Investigation of a Questionnaire Used to Measure Self-Perception of Self-Regulated Learning in Veterinary Students
Bibliographic record
Abstract
In the United States, the veterinary medical curriculum is 4 years, and at most institutions, no more than one-third of that time is devoted to clinical training, meaning that graduates must continue learning post graduation. Additionally, practicing veterinarians must keep up with new discoveries and techniques in the veterinary medical field and may also choose to pursue specific interests or specialties post graduation. For these reasons, it is essential that veterinarians be competent, self-regulated, life-long learners. Despite agreement regarding the importance of self-regulated learning (SRL) for veterinary professionals, there is currently a paucity of data available on self-regulated learning in veterinary students. The Self-Regulated Learning Perception Scale (SRLPS) is a 41-item instrument that has been previously validated in other graduate student populations, including medical students. It addresses four domains of self-regulated learning, including motivation and action to learning, planning and goal setting, strategies for learning, and assessment and self-directedness. For this project, we hypothesized that the SRLPS would have high reliability among veterinary students. As part of a larger online survey, 82 veterinary students (years 1-4) voluntarily completed the SRLPS. The instrument was generally internally consistent, with the dimensions "Motivation and action to learn," "Planning and goal setting," "Strategies for learning and assessment," and "Lack of self-directedness" having Cronbach's alpha values of .73, .8, .87, and .63 respectively. The SRLPS could have broad applications in veterinary educational practices and research, including assessing impact of courses on professional development and/or coaching/mentoring programs and better understanding short- and long-term educational and career outcomes for veterinarians.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".