire to master’s degrees. However, higher proportions of White/Asian American degree holders aspire to doctoral and graduate business degrees, and the proportion of URM degree holders who do not plan to pursue education beyond a bachelor’s degree is nearly equivalent to the proportion who aspire to a master’s degree. These differences are displayed in Figure C-16.
To conceptualize the various post-college pathways taken by people who graduate with degrees in engineering, a flowchart is presented in Figure C-17 to illustrate the various decisions made by degree holders seven years after entering college. A small percentage leaves the workforce all together; only 3.4% indicated they were not working and did not plan to work. Nearly 30% had attended, or were attending, graduate or professional school of some sort – the vast majority of whom had chosen an engineering graduate program. Less than 9% pursued other STEM graduate study, and approximately 15% chose a non-STEM field, often business. Of those who had attended graduate school, slightly more than 40% were currently enrolled at the time the survey was administered. Slightly more than two-thirds of engineering degree holders entered the workforce without pursuing graduate study, and more than 9 in 10 of those in the workforce were currently employed at the time of the survey. Of those who were currently employed, the vast majority in this group were employed as engineers; approximately 5% worked in other STEM-related careers and nearly one-quarter were employed in non-STEM occupations.
The multivariate analyses also identified the type of institutions engineering aspirants attend that predict different pathways taken after completing their degrees. The regression model is displayed in Appendix C. These pathways include engineering pathways (choosing an engineering job or enrolling in an engineering graduate program), other STEM pathways (i.e., choosing a non-engineering STEM-related job or graduate program after college), or non-STEM pathways. Attending a four-year college, as opposed to a university, predicts a higher likelihood of choosing an engineering pathway over either a non-STEM or other STEM pathway. Attending a public university, compared to a private university, predicts a higher likelihood of choosing an engineering pathway over another STEM pathway. Finally, attending a more selective institution predicts a higher likelihood of choosing a non-STEM post-college pathway over an engineering pathway. Given the level of talent and persistence to the degree, this is somewhat disappointing that such students are less likely to continue in engineering after graduation. Further analysis of reasons for working in unrelated careers, post-college plans, and expectations follow in the next sections.
To get a sense of the type of careers graduate students planned to pursue after completing their programs, Figure C-18 illustrates the immediate expectations graduate students had for employment upon completion of their programs, and Figure C-19 displays these students’ ultimate career goals. Nearly half of graduate students expected to be employed in industry while another 30% expected to work in a position for which their program prepared them upon graduation. These figures changed very little between immediate expectations and ultimate goals, with the percentage of graduate students choosing “other position in industry” as their ultimate goal dropping by about 6%. The biggest change from immediate expectation to ultimate goal was in the percentage who
planned to become tenure track faculty members. Fewer than 2% selected either tenure-track, non-tenure-track, or non-postsecondary faculty member as their immediate expectation, but more than 8% chose tenure-track faculty member as their ultimate career goal. Although most engineering degree holders who pursue graduate study plan to work as engineers, nearly one in five is considering some type of academic or research position after completing their graduate work.
As this particular cohort of engineering graduates completed their bachelor’s degrees right as the recent Great Recession set in, they were asked a series of questions about their satisfaction with their standard of living and whether the economy affected them in any adverse manner. Overall, more than three-quarters of engineering degree holders indicated agreement that they were satisfied with their current standard of living. More than one-third, however, agreed that the economy had hurt their standard of living, nearly 40% had trouble finding a job, and almost half felt the economy affected decisions about their careers (see Figure C-20). Overall, 63% of engineering degree holders agreed with at least one of the three economic concerns, although a slightly smaller proportion (58%) of those satisfied with their standard of living agreed with at least one of the three concerns.
These data were also disaggregated by sex and status as an underrepresented racial or ethnic minority. Only two items differed significantly on the basis of sex. Women were more satisfied with their standard of living, and men were more likely to agree that they have had trouble finding a job. Three items differed significantly between URM participants and their White and Asian American colleagues. White and Asian American degree holders were more likely to indicate satisfaction with their current standard of living, while URM degree holders were more likely to hold at least one of the three economic concerns included on the survey. Additionally, URM degree holders were
the only group where the proportion of those who expressed satisfaction with their standard of living who agreed with at least one financial concern was higher than the proportion of overall URM engineering degree holders.
