Determinants of Accounting Student Evaluations of Teaching Scores
Bibliographic record
Abstract
Given the prevalent use of the student evaluations of teaching (SET) as a measure of teaching effectiveness, this study aims to investigate the determinants of SET scores among students attending the College of Business Studies at the Public Authority for Applied Education and Training (PAAET), Kuwait. A total of 678 SET were analysed using univariate and multiple regression analyses. It was found that SET scores were significantly and positively biased by expected grade, student age and course level. In contrast, class size and faculty experience were found to be significantly and negatively related to SET. Expected grade had the strongest impact on SET scores. The study findings raise concerns about the reliability and validity of the SET as well as their suitability for evaluation purposes. As SET scores have an important assessment function and serve as formative and summative measures in personnel decisions, the incentives for faculty to compromise their grading standards to receive good teaching evaluations increase. Accordingly, administrators should devote more effort to ensure a careful and complete understanding and interpretation of SET if they want to effectively incorporate them into the faculty evaluation process. To the authors’ knowledge, this is the first study to explore determinants of student evaluations of teaching scores in Kuwait.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".