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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.

Although advising relationships are key for doctoral student success, little research has addressed how they form. Understanding the formation of advising relationships can help contextualize their later development and ultimately support a student’s decision to persist in the doctorate. To understand relationship formation, the purpose of this qualitative study is to identify and describe the types of advisor–advisee selection processes that exist in engineering, science, and math doctoral programs and examine patterns across disciplines within those fields. We conducted interviews with doctoral program directors and engaged in document analysis of graduate student handbooks from 55 doctoral programs in the aforementioned fields in high research institutions across the United States. Using principal–agent theory as a theoretical lens, our findings showed that engineering programs tend to decentralize the advisor selection process by funding students across different funding sources upon enrollment. Contrariwise, science and math programs tended to fund all students in a cohort from a common funding source, which allowed students to have more time to gather information, meet, and select an advisor. These findings also show important nuances when comparing graduate education in these programs that directly impact the doctoral student experience and reiterates the necessity to study these fields separately.

Self-report assessments are used frequently in higher education to assess a variety of constructs, including attitudes, opinions, knowledge, and competence. Systems thinking is an example of one competence often measured using self-report assessments where individuals answer several questions about their perceptions of their own skills, habits, or daily decisions. In this study, we define systems thinking as the ability to see the world as a complex interconnected system where different parts can influence each other, and the interrelationships determine system outcomes. An alternative, less-common, assessment approach is to measure skills directly by providing a scenario about an unstructured problem and evaluating respondents’ judgment or analysis of the scenario (scenario-based assessment). This study explored the relationships between engineering students’ performance on self-report assessments and scenario-based assessments of systems thinking, finding that there were no significant relationships between the two assessment techniques. These results suggest that there may be limitations to using self-report assessments as a method to assess systems thinking and other competencies in educational research and evaluation, which could be addressed by incorporating alternative formats for assessing competence. Future work should explore these findings further and support the development of alternative assessment approaches.

Objective: Transfer student capital (TSC) helps community college students realize the potential for the transfer pathway to serve as a lower-cost option to a bachelor’s degree. However, students’ accrual of TSC depends on the quality and quantity of information networks and infrastructure; information asymmetry in these networks can impede students’ transfer progress. Methods: Using interview data from stakeholders who support engineering transfer students at one research university and two community college partners, we apply a methodology that combines qualitative coding techniques (i.e., descriptive, process, and evaluative coding) with network and pathway analyses to explore an information network for coursework transfer in engineering. Results: Our findings illustrate the disjointed and complex web of information sources that transfer students may use to accrue TSC. We highlight pathways fraught with information asymmetry as well as information sources and processes that give promise to students’ ability to accrue TSC and successfully navigate transfer of coursework vertically. Conclusions: An abundance of information sources and paths does not equate to a better transfer system. Utilizing network analysis to visualize and evaluate information sources and processes provides an additional method for evaluating information systems for transfer. Consolidating information sources or improving processes linking information sources could improve inefficiencies in transfer students’ transitions.