Observations of Veterinary Medicine Students’ Approaches to Study in Pre-clinical Years
Bibliographic record
Abstract
RATIONALE FOR THIS STUDY: This study has two purposes. The first is to explore an instrument of evaluation of the approaches to study (deep, strategic, and surface) adopted by students in the pre-clinical years of their veterinary degree program. The second is to examine relationships between these approaches and a broad range of further factors deemed relevant to the veterinary medicine context. We envisage that a greater knowledge of how these students learn will aid curriculum reform in a way that will enrich the learning experience of veterinary students. METHODOLOGY: A questionnaire consisting of the 52-question Approaches to Study Inventory (ASI) and an additional 49 questions relating mainly to teaching, assessment, and study skills was distributed to 215 veterinary medicine (MVB) students in their pre-clinical years of study. Factor analysis was used to ensure that the ASI section of the questionnaire maintained previously reported structure. The internal reliability of the approaches measured was tested using Cronbach alpha analysis. The approaches were described as frequency distributions. Associations between the parameters (deep, strategic, and surface) and 49 additional context-specific factors were investigated using loglinear analysis. RESULTS: (1) Factor analysis revealed that the integrity and structure of the instrument in this context was generally comparable to previous studies. (2) The impact of a high workload was evident in the surface approach, with fear of failure becoming a strong motivating factor and syllabus boundness a widely used strategy. (3) Associations made between the approaches and 49 context-specific factors showed strong associations between both workload and lack of prior knowledge with the surface approach. (4) Grades were associated positively with both the deep and strategic approaches but negatively with the surface approach. (5) A range of learning and study skills were associated positively with the deep and strategic approaches and negatively with the surface approach. CONCLUSION: The ASI proved to be a reliable and insightful instrument, highlighting specific surface learning tendencies present in the group as well as a deep learning approach, the pattern of which deviates from previous studies on this subject. This study also confirms the value of some teaching practices as a means of supporting deep learning and perhaps challenging surface learning strategies. The prevalent perception of a high workload is notable, as is its positive association with surface learning.
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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 | 0.003 | 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".