Themes from Veterinary Student Evaluations of Teaching Before and After Final Exams: Classroom Climate, Achievement Striving, Goal Attainment, and Operational Deliverables
Bibliographic record
Abstract
Comments requested on student evaluation of teaching (SET) forms are intended to help instructors identify strengths and weaknesses in their teaching methods. However, low SET completion rates limit their usefulness. Standard practice in US higher education is that students complete SETs before their final examination period due to concerns about negative effects of the exam and grade. Toward increasing completion rates, we altered the standard availability window and examined how completion of SETs after final exams affected the themes and sentiment in comments. Students were randomly assigned to before finals and after finals groups. Comments were coded and qualitative data transformed into frequencies. Three themes emerged: classroom climate, achievement striving and goal attainment, and operational deliverables. Students focused most on how instructors promoted or detracted from their understanding and retention, clarity of assessments, and teaching aid effectivity. They also frequently noted their appreciation and perceptions of whether instructors enjoyed teaching. Students praised instructors’ engagement as speakers and perceived dedication and effort. When students were critical, they focused most on grades, quantity of material, pace, and the curriculum. Before final exams, students commented more on the instructor's personality, clinical applicability of content, teaching aid effectivity, and pace; after final exams, they focused more on assessments. However, comments were largely commendable rather than critical, regardless of when SETs were completed. Contrasting some faculty perceptions that SETs are used to express anger, most students in this study expressed gratitude toward instructors for assisting them in achieving their goal of becoming a veterinarian.
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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 | 0.004 | 0.002 |
| 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 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".