Do Veterinary Students See a Need for More In-Course Discussion? A Survey
Bibliographic record
Abstract
Rather than merely transferring information, veterinary education should stimulate and motivate students and encourage them to think. Currently in veterinary education, most curricula use the method of frontal teaching (e.g., in lectures). A student-centered critical approach to information is rarely used. Our research sought to determine if students consider in-course discussion useful and if sufficient possibilities for discussion are provided and supported by their lecturers. In December 2013, we conducted a survey of fourth-year students. Specifically, we wanted to know if students consider in-course discussion about course content useful for successful learning and if students wish to have more opportunities for discussion during class time. Finally, we wanted to identify barriers that limit the students' motivation and ability to engage in discussion of course content. In total, 105 students completed the survey. The majority of students agreed or strongly agreed that clinical topics should be discussed during class time. Frequently stated reasons were improved learning (85.7%) and the opportunity to look at topics from different perspectives (92.4%). In conclusion, we found a considerable dearth of and request for discussion within veterinary education. In light of these findings, we emphasize the need for new teaching strategies that promote independent thinking and critical questioning. We suggest the implementation of more discussion opportunities in well considered and moderated settings in veterinary teaching.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".