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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".