Western-Educated Faculty Challenges in a Gulf Classroom
Bibliographic record
Abstract
The purpose of this study was to investigate challenges for new Western/Western-educated faculty and their responses to those challenges as they began teaching at a national women’s University located in the Arabian Gulf. Twenty-four new faculty were recruited for the study. Faculty were hired from America, the UK, Canada, and Australia to provide an American-like education to students. 75% percent of participants were native English speakers, and 54% had not taught in a foreign university before. Before arriving in the country to begin teaching, the participants were asked to complete a questionnaire measuring their attitudes towards student learning and performance, and their teaching methods. Six to ten weeks after the beginning of their first semester they were asked to participate in focus groups. The focus group questions asked faculty to discuss issues and challenges they were facing and how they were coping. Results of the quantitative data and the focus groups show faculty were facing classroom challenges but were committed and creative in finding teaching/learning strategies. Focus group data further showed faculty were experiencing cultural differences that were challenging and frustrating. They were finding their teaching rewarding and were enjoying the experience of interacting with the students who were the first generation of women in the nation to be educated in a Western-style pedagogy.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".