Small samples, unreasonable generalizations, and outliers: Gender bias in student evaluation of teaching or three unhappy students?
Bibliographic record
Abstract
In a widely cited and widely talked about study, MacNell et al. (2015) [1] examined SET ratings of one female and one male instructor, each teaching two sections of the same online course, one section under their true gender and the other section under false/opposite gender. MacNell et al. concluded that students rated perceived female instructors more harshly than perceived male instructors, demonstrating gender bias against perceived female instructors. Boring, Ottoboni, and Stark (2016) [2] re-analyzed MacNell et al.’s data and confirmed their conclusions. However, the design of MacNell et al. study is fundamentally flawed. First, MacNell et al.’ section sample sizes were extremely small, ranging from 8 to 12 students. Second, MacNell et al. included only one female and one male instructor. Third, MacNell et al.’s findings depend on three outliers – three unhappy students (all in perceived female conditions) who gave their instructors the lowest possible ratings on all or nearly all SET items. We re-analyzed MacNell et al.’s data with and without the three outliers. Our analyses showed that the gender bias against perceived female instructors disappeared. Instead, students rated the actual female vs. male instructor higher, regardless of perceived gender. MacNell et al.’s study is a real-life demonstration that conclusions based on extremely small sample-sized studies are unwarranted and uninterpretable.
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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 | 0.034 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".