PANEL: Gender bias in student evaluations of teaching
Bibliographic record
Abstract
Abstract There is evidence that student evaluation of teaching (SET) is influenced by gender (both of the instructor and of the student). For example, one study in an online course showed that students rated instructors they believed were male higher than those they believed were female, regardless of actual gender or teaching performance (MacNeil, 2015). Factors like size of class and perceived nurturing behavior from female instructors may also affect the outcomes. Because hiring, promotion, and tenure decisions are increasingly reliant on student evaluations as a measure of teaching effectiveness and women may be more likely to receive lower ratings (particularly from male students, which comprise a majority of our engineering student bodies), this is a topical issue that may systematically inhibit the retention and advancement of female faculty members in engineering. During this panel we will discuss the role of SET in tenure and other decisions; the evidence for/effect of evaluation gender bias; the underlying basis for student ratings (teaching effectiveness, or other (potentially gendered) factors); strategies to mitigate the effect of gender bias on SET (student-, class-, department-, or institution-level); and the effect of gender interaction (teacher-student), with majority male student bodies.
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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 | 0.008 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".