Undergraduate students’ perceptions of learning from foreign-born faculty in American university settings
Bibliographic record
Abstract
Foreign-born faculty (FBF) comprise between a quarter and a third of the higher education workforce in the U.S. today. As part of a larger mixed methods research project, we examined undergraduate students’ perceptions of FBF prior to and after engaging with them in the classroom. We conducted a cross-sectional online survey of undergraduate students (N = 474) enrolled at three public universities in the southeastern U.S. We examined demographic and educational correlates of undergraduate students’ perceptions of FBF, preferences regarding taking courses (preference factor) taught by FBF, and perceived benefits of learning (benefit factor) from FBF. Findings showed that several student characteristics (e.g. being Latinx/Hispanic; being born or raised with one or both parents from overseas; being an upperclassman; being a human-service major; taking at least one course with FBF) were associated with more positive perceptions of FBF and a higher level of perceived benefits of learning from FBF. The findings in this study strongly support the assertion that providing U.S. undergraduate students with more opportunities to learn from FBF in their college classrooms will support those students’ development of positive perceptions of diversity and difference as a key educational outcome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".