Veterinary Student Evaluations of Teaching: Scores and Response Rate When Administered before or after Final Exams
Bibliographic record
Abstract
In higher education, instructors and administrators use student evaluations of teaching (SETs) as formative and summative assessments of instruction; thus, they need adequate response rates for optimal validity and reliability. Veterinary students are often requested to complete dozens of SETs each semester, and response rate is shown to decline as the number of SETs increases. Allowing students to complete SETs after final examinations has been suggested to help increase response; however, students’ knowledge of their final course grade has been previously shown to negatively influence SET scores. This case study explored how making SETs available to veterinary students after final exams affected quantitative item scores and response rate when compared to SETs administered during the final weeks of the semester, prior to final examinations. Participants ( n = 262) were randomly assigned to before finals or after finals groups, and 171 students completed 2,926 SETs. Students were more likely to complete evaluations before finals (vs. after), and first-year students completed more SETs than third-year students. Compared to the prior year, in which SETs were administered before finals, students in the study year completed 31% fewer SETs. Timing of SET delivery did not significantly affect SET item scores, but third-year students rated instructors higher than first-year students on five of 10 items. Students’ self-reported expected grade was positively correlated with all 10 SET items for both groups. In this study, timing of completion had no statistical effect on SET item score. However, when students completed SETs after final exams, response rates decreased.
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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 | 0.023 | 0.027 |
| 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) | 0.001 | 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".