Self-Regulated Learning in Early-Year Veterinary Students: Analyzing Strategy Usage and Strategy Knowledge in Anatomy Classes
Bibliographic record
Abstract
Self-regulated learning (SRL) is critical in enabling students to manage extensive learning material effectively. However, the transition from secondary to tertiary education presents significant challenges for students as the demands on their SRL skills increase substantially. In veterinary education, this is particularly evident in anatomy. A better understanding of early-year students' SRL strategy knowledge and SRL strategy usage is needed to design effective interventions. Here we conducted cross-sectional and longitudinal analyses. The cross-sectional approach aimed to investigate: 1a) levels in SRL strategy knowledge and usage; 1b) relations between strategy knowledge and usage; and 1c) their relation to academic achievement. Furthermore, differences between first- and third-semester students were analyzed. The longitudinal approach aimed to: 2) investigate changes in strategy knowledge and usage during one semester. A sample of N = 181 veterinary anatomy students (108 first and 73 third semester) completed an SRL strategy knowledge test and an SRL strategy usage self-report questionnaire. Sixty students filled out both instruments one semester later. Results showed: 1a) moderate levels of knowledge and usage; 1b) low-to-moderate correlations between knowledge and usage; and 1c) a moderate correlation between usage and achievement. First- and third-semester students only differed in the relation of usage to achievement. Furthermore, 2) motivational strategy usage increased while cognitive and metacognitive strategy usage decreased over one semester, but there were no changes in strategy knowledge. Based on our results, it seems necessary that early-year students not only need training to enhance strategy knowledge but also on how to transfer this knowledge to their everyday study life, especially practical settings.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".