Comparison of student evaluation of teaching results when stratified by protocol, course content, and course structure
Bibliographic record
Abstract
Focusing on the mechanical engineering undergraduate program at the University of Alberta, this study attempts to quantify biasesin student evaluation of teaching (SET) results that could be attributed to SET protocol, course content, and course delivery mode.SET results were compiled for five academic years of paper based SET evaluation and one semester of online SET evaluation. 20core undergraduate courses were included; class size from 70–130; 35 professors. Statistical analysis included compilation offrequency histograms, determination of means and standard deviations, and rank-sum tests for significant differences based onaggregated data for several stratifications. Results showed significantly reduced response rate for online SET when compared topaper; ratings of professor evaluation were not different. No significant differences were found when results were compared on thebasis of course content or delivery mode. Our aggregated data showed SET protocol lead to lower response rate, but notsignificant differences in instructor evaluation. Course content and delivery mode did not manifest in significant changes in SETresults. Typical variability in instructor rating was 0.4/5.0 considering all data. Administrators and senior faculty should be awareof these results when ascertaining instructor performance. Although focused on one department, the study is a first step in a largerevaluation of SET in engineering. The study identified key variables that must be further evaluated.
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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 | 0.033 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".