Are students gender-neutral in their assessment of online teaching staff?
Bibliographic record
Abstract
The scores from student evaluation forms are often used to construct assessments about the quality and effectiveness of course instruction. These assessments, in turn, can have serious professional impacts on instructors with respect to future course assignments, grants, promotion and hiring decisions. Previous research suggests that scores from student evaluations can be significantly predicted by factors other than teaching quality, in particular, the gender of teaching staff. However, most existing research is challenged by logistical constraints of separating gender from teaching quality in face-to-face courses. The goal of the current study was to examine gender-neutrality in students’ assessments of teaching assistants, by manipulating perceived teaching assistant gender for an online course. Using a mixed methods approach across nine semesters in which teaching assistants posed as both male and female, findings from 232 students suggest mixed evidence concerning gender bias in evaluations of teaching assistant performance. Highlights include no significant differences for the overall mean performance scores between the perceived male versus perceived female subgroups; individual item analysis suggests that students were more likely to rate the perceived male teaching assistant as having high expectations and the course as having a more challenging workload. Limitations and directions for future research are discussed.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".