A Retrospective Analysis of Pre-/Post-Test Scores of Students Participating in Online Asynchronous Shelter Surgery Coursework
Bibliographic record
Abstract
The University of Pennsylvania School of Veterinary Medicine first offered the elective Student Shelter Opportunities I (SSOI) course in 2016 to provide pre-clinical students with an opportunity to engage with shelter medicine and high-quality, high-volume surgery (HQHVS) concepts. The course utilized online asynchronous coursework to deliver content that was completed on a self-guided timeline by students. With most of the veterinary medical curriculum delivered in a traditional classroom format, it is important to assess learning in this unique course format. There is also limited information on educational experiences in online shelter medicine coursework. This retrospective study aimed to evaluate student learning in the asynchronous online portion of the SSOI elective course using paired pre- and post-test scores from a multiple-choice type assessment. The study investigated how students’ pre-test and post-test scores compared and whether time to completion of material influenced student assessment performance. Paired assessments from 400 students were analyzed, and a statistically significant increase was found in post-test scores compared to pre-test following completion of the online coursework ( p < .001). There was no significant difference in the mean change in score from pre-test to post-test for students who completed the online course material in 30 days or less compared to those who completed it in greater than 30 days. This study’s findings support online asynchronous learning as an effective option to teach veterinary students and can be considered in the development of veterinary coursework, including for curricular adjustments to increase online learning during the SARS-CoV-2 pandemic.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".