Cultivating a Science, Technology, Engineering and Mathematics (STEM) community for two-year college student success and persistence
Bibliographic record
Abstract
Undergraduate students studying Science, Technology, Engineering and Mathematics (STEM) often fail to persist in critical "gateway" courses, resulting in students leaving the STEM pathway. Community college students leave STEM pathways at higher rates than students at universities. Implementation of a program designed to engage community college STEM students and faculty in a community of support was associated with increased persistence in STEM gateway courses and associate degree completion. Program elements included support staff, a STEM study room with peer tutors, faculty advisors, and transfer assistance. Over seven years, 415 students joined this opt-in support program. The majority of students in this program were economically disadvantaged and many were nontraditional college students. Using institutional data we tested the hypothesis that participation in this program was associated with increased student success and persistence in STEM courses and at the college. The mean GPA for students in the program in the ten courses with the highest STEM enrollments was higher (2.89) than that for other students (2.76). Quarter-to-quarter persistence was 87% for program students compared to 67% for non-program students in a matched student population. In STEM gateway courses, program students had between 1.2x to 3.5x greater likelihood than non-program students of progressing to precalculus-2 controlling for first-attempt GPA in precalculus-1. Similar persistence patterns were observed for other gateway STEM courses. Observed persistence for students in the program was higher than comparable groups of students, including persistence for those who experienced early failure in STEM courses. These data suggest students should be supported through early failure to enable persistence in critical STEM sequences, especially in gateway Math and Chemistry courses.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".