Investigating Veterinary Medicine Faculty Perceptions of Lecture Capture: Issues, Concerns, and Promises
Bibliographic record
Abstract
Lecture capture technology is becoming more pervasive in today's classrooms. Students are demanding their lectures be recorded, but many instructors remain resistant. The goal of this study was to investigate faculty perceptions of lecture capture and to understand their concerns with the technology. Through a review of the existing literature, three common reasons for not recording were identified: impact on class attendance, incompatible pedagogy, and technical concerns. To test the hypotheses, an electronic survey was created and distributed to the faculty of a veterinary college in the southeastern US. The survey included both quantitative and qualitative questions. An invitation was emailed to all 134 faculty members, garnering 50 responses. Results were consistent with the hypotheses. Impact on class attendance, teaching styles, and technical considerations have dissuaded many instructors from adopting lecture capture technology. However, a fourth theme that emerged was faculty lack of awareness/familiarity. According to the qualitative responses, many faculty either did not know lecture recording was available in their teaching spaces or were not trained in how to use the technology. Recommendations for future research include distributing the survey campus-wide and providing more opportunities for faculty training. It would also be worthwhile to repeat the survey after providing more information and training materials to faculty, or after switching from an opt-in to an opt-out approach, to see whether perceptions have changed among the college's faculty.
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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 | 0.038 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".