Reasons for Working in Unrelated Career
Approximately 12% (n=49) of the sample of engineering degree holders indicated working in a career that was unrelated to the discipline of their highest degree. Respondents who were working in an unrelated field were then asked a series of questions to determine some of the major reasons they chose to work outside their field. The reason most participants indicated affecting their decision to work outside their field at least to some extent was job location. More than 70% found location to be an important factor. More than half of participants also indicated pay and promotion opportunities (64%) and working conditions (55%) as affecting their decision as well. Equal proportions (43%) indicated family-related reasons or job availability as an important factor, and only one-third cited a change in professional interests as the reason for working outside their field. For this cohort, the decision to work outside their field of study appeared to result more from the economy and other structural factors than from a change in their interests. These data could not be disaggregated by sex or race due to the very small number of survey respondents who indicated working outside their field.
CONCLUSIONS AND RECOMMENDATIONS
Although the proportion of entering first-time, full-time college students interested in studying engineering has increased over the past decade, national reports still voice a concern that this increase has been insufficient to meet projected engineering workforce needs. Engineering aspirants are also some of the highest academically performing students in the nation, which may be attributable both to the selective admissions processes into engineering majors at many universities as well as students’ preconceptions that engineering is an academically demanding
field. Although engineering students complete degrees within their field at higher rates (41%) than physical (32%) or biomedical science students (37%), engineering students were also more likely to have not completed within six years (30% compared to 24% and 29%, respectively). In addition, many of these engineering bachelor’s degree holders chose post-college career and educational pathways outside of engineering. The purpose of this paper was to explore these outcomes and how college experiences predicted both retention in engineering degree programs and the pathways graduates took after completing their undergraduate programs.
Our analysis demonstrated various experiences that likely contributed to students’ retention in engineering programs and their selection of an engineering post-college pathway. Experiences that predicted retention in engineering included participation in internship programs and major-related clubs or organizations as well as studying with other students more frequently. Undergraduate research participation was also descriptively related to retention, and although it did not predict retention above other factors in our final regression model, undergraduate research did predict a higher likelihood of students choosing an engineering graduate program or an engineering job after graduation. Given engineering faculty were most likely to report involving undergraduates in their research, this finding is encouraging as it appears to enhance students’ aspirations to pursue a career in engineering. In addition, engineering students reported the highest levels of academic self-concept but reported the most growth in social self-concept. Faculty support and mentoring and studying with peers both related to gains in academic and social self-concept.
In terms of broader goals around diversifying the engineering field, women in particular continue to be underrepresented in engineering from college entry through entrance into the workforce. As women’s completion rates were higher than men’s, most of their underrepresentation seemed to stem from low representation at college entry. In addition, underrepresented racial/ethnic minority students entered college with similar aspirations as their White and Asian American peers to pursue engineering, but graduated in engineering, and STEM overall, at much lower rates. For URM students, experiences in college are likely diverting their engineering aspirations; one factor negatively predicting retention in engineering was the frequency students reported hearing faculty express racial stereotypes.
After completing college, approximately 40% of engineering degree holders intended to pursue some type of a master’s program, while 30% planned no further education than their bachelor’s degree and the remainder had higher degree aspirations. Women were somewhat more likely than men, and White and Asian American students more likely than their URM peers, to aspire to doctoral degrees. By seven years after college entry, nearly 30% of engineering degree holders had enrolled and possibly completed some type of graduate program, with more than three-quarters pursuing an engineering graduate degree. Of those who entered the labor force after graduation, approximately 70% were working in an engineering career seven years after first entering college. Small proportions indicated either being unemployed or having left the workforce altogether. For engineering graduates who were working in an unrelated field, the most frequently cited reason for working outside their field was related to location (~70%); other reasons included pay or promotion opportunities (64%), working conditions (55%), family reasons (43%), and job availability (43%). In general, most people who complete an engineering degree remain in the field after graduation; for those who do not the most cited reason is a geographic employment restriction. It appears that the better higher education can support engineering aspirants’ plans to pursue engineering as a career, and increase engineering degree productivity rates, those graduates will likely enter the engineering workforce and replenish projected needs.
These findings lead to several recommendations in order to boost engineering degrees and movement of diverse students through academic programs and into the workforce or academia. We offer recommendations in three major areas that are appropriate for practice and policy within and across institutions.
