An Instructional Intervention to Encourage Effective Deep Collaborative Learning in Undergraduate Veterinary Students
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, veterinary education has received an increased amount of attention directed at the value and application of collaborative case-based learning. The benefit of instilling deep learning practices in undergraduate veterinary students has also emerged as a powerful tool in encouraging continued professional education. However, research into the design and application of instructional strategies to encourage deep, collaborative case-based learning in veterinary undergraduates has been limited. This study focused on delivering an instructional intervention (via a 20-minute presentation and student handout) to foster productive, collaborative case-based learning in veterinary education. The aim was to instigate and encourage deep learning practices in a collaborative case-based assignment and to assess the impact of the intervention on students' group learning. Two cohorts of veterinary students were involved in the study. One cohort was exposed to an instructional intervention, and the other provided the control for the study. The instructional strategy was grounded in the collaborative learning literature and prior empirical studies with veterinary students. Results showed that the intervention cohort spent proportionally more time on understanding case content material than did the control cohort and rated their face-to-face discussions as more useful in achieving their learning outcomes than did their control counterparts. In addition, the perceived difficulty of the assignment evolved differently for the control and intervention students from start to end of the assignment. This study provides encouraging evidence that veterinary students can change and enhance the way they interact in a group setting to effectively engage in collaborative learning practices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it