An International Survey of Veterinary Students to Assess Their Use of Online Learning Resources
Bibliographic record
Abstract
Today's veterinary students have access to a wide range of online resources that support self-directed learning. To develop a benchmark of current global student practice in e-learning, this study measured self-reported access to, and use of, these resources by students internationally. An online survey was designed and promoted via veterinary student mailing lists and international organizations, resulting in 1,070 responses. Analysis of survey data indicated that students now use online resources in a wide range of ways to support their learning. Students reported that access to online veterinary learning resources was now integral to their studies. Almost all students reported using open educational resources (OERs). Ownership of smartphones was widespread, and the majority of respondents agreed that the use of mobile devices, or m-learning, was essential. Social media were highlighted as important for collaborating with peers and sharing knowledge. Constraints to e-learning principally related to poor or absent Internet access and limited institutional provision of computer facilities. There was significant geographical variation, with students from less developed countries disadvantaged by limited access to technology and networks. In conclusion, the survey provides an international benchmark on the range and diversity in terms of access to, and use of, online learning resources by veterinary students globally. It also highlights the inequalities of access among students in different parts of the world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".