Whose feedback? A multilevel analysis of student completion of end-of-term teaching evaluations
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
Student evaluation of teaching (SET) is now common practice across higher education, with the results used for both course improvement and quality assurance purposes. While much research has examined the validity of SETs for measuring teaching quality, few studies have investigated the factors that influence student participation in the SET process. This study aimed to address this deficit through the analysis of an SET respondent pool at a large Canadian research-intensive university. The findings were largely consistent with available research (showing influence of student gender, age, specialisation area and final grade on SET completion). However, the study also identified additional influential course-specific factors such as term of study, course year level and course type as statistically significant. Collectively, such findings point to substantively significant patterns of bias in the characteristics of the respondent pool. Further research is needed to specify and quantify the impact (if any) on SET scores. We conclude, however, by recommending that such bias does not invalidate SET implementation, but instead should be embraced and reported within standard institutional practice, allowing better understanding of feedback received, and driving future efforts at recruiting student respondents.
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 | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 it