Improving diversity in engineering:
- Students can benefit from targeted activities to improve knowledge about engineering careers and increase engineering degree aspirations prior to and during the first year of college. However, the data indicate that women and African Americans may particularly benefit since their initial interests have been lower than their representation in the four-year college population. We recommend pre-college programs that target these students and provide hands-on opportunities for design activities and enhance use of mathematics and
- physics curricula. Women, in particular, have good retention rates once identifying an interest in engineering and can immediately benefit from activities to increase awareness, knowledge about careers, and interest at earlier stages of the education pipeline. This can occur by augmenting high school curricula and offering summer programs targeted for women and African Americans with promising mathematics and physics knowledge and abilities.
- Collaborations with industries can serve as important partners to sponsor activities in local schools that may be financially-strapped to help orient students toward careers in engineering. Retired engineers may consider teaching and/or offering services to help mentor or educate more youth in a variety of schools to understand engineering careers.
- Hispanic students enter college with aspirations in engineering similar to their representation in four-year institutions. This indicates that we can build on student interest and motivation but also that specific groups require more assistance in sustaining their interests during college through 1) increasing mastery of material to overcome uneven preparation in schools, which contributes to academic self-concept, 2) and developing supportive transition programs for aspiring engineers that ensure their success in the first year of college and beyond.
Building and sustaining support for college students:
- Within institutions of higher education, many introductory courses in physics have been redesigned as working studios of active learning, which not only helps mastery of the material but also attracts students who may otherwise opt toward other fields of study. Engineering schools and departments should collaborate with faculty teaching introductory courses (mathematics, physics, chemistry, and biology) to provide more problem-based and active learning environments.
- Peers and peer clubs and organizations focused on academics and careers (e.g. Society of Black Engineers, Society of Hispanic Professional Engineers) are important activities that are related retention in engineering. Institutions should support such student-run organizations academically and financially.
- Institutions should ensure a range of internship opportunities are made available to all students, with specific attention to underrepresented groups who may not otherwise have access to the same opportunities or networks. Internships work to increase retention and help students to see themselves becoming an engineer.
- Engineering schools and departments should promote faculty development to work with diverse learners, understand the effects of implicit bias and negative stereotyping, and build inclusive classroom environments so that students learn to work in teams with diverse classmates. Underrepresented groups are often the target of stereotyping, offensive comments, or exclusion when they are “solo status” or one of the few women or minorities in education or work environments.
Sustaining support for graduate study and work in engineering:
- Faculty networks and support are critical to students finding appropriate graduate programs, gaining admission, and identifying a range of careers in research and academia. Faculty recognition is critical to becoming a scientist and many students may not initially see themselves as pursuing graduate work without the advice or encouragement from faculty.
- Selective institutions often divert talented students to other non-STEM fields during college, but these data show that they also do so after college. If they prepare more students for graduate study, they should consider specific initiatives to help train graduate students to become effective teachers in engineering and other science fields. They may also consider building partnerships with industry into education programs to encourage engineering graduates to advance the next scientific innovations.
- Economic issues play an important role in determining students’ choices after obtaining a degree in engineering. Finding ways to make the most of highly skilled engineers in a variety of work environments should be a joint effort of academia and industry particularly during periods of retrenchment. Future work may help to identify multiple pathways in the long term careers of engineers so that more young people can take a longer view of the choices they make now that may move them from scientific development, to assessing government needs, and/or policy work.
National reports project colleges and universities in the United States are not graduating enough students in science and engineering programs to meet anticipated workforce needs (President’s Council of Advisors on Science and Technology, 2012). This study examined students’ trajectories through engineering programs from college entry through their post-college academic and employment outcomes. Higher education plays an important role in training tomorrow’s engineers; by identifying factors that contribute to retention, degree completion, and postcollege pathways, this study helps inform policy and practice to improve the capacity of colleges and universities for replenishing the nation’s engineering workforce.
