The Influence of Mode of Instruction (Recorded Versus In-Person Lecture) on Student Achievement on Written Examinations in a Veterinary Clinical Toxicology Course
Bibliographic record
Abstract
The use of recorded on-line lecture presentation has increased in recent years in veterinary medical education. The effects of recorded on-line lectures on student knowledge acquisition are incompletely studied and there is very little information specifically addressing veterinary medical students. We studied the written examination performance of 373 third-year students spanning 4 calendar years (2017–2019, 2022) enrolled in a veterinary toxicology course which were exposed to either in-person lectures or recorded lectures of the exact same material. There was no difference in overall examination performance for students receiving on-line instruction compared to in-person lectures from the same instructor and instructional materials ( p = .254). However, students receiving in-person lectures compared to those that received recorded lectures demonstrated improved performance on exact matching questions (92.9% vs. 81.8%, respectively; p < .001). This study contributes to the limited body of knowledge regarding didactic instructional methodology in veterinary medicine. Further and more detailed studies are warranted to ensure optimal methods are employed in veterinary student instruction.
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.
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 | 0.002 | 0.018 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".