Approaches and Study Skills of Veterinary Medical Students: Effects of a Curricular Revision
Bibliographic record
Abstract
The objective of this study was to determine if a revised, recently implemented curriculum, embracing an integrated block design with a focus on student-centered, inquiry-based learning, had a different effect on veterinary medical students' approaches to studying than the previous curriculum. A total of 577 students completed a questionnaire consisting of the short version of the Approaches and Study Skills Inventory for Students (ASSIST). It included questions relating to conceptions about learning, approaches to studying, and preferences for different types of courses and teaching. In addition, students were asked to respond to general questions regarding the design of the revised curriculum. The scores for the deep and strategic learning approaches were higher for students studying under the previous curriculum compared to the revised curriculum, despite the fact that the revised curriculum was specifically designed to foster deep learning. The scores for the surface learning approach were lower in the students studying the revised curriculum compared to students studying under the previous curriculum. We identified the following factors affecting student learning: alteration of learning activities, such as problem-based learning, from the recommended models; a lack of instructor support for the revised curriculum; assessments that were not aligned to encourage critical thinking; and directed self-learning activities that were too comprehensive to complete in the allotted time. The results of this study can be used to improve the implementation of student-centered and inquiry-based curricula by identifying potential problems that could prevent a deep learning approach in veterinary medical students.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".