A social-belonging intervention improves STEM outcomes for students who speak English as a second language
Bibliographic record
Abstract
Students who speak English as a second language (ESL) are underserved and underrepresented in postsecondary science, technology, engineering, and math (STEM) fields. To date, most existing research with ESL students in higher education is qualitative. Drawing from this important body of work, we investigate the impact of a social-belonging intervention on anticipated changes in belonging, STEM GPA, and proportion of STEM credits obtained in students' first semester and first year of college. Using data from more than 12,000 STEM-interested students at 19 universities, results revealed that the intervention increased ESL students' anticipated sense of belonging and three of the four academic outcomes. Moreover, anticipated changes in belonging mediated the intervention's effects on these academic outcomes. Robustness checks revealed that ESL effects persisted even when controlling for other identities correlated with ESL status. Overall, results suggest that anticipated belonging is an understudied barrier to creating a multilingual and diverse STEM workforce.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".