Board 107: Explaining Choice, Persistence, and Attrition of Black Students in Electrical, Computer, and Mechanical Engineering: Award #EEC-1734347 - Year 1
Bibliographic record
Abstract
Abstract The objective of this project is to identify policies and practices that lead to increased persistence and graduation as well as attrition for Black students in Electrical Engineering (EE), Computer Engineering (CpE), and Mechanical Engineering (ME) and to make actionable recommendations for policy makers regarding best practices. Building on prior work that demonstrated the impacts of gender and race on academic trajectories for engineering as a whole and electrical/computer engineering (ECE) and ME in particular, our transformative mixed-methods project responds to calls for more cross-institutional qualitative and longitudinal studies of minorities in engineering education. The study will investigate the following overarching research questions: 1. Why do Black men and women choose and persist in, or leave, EE, CpE, and ME? 2. What are the academic trajectories of Black men and women in EE, CpE, and ME? 3. In what ways do these pathways vary by gender or institution? 4. What institutional policies and practices promote greater retention of Black engineering students? Our project will explore the different disciplinary cultures of EE, CpE, and ME as these fields provide a sharply contrasting picture of engineering matriculation, persistence, and attrition for Black students. EE is one of the largest and oldest engineering disciplines, dating back to the 1880s. CpE is frequently combined with EE in a single department, but it has much different student demographics and outcomes. Both EE and CpE attract an above average percentage of Black males and females compared to other engineering disciplines. ME dates back to the 19th century and is currently the largest engineering discipline, awarding 23.8% of engineering degrees in the U.S. and Canada in 2015. ME attracts a smaller percentage of Black engineering students, but is better at retaining them (especially females) to graduation. Our mixed-methods approach combines the quantitative power of large sample sizes available from the Multi-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) and the qualitative richness of 80 in-depth interviews and detailed content analysis of institutional policies and contexts at four institutions. This approach will allow for the development of the thematic rigor necessary to advance theoretical understanding of engineering education for underrepresented minorities (URMs). We will draw on the theoretical frameworks of intersectionality, critical race theory, and community cultural wealth to guide our research and interpret our findings. In the first year, we will report preliminary results from a scan of the policies and programs in place for Black students in these majors, a topline summary of interviews with persisters and switchers in these majors, and preliminary quantitative analysis of outcomes for Black students in these majors from the MIDFIELD database.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".