Investigation of Veterinary Student and Faculty Perspectives of Factors Affecting In-Person Lecture Attendance
Bibliographic record
Abstract
A proposal was submitted to our institution's curriculum committee to discontinue lecture livestreaming to increase attendance, and we performed a study to investigate factors affecting lecture attendance. In January 2022, the faculty and students were surveyed to explore their perspectives on the impact that student attendance has on both the student and faculty lecture experience. We included a subset of common questions to allow for comparison. For students, in-person lecture attendance was not largely influenced by content or delivery. Instead, most students indicated flexibility, preserving emotional well-being, optimizing efficiency, exams, and COVID-19 as important. Students also indicated that part-time jobs, caring for family or pets, and commuting were additional reasons to select a remote lecture experience. Faculty also recognized the impact of these factors on lecture attendance, but they were concerned about student learning and preparedness for clinics, and their own effectiveness and well-being as educators. Sixty-one percent of faculty agreed that low lecture attendance negatively impacted their overall professional satisfaction and 67% indicated that it decreased their enjoyment of teaching. Faculty mentioned missing real-time feedback from students and they expressed sadness at the loss of personal interactions. After reviewing results of the study, the curriculum committee voted to discontinue livestreaming of lectures. Although students provided strong feedback on the importance of flexibility, the committee agreed with faculty concerns. It remains to be determined whether lecture attendance increases because of this decision and preparedness for clinics should be objectively measured in the future.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".