Pre-Lab Videos as a Supplemental Teaching Tool in First-Year Veterinary Gross Anatomy
Bibliographic record
Abstract
To adapt to an interactive generation of learners, video resources can provide information necessary for lab preparation, describe clinical correlations, and maximize dissection time. In this study, dissection summary videos with embedded quizzes were to be viewed by K-State first-year veterinary students prior to their canine anatomy lab sessions. Videos were created using an iPhone, edited via Camtasia editing software, and uploaded to the course Canvas page. Following the conclusion of the Fall 2022 semester, final course grade, practical exam scores (exam), pre-lab video (video) time interaction, pre-lab quiz (quiz) scores, and student perception data were analyzed. Positive, statistically significant correlations were found between number of videos viewed and certain exam scores, with the strongest correlation being for the lower quartile specifically. Significant correlations were also found between average exam score and total number of videos viewed throughout the semester, and final course grade and total time spent viewing all videos. Positive, statistically significant correlations were found between average quiz score and exam score. A thematic analysis of student comments revealed videos appeared to have been a beneficial part of the course, providing students with a valuable resource for preparation, study, and increased understanding and confidence. These findings indicate that providing videos as a supplemental resource is beneficial to veterinary student learning and well perceived. This study also suggests that video views can predict lower quartile student exam score. The correlations in this study are weak, but the statistical significance depicts a positive impact on student practical exam scores.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".