Practical Classes: A Platform for Deep Learning? Overall Context in the First-Year Veterinary Curriculum
Bibliographic record
Abstract
The aim of this study is to evaluate the many practical formats that support the first-year veterinary curriculum. These practical classes are diverse in content and style. They include laboratory-based formats, classes involving live animals and cadavers, classes conducted using computer-aided learning tools, study groups, and information technology training. This preliminary study examines ratings for these practical classes, but also relates these ratings to students' approaches to study with the aim of understanding how a deep learning approach manifests itself in the practical setting. The diverse behaviors and attitudes to practical classes are also evaluated in the light of the approaches to study. A questionnaire that evaluated (1) a total of 24 practical classes, (2) the 52-item Approaches to Study Inventory, and (3) 13 behaviors within and attitudes to practical classes was distributed to 69 first-year veterinary students in their final term. Practical classes that involved live animals and cadavers were rated most positively by this group of students. These ratings, however, did not correlate significantly with the deep or surface learning score. The majority of practical classes where the ratings were found to be associated with deep and surface learning were laboratory-based, although overall these practical classes tended to be rated lower than those involving animals. Ratings did not correlate significantly with the strategic approach. A number of behaviors and attitudes to practical classes were also found to be positively and significantly (p=0.0001) associated with the deep learning approach. This preliminary study indicates that this cohort of veterinary students has an overall positive perception of practical classes that permit contact with live animals or cadavers. Although the perception of laboratory-type practical classes was lower overall, the ratings for these practical classes appeared to be influenced by their deep and surface learning scores. We hypothesize that these approaches influence student engagement with and appreciation of laboratory-type classes, but not of classes involving live animals or cadavers. This would suggest that a different "type" of learning is taking place in these different contexts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".