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We summarize national-scale data for Ph.D. earners in engineering or computer science from 2015 to 2019 whose post-graduate school employment is known, highlighting outcomes for biological/biomedical/biosystems engineering students. We use NSF’s Survey of Earned Doctorates (SED), which has collected information from Ph.D. recipients in the USA since 1957. The data are collected at the time of degree completion and constitute a greater than 90% response rate. Compared to all engineering and computer science disciplines, biological/biomedical/biosystems engineering has a higher proportion going to 4yr/med/research institutions (52% vs. 33%) and non-profit (3.6% vs. 2.9%) and lower proportion going to industry (33% vs. 48%), government (4.3% vs. 8.4%), and is similar for non-US positions (6.1% vs. 5.7%). Compared to 2010–2014 biological/biomedical/biosystems engineering Ph.D. recipients, more 2015–2019 recipients are going to industry (25% to 33%) and fewer to 4yr/med/research institutions (59% to 52%) and governmet (5.3% to 4.3%). Across all engineering and computer science disciplines, a smaller proportion of females entered industry (43%) compared to males (49%), while a larger proportion of females entered 4yr/med/research institutions (37%) compared to males (32%). Over half of Asian doctoral recipients entered industry, as compared to 38% of Hispanic doctoral recipients. In contrast, a higher proportion of Hispanic individuals (37%) entered 4yr/med/research institutions after their doctoral programs, as compared to 31% of Asian doctoral recipients. Black doctoral recipients had the highest proportion enter positions in government (14%) and non-profit (4%) sectors. Our results are situated in the broader literature focused on postdoctoral career, training, and employment sectors and trends in STEM. We discuss implications for graduate programs, policymakers, and researchers.

 

Whether doctoral students are funded primarily by fellowships, research assistantships, or teaching assistantships impacts their degree completion, time to degree, learning outcomes, and short- and long-term career outcomes. Variations in funding patterns have been studied at the broad field level but not comparing engineering sub-disciplines. We addressed two research questions: How do PhD student funding mechanisms vary across engineering sub-disciplines? And how does variation in funding mechanisms across engineering sub-disciplines map onto the larger STEM disciplinary landscape? We analyzed 103,373 engineering and computing responses to the U.S. Survey of Earned Doctorates collected between 2007 and 2016. We conducted analysis of variance with Bonferroni post hoc comparisons to examine variation in funding across sub-disciplines. Then, we conducted a k-means cluster analysis on percentage variables for fellowship, research, and teaching assistantship funding mechanism with STEM sub-discipline as the unit of analysis. A statistically significantly greater percentage of biomedical/biological engineering doctoral students were funded via a fellowship, compared to every other engineering sub-discipline. Consequently, biomedical/biological engineering had significantly lower proportions of students supported via research and teaching assistantships than nearly all other engineering sub-disciplines. We identified five clusters. The majority of engineering sub-disciplines grouped together into a cluster with high research assistantships and low teaching assistantships. Biomedical/biological engineering clustered in the high fellowships grouping with most other biological sciences but no other engineering sub-disciplines. Biomedical/biological engineering behaves much more like biological and life sciences in utilizing fellowships to fund graduate students, far more than other engineering sub-disciplines. Our study provides further evidence of the prevalence of fellowships in life sciences and how it stretches into biomedical/biological engineering. The majority of engineering sub-disciplines relied more on research assistantships to fund graduate study. The lack of uniformity provides an opportunity to diversify student experiences during their graduate programs but also necessitates an awareness to the advantages and disadvantages that different funding portfolios can bestow on students.

 

In this paper, we report the description and evaluation of an annual workshop titled “Capacity Building Workshops for Competitive S-STEM Proposals from Two-Year Colleges in the Western U.S.” which was offered in June of 2019, 2020, and 2021 with the goal of facilitating submissions to the NSF S-STEM program from 2-year colleges (2YCs). The two-day workshop was composed of separate sessions during which participants discussed several aspects of proposal preparation. Participants also received pre- and post-workshop support through webinars and office hours. To evaluate the program, post-workshop surveys were administered through Qualtrics™. The workshop and related activities received overall positive feedback with specific suggestions on how to better support participants. The paper discusses specific challenges faced by 2YC teams in preparing their proposals. Over three offerings, the program welcomed 103 participants on 51 teams from 2YCs. As of 2021, 11 teams total (from the 2019 cohort) submitted proposals. Among them, four were funded, which is approximately double the typical success rate. Six of the declined teams resubmitted and one of them is currently in negotiations. 

