Students’ Approaches to Learning During Pre-Clinical and Clinical Phases of a Veterinary Curriculum, Their Motivations, and Their Correlation with GPA
Bibliographic record
Abstract
This study was conducted to determine if veterinary students adopt a different approach to learning in the clinical compared to pre-clinical phase, and what factors motivate their approach. We also sought to determine if the learning approach adopted correlates with grade point average (GPA). Two questionnaires were administered to the same cohort of students (112 students) at the end of the pre-clinical and at the end of the clinical phase. A total of 87 students completed at least one questionnaire. The questionnaires included the Approaches and Study Skills Inventory for students, which was used to provide scores for three learning approaches: surface (focus on memorization), strategic (focus on optimum grades), and deep (focus on understanding). The questionnaires also included open-ended questions probing for motivations behind adopting learning approaches. Statistical analyses were performed on the data to detect correlations between variables. Students were more likely to adopt a surface approach in the pre-clinical phase than in the clinical phase, although other learning approaches were not different between phases. No strong correlations existed between learning approach and GPA. Students who adopted a deep approach were typically motivated by higher-level motivations than those who adopted a surface approach, especially in the clinical phase. Time constraints, the desire to get good grades, and passing classes were the main reasons for adopting the surface approach. The results of the study can be beneficial for students by allowing them to identify those pressures that could prevent them from adopting a deeper approach earlier in the curriculum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".