Persistence, Engagement, and Migration in Engineering Programs
Bibliographic record
Abstract
Abstract Records from the Multiple‐Institution Database for Investigating Engineering Longitudinal Development indicate that engineering students are typical of students in other majors with respect to: persistence in major; persistence by gender and ethnicity; racial/ethnic distribution; and grade distribution. Data from the National Survey of Student Engagement show that this similarity extends to engagement outcomes including course challenge, faculty interaction, satisfaction with institution, and overall satisfaction. Engineering differs from other majors most notably by a dearth of female students and a low rate of migration into the major. Noting the similarity of students of engineering and other majors with respect to persistence and engagement, we propose that engagement is a precursor to persistence. We explore this hypothesis using data from the Academic Pathways Study of the Center for the Advancement of Engineering Education. Further exploration reveals that although persistence and engagement do not vary as much as expected by discipline, there is significant institutional variation, and we assert a need to address persistence and engagement at the institutional level and throughout higher education. Finally, our findings highlight the potential of making the study of engineering more attractive to qualified students. Our findings suggest that a two‐pronged approach holds the greatest potential for increasing the number of students graduating with engineering degrees: identify programming that retains the students who come to college committed to an engineering major, and develop programming and policies that allow other students to migrate in. There is already considerable discourse on persistence, so our findings suggest that more research focus is needed on the pathways into engineering, including pathways from other majors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".