STEM ATTRITION AMONG HIGH-PERFORMING COLLEGE STUDENTS IN THE UNITED STATES: SCOPE AND POTENTIAL CAUSES
Bibliographic record
Abstract
Postsecondary education plays a critical role in building a strong workforce in science, technology, engineering, and mathematics (STEM) fields. The U.S. postsecondary education system, however, frequently loses many potential STEM graduates through attrition. An increasing portion of STEM leavers are top performers who might have made valuable additions to the STEM workforce had they stayed in STEM fields. Using data from the 2004/09 Beginning Postsecondary Students Longitudinal Study (BPS:04/09), this study tracks a cohort of beginning bachelor’s degree students over 6 years, providing a close look at STEM attrition among a group of high-performing college students. Capitalizing on the transcript data collected through BPS:04/09, this study also examines STEM coursetaking, detailing how participation and performance in undergraduate STEM coursework are associated with students’ departure from STEM fields. The study finds that about a quarter of high-performing beginning bachelor’s degree students entered STEM fields (i.e., declared a STEM major) during their enrollment between 2003 and 2009, and a third of these entrants had left STEM fields by spring 2009. The results of multinomial probit regression analysis indicate that students’ intensity of STEM coursework in the first year and their performance in STEM courses may have played an important role in their decisions to switch majors out of STEM fields
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".