The Educational Efficacy of Humane Teaching Methods: A Systematic Review of the Evidence
Bibliographic record
Abstract
Humane alternatives to harmful educational animal use include ethically-sourced cadavers, models, mannequins, mechanical simulators, videos, computer and virtual reality simulations, and supervised clinical and surgical experiences. In many life and health sciences courses, however, traditional animal use persists, often due to uncertainty about the educational efficacy of humane alternatives. The most recent comprehensive reviews assessing learning outcomes of humane teaching methods, in comparison to harmful animal use, were published more than 10 years ago. Therefore, we aimed to collate and analyse the combined evidence from recent and older studies about the efficacy of humane teaching methods. Using specific search terms, we systematically searched the Web of Science, SCOPUS, and EMBASE databases for relevant educational studies. We extracted information on publication years, the country in which the study was conducted, field, humane teaching methods, form of learning outcome assessment, and the learning outcome of the humane teaching methods, in comparison with harmful animal use. We found 50 relevant studies published from 1968-2020, primarily stemming from the USA, UK, and Canada. Humane teaching methods produced learning outcomes superior (30%), equivalent (60%), or inferior (10%) to those produced by traditional harmful animal use. In conclusion, a wide-spread implementation of humane teaching methods would not only preserve learning outcomes, but may in fact be beneficial for animals, students, educators, and institutions.
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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 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".