Evaluating Student Satisfaction with Remote Learning in a Veterinary School
Bibliographic record
Abstract
Veterinary college curricula are generally offered through face-to-face lectures and laboratories. However, because of the COVID-19 global pandemic, entire veterinary curricula throughout the United States were forced to utilize remote learning with large portions of courses provided through synchronous or asynchronous delivery platforms employing video portal systems in spring 2020. The purpose of this study was to examine the satisfaction of veterinary students who were taught through remote learning with the option of synchronous live streaming lectures or asynchronous recorded lectures for a portion of 1 semester. This study also examined student satisfaction by comparing two cohorts of students taught via remote learning during the same semester (semesters 2 and 4 in the curriculum). The sample population consisted of a convenience sample of 242 veterinary students from one large southeastern veterinary college, who were asked to complete the end-of-semester course evaluation, which included five statements pertaining to remote learning. This study was performed to provide insight into changes that could be considered in the future as veterinary education seeks to utilize advancing technology and increase flexibility in learning while still providing high-quality education. Measures of dispersion and frequency were used to analyze the data. Veterinary students in this study preferred watching recorded lectures to streaming live lectures. Additional responses indicated overall agreement from both groups regarding lecture length, support for remote learning, and available resources for remote learning.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".