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Regression Models Predicting Self-Concept for Fourth-Year Engineering Students (n=650)
|Academic self-concept||Social self-concept|
|Underrepresented Minority Student||–0.074||0.549||–0.004||–0.135||0.122||0.547||0.007||0.223|
|Parent employed as engineer||0.035||0.708||0.002||0.049||1.611||0.703||0.066||2.292||*|
|Avg HS GPA||0.260||0.236||0.041||1.104||–0.029||0.219||–0.004||–0.133|
|Yrs of HS study: Math||0.629||0.569||0.034||1.106||–0.388||0.565||–0.020||–0.688|
|Yrs of HS study: Physics||0.273||0.209||0.040||1.307||0.413||0.208||0.057||1.991||*|
|Masters degree asp (ref: bach or less)||–0.113||0.723||–0.007||–0.156||0.597||0.725||0.034||0.823|
|Doctoral degree asp||1.131||0.815||0.060||1.387||1.087||0.814||0.055||1.335|
|Medical degree asp||0.831||1.311||0.022||0.634||1.340||1.310||0.033||1.023|
|Other degree asp||–0.631||2.390||–0.008||–0.264||–1.835||2.376||–0.022||–0.772|
|Type: four-year college||–0.477||0.579||–0.028||–0.824||–0.852||0.576||–0.047||–1.480|
|Selectivity (avg SAT scaled by 100)||–0.517||0.318||–0.074||–1.624||0.040||0.316||0.005||0.125|
|Percent of students in STEM (10)||–0.035||0.140||–0.008||–0.251||0.136||0.139||0.031||0.977|
|Participated in an internship program||0.636||0.533||0.038||1.193||–0.847||0.532||–0.048||–1.592|
|Participated in an undergraduate research program (e.g. MARC, MBRS, REU)||0.087||0.695||0.004||0.125||–0.135||0.690||–0.006||–0.195|
|Joined a club or organization related to your major||0.850||0.565||0.049||1.505||1.035||0.561||0.056||1.846|
|Studied with other students||1.035||0.451||0.074||2.296||*||1.622||0.449||0.109||3.615||***|
|Met with an advisor/counselor about your career plans||0.579||0.439||0.044||1.321||0.706||0.437||0.050||1.615|
|Had instruction that supplemented course work||0.591||0.434||0.045||1.363||0.230||0.431||0.017||0.534|
|Faculty support and mentoring||0.957||0.316||0.108||3.028||**||1.092||0.314||0.116||3.474||***|
|Worked full-time while attending school||–0.692||0.687||–0.033||–1.008||0.784||0.686||0.035||1.143|
|Taken an ethnic studies course||0.114||0.530||0.007||0.216||1.626||0.527||0.090||3.085||**|
|Taken a women’s studies course||0.222||0.806||0.009||0.275||0.640||0.798||0.024||0.802|
|Hrs per wk: Commuting||0.315||0.202||0.051||1.559||–0.127||0.201||–0.020||–0.635|
|Academic self-concept||Social self-concept|
|Felt overwhelmed by all I had to do||–0.713||0.451||–0.049||–1.580||–1.068||0.450||–0.069||–2.377||*|
|I have been singled out because of my race/ethnicity, gender, or sexual orientation||–0.123||0.353||–0.013||–0.350||–0.004||0.351||0.000||–0.011|
|I have heard faculty express stereotypes about racial/ethnic groups in class||–0.410||0.359||–0.040||–1.141||0.598||0.358||0.055||1.672|
|Final model r2||0.353||0.433|
Note. * p<0.05; ** p<0.01; *** p<0.001. Sources: 2004 Freshman Survey, 2008 College Senior Survey, Cooperative Institutional Research Program, Higher Education Research Institute, UCLA.
