Veterinary School Instructor Knowledge of Learning Strategies
Bibliographic record
Abstract
Abstract Over a century of research has documented evidence-based approaches to learning practices that support robust, long-lasting learning. Recent work has queried whether individuals are aware of and implement such best practices in learning, predominantly focusing on self-reports of undergraduate students. Few studies have investigated instructor knowledge of evidence-based learning practices and no prior study has comprehensively surveyed knowledge of evidence-based learning practices among veterinary instructors. In the present study, we surveyed veterinary instructors’ ( N = 355) knowledge of evidence-based learning practices and also asked them to rate the value of strategies described in six learning scenarios. Instructors endorsed a number of evidence-based learning practices (e.g., spacing, creating diagrams, self-testing) but also endorsed other learning practices and principles with little or no support (e.g., learning styles). Further analyses indicated that the number of evidence-based learning practices endorsed was unrelated to the ranking or acceptance rate of the veterinary program. Results from the evaluation of learning scenarios indicated that instructors favored the evidence-based learning practice in less than half of the scenarios. Thus, instructors endorsed a mix of learning strategies with substantial empirical support and others with far less support. Based on these findings, we propose five priority areas for professional education of veterinary instructors that include strategic development of generative activities, spaced practice, sensitivity to cognitive capacity of learners, and effective self-regulated learning.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".