Lecturer’s Gender and their Valuation of Student Evaluation of Teaching
Bibliographic record
Abstract
Student evaluation of teaching (SET) is carried out every semester at Malaysian universities and lecturers are evaluated based on student ratings. But very little is researched about what lecturers actually think about SET and whether it serves any meaningful purpose at all. This quantitative study involving six public universities on the East Coast of Peninsular Malaysia examines the extent to which male and female lecturers value their student evaluations of teaching (SET) and if their valuations differ significantly by gender. The study also elicits perceptions of male and female lecturers on the formative and summative functions the SET serves. A survey questionnaire comprising 29 Likert-scale items was employed to obtain data for this study. The respondents comprised 137 language instructors of English and Arabic proficiency courses for undergraduates from those six universities. Data were analysed descriptively and inferentially using descriptive statistics and independent sample t-test. The study produces several outcomes. The study found that although male lecturers value SET higher than their female colleagues, no significant differences are observed in their valuation of SET by gender. Also of significance is the observation that lecturers of both gender found SET more useful for formative rather than summative functions. This study itself is significant in order to understand the lecturers’ perceptions of SET which will stimulate further discussions and thinking around the issue of strengthening the teaching practice as SET itself can be an effective tool for lecturer’s development if lecturers themselves understand and value the process.
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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 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".