Regression Model Predicting Four-year Retention in Engineering (n=979)
|Either parent employed in engineering||0.132||***||0.141||0.306||1.151|
|Pre-college academic preparation|
|Average HS GPA||0.221||***||0.279||*||0.116||1.322|
|SAT or ACT equivalent (scaled by 100)||0.262||***||0.162||0.098||1.176|
|Years of study in HS: Math||0.025||–0.164||0.286||0.849|
|Years of study in HS: Physics||0.059||–0.035||0.095||0.965|
|Masters degree aspiration (ref: bachelors or less)||–0.010||0.039||0.332||1.040|
|Doctoral degree aspiration||0.018||–0.033||0.372||0.968|
|Medical degree aspiration||0.006||0.517||0.635||1.677|
|Other degree aspiration||–0.001||–0.483||1.008||0.617|
|Four-year college (ref: university)||–0.062||0.003||0.267||1.003|
|Selectivity (average SAT, scaled by 100)||0.152||***||–0.350||*||0.140||0.705|
|Percent students in STEM (10% increments)||0.184||***||0.358||***||0.084||1.430|
|Participated in an internship program||0.288||***||0.938||***||0.240||2.554|
|Participated in an undergraduate research program||0.085||*||0.498||0.315||1.645|
|Participated in a club or organization related to your major||0.263||***||0.982||***||0.259||2.671|
|Studied with other students||0.236||***||1.164||***||0.211||3.203|
|Met with advisor/counselor about career plans||–0.030||–0.049||0.196||0.952|
|Had instruction that supplemented coursework||0.047||–0.276||0.191||0.759|
|Faculty interaction construct (scaled by 10)||–0.074||*||–0.375||**||0.141||0.687|
|Worked full-time while attending school||–0.176||***||–0.263||0.295||0.769|
|Taken an ethnic studies course||–0.130||***||0.105||0.247||1.111|
|Taken a women’s studies course||–0.197||***||–0.735||*||0.331||0.479|
|Hours per week: commuting||–0.213||***||–0.418||***||0.090||0.658|
|Self-concept and support|
|Felt family support to succeed||–0.037||–0.137||0.189||0.872|
|Academic self-concept (scaled by 10)||0.131||***||0.213||0.177||1.237|
|Social self concept (scaled by 10)||0.023||–0.188||0.155||0.828|
|Felt overwhelmed with all I had to do||0.082||*||0.194||0.213||1.214|
|I have been singled out on the basis of sex, race/ethnicity, or sexual orientation||–0.069||–0.111||0.159||0.895|
|I have heard faculty express racial/ethnic stereotypes in class||–0.175||***||–0.545||***||0.163||0.580|
Note. * p<0.05; ** p<0.01; *** p<0.001. Sources: 2004 Freshman Survey, 2008 College Senior Survey, Cooperative Institutional Research Program, Higher Education Research Institute, UCLA.
Regression model predicting post-college pathways for engineering graduates (n=1956)
|Engineering vs non-STEM pathway||Engineering vs other STEM pathway|
|B||exp(B)||Std. Error||Sig.||B||exp(b)||Std. Error||Sig.|
|Asian/Pacific Islander (ref: White)||0.380||1.462||0.147||**||–0.424||0.654||0.176||*|
|Latino (ref: White)||–0.675||0.509||0.144||***||0.279||0.263|
|Black (ref: White)||–0.588||0.555||0.206||**||0.383||0.450|
|American Indian (ref: White)||–0.370||0.292||0.971||0.760|
|Other Race (ref: White)||–0.083||0.279||0.301||0.428|
|Native English Speaker||–0.314||0.191||0.647||1.909||0.220||**|
|Low Income (ref: Middle)||0.936||2.549||0.272||***||1.990||7.318||0.572||***|
|Low-Middle Income (ref: Middle)||0.217||0.156||0.289||0.216|
|High-Middle Income (ref: Middle)||–0.168||0.098||–0.137||0.145|
|High Income (ref: Middle)||–0.282||0.146||–0.558||0.572||0.187||**|
|Mother’s Level of Education||0.043||0.027||0.032||0.039|
|High School GPA||0.281||1.324||0.045||***||–0.024||0.072|
|Years of Study in HS: Math||–0.006||0.094||0.234||0.130|
|Years of Study in HS: Physics||–0.036||0.036||0.232||1.261||0.052||***|
|Goal: Raising a Family||–0.030||0.048||0.059||0.070|
|Goal: Being Very Well-off Financially||–0.091||0.054||0.000||0.076|
|Estimated Undergraduate Debt ($10,000 units)||0.050||1.050||0.000||**||0.0200||0.030|
|Worked with Faculty on Research||0.376||1.457||0.104||***||–0.781||0.458||0.137||***|
|Received Mentoring from Faculty||0.007||0.090||–0.344||0.709||0.140||*|
|Participated in a Structured Research Program||0.304||1.355||0.131||*||0.210||0.153|
|Participated in an Academic or Professional Club||0.169||0.093||0.133||0.134|
|Initial Engineering Aspirant (ref: Late-comer)||0.633||1.883||0.134||***||0.413||1.512||0.203||*|
|Selectivity (average SAT score, scaled by 100)||–0.231||0.794||0.051||***||–0.007||0.074|
|Type: Four-year College (ref: university)||0.606||1.834||0.132||***||0.770||2.161||0.189||***|
Note. * p<0.05; ** p<0.01; *** p<0.001; engineering pathway is defined as choosing an engineering graduate program or employment as an engineer, and other STEM pathway is defined as choosing a STEM graduate program or career other than engineering. Source: 2011 Post-Baccalaureate Survey, Higher Education Research Institute, UCLA.