The impact of cross-cultural issues on perceptions of teaching quality: do staff and students agree?
Bibliographic record
Abstract
The issue: The aim of this paper is to report on a substantive piece of research conducted with staff and students here at Middlesex University Business School. The focus of this research was to identify and explore the impact of cultural diversity on the quality of teaching and learning as perceived by both our students and our academic colleagues. The context: We have a high number of overseas students; in 2004 there were 794 undergraduate students and 798 taught postgraduate students from all parts of the world; this amounted to approximately one quarter of the student population. In the light of this increased diversity we felt it was appropriate to take a systematic view of the implications of these changed circumstances on staff and students. Methodology: Our research project utilised both focus groups for staff and questionnaires for students. 30 staff participated in the former and 953 students responded to the latter, which represents approximately one quarter of each total population. Data from the focus groups was content analysed and the questionnaires have been subject to an exploratory descriptive analysis. The results of the two elements of the study were then compared to see the extent to which they support or contradict each other, and the particular areas where this occurs.
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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 | 0.040 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".