Comparison of student evaluation of teaching results when stratified by protocol, course content, and course structure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it