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Record W4322501752 · doi:10.1080/03075079.2023.2183385

Undergraduate students’ perceptions of learning from foreign-born faculty in American university settings

2023· article· en· W4322501752 on OpenAlexaboutno aff
Sung‐Ju Kim, Soonok An, Maura Nsonwu, Sharon D. Morrison, Jacqueline Henry

Bibliographic record

VenueStudies in Higher Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationPsychologyPreferenceMedical educationPerceptionDiversity (politics)PedagogyWorkforceQuarter (Canadian coin)Mathematics educationMedicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.459
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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