Abstract Background Students’ attitudinal beliefs related to how they see themselves in STEM have been a focal point of recent research, given their well-documented links to retention and persistence. These beliefs are most often assessed cross-sectionally, and as such, we lack a thorough understanding of how they may fluctuate over time. Using matched survey responses from undergraduate engineering students ( n = 278), we evaluate if, and to what extent, students’ engineering attitudinal beliefs (attainment value, utility value, self-efficacy, interest, and identity) change over a 1-year period. Further, we examine whether there are differences based on gender and student division, and then compare results between cross-sectional and longitudinal analyses to illustrate weaknesses in our current understanding of these constructs. Results Our study revealed inconsistencies between cross-sectional and longitudinal analyses of the same dataset. Cross-sectional analyses indicated a significant difference by student division for engineering utility value and engineering interest, but no significant differences by gender for any variable. However, longitudinal analyses revealed statistically significant decreases in engineering utility value, engineering self-efficacy, and engineering interest for lower division students and significant decreases in engineering attainment value for upper division students over a one-year period. Further, longitudinal analyses revealed a gender gap in engineering self-efficacy for upper division students, where men reported higher means than women. Conclusions Our analyses make several contributions. First, we explore attitudinal differences by student division not previously documented. Second, by comparing across methodologies, we illustrate that different conclusions can be drawn from the same data. Since the literature around these variables is largely cross-sectional, our understanding of students’ engineering attitudes is limited. Our longitudinal analyses show variation in engineering attitudinal beliefs that are obscured when data is only examined cross-sectionally. These analyses revealed an overall downward trend within students for all beliefs that changed significantly—losses which may foreshadow attrition out of engineering. These findings provide an opportunity to introduce targeted interventions to build engineering utility value, engineering self-efficacy, and engineering interest for student groups whose means were lower than average. 

DEI programming in recent years has focused significant efforts on fostering inclusion of visible identities, such as race/ethnicity, certain dominant gender identities, and certain dominant forms of sexual orientation. However, there is a lack of understanding of how DEI programs can target people with multiple, possibly invisible, marginalized identities, such as asexuality. Furthermore, while DEI programs tend to provide valuable and necessary support spaces for students from marginalized backgrounds, they may not consider how marginalized students create their own resistance practices. In this paper, we explore the liminal space of invisible identity and its intersections with other identities through a case study of an asexual cis-gender woman undergraduate engineering student through the lens of transformational resistance and identity development. Through her narrative, we see how transformational resistance can occur at any part of the identity development process, though certain identities during these parts may not be salient or significant to the individual. This paper addresses the complexity in creating diversity, equity, and inclusion (DEI) spaces for invisible marginalized identities and offers the experiences of the participant to question the bounds of inclusivity in these spaces. 

Despite well‐documented benefits, instructor adoption of active learning has been limited in engineering education. Studies have identified barriers to instructors’ adoption of active learning, but there is no well‐tested instrument to measure instructors perceptions of these barriers.

We developed and tested an instrument to measure instructors’ perceptions of barriers to adopting active learning and identify the constructs that coherently categorize those barriers.

We used a five‐phase process to develop an instrument to measure instructors’ perceived barriers to adopting active learning. In Phase 1, we built upon the Faculty Instructional Barriers and Identity Survey (FIBIS) to create a draft instrument. In Phases 2 and 3, we conducted exploratory factor analysis (EFA) on an initial 45‐item instrument and a refined 21‐item instrument, respectively. We conducted confirmatory factor analysis (CFA) in Phases 4 and 5 to test the factor structure identified in Phases 2 and 3.

Our final instrument consists of 17 items and four factors: (1) student preparation and engagement; (2) instructional support; (3) instructor comfort and confidence; and (4) institutional environment/rewards. Instructor responses indicated that time considerations do not emerge as a standalone factor.

Our 17‐item instrument exhibits a sound factor structure and is reliable, enabling the assessment of perceived barriers to adopting active learning in different contexts. The four factors align with an existing model of instructional change in science, technology, engineering, and mathematics (STEM). Although time is a substantial instructor concern that did not comprise a standalone factor, it is closely related to multiple constructs in our final model.

 

In this study, we examined the relation between students’ affective and behavioral response to active learning, the influence of students’ belongingness and their self-efficacy on these responses, and the moderating influence of students’ gender-identity. We found that, despite mean differences in value, positivity, and distraction, there were not gender differences in the pattern of relations between variables. For both groups, belongingness and self-efficacy independently predicted students’ affective response and their evaluation of the class. Belongingness also predicted students’ participation in class. These findings suggest that student-level factors play an important role in how students respond to active learning and that fostering an atmosphere that supports both self-efficacy and belongingness may be beneficial for all students.