Learning While in Work: Exploring Influences on Engagement and Achievement in Veterinary Professionals Studying Remotely
Bibliographic record
Abstract
Remote learning provides flexible opportunities for veterinarians and veterinary technicians to undertake professional development qualifications alongside their work. Although this offers advantages in accessing courses that may otherwise not be available, online provision may not suit all learners equally. Using thematic analysis of semi-structured interviews with a group of veterinary educators (faculty, veterinarians, and veterinary nurses/technicians), this study explored their engagement and learning outcome achievements from a post-graduate certificate in veterinary education. Participants were highly motivated to engage, but their engagement was compromised when they had low levels of professional autonomy (particularly in scheduling study time and opportunities to put learning outcomes into practice). Some participants also found engagement more challenging when they experienced academic uncertainty or a reduction in social learning opportunities. A lot of the learning occurred in the participants applying taught content to practice, and therefore the education interface extended to the workplace. Educators teaching similar students using an online-only format should thus recognize the learners' workplaces as an important part of the learning environment and find ways to help them learn in that context. Learner engagement is also supported by interventions to foster social connections, scaffolded strategies for self-direction, and helping them to balance achievement against well-being goals.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".