Replacing a Veterinary Physiology Endocrinology Lecture with a Blended Learning Approach Using an Everyday Analogy
Bibliographic record
Abstract
Understanding scientific concepts and processes is critical for veterinary education. This article outlines the impact of blended learning and the use of an analogy on student understanding of the hypothalamic-pituitary-target gland axis over a three-year period. The first-year veterinary physiology course at our institution was modified to incorporate a blended learning approach. An analogy centered around a fast-food restaurant was introduced via an animated video to explain key concepts using an online module. Students completed the module on their own time and class time was optional for asking questions or obtaining clarification as needed. Learning was assessed using the same set of multiple-choice exam questions (MCQs). As hypothesized, students using the online module performed equally well (significantly better for those in the lower quartile) on three summative MCQs to those who received the same information delivered by traditional lecture. Student feedback identified positive aspects regarding blended learning using the analogy, including dynamic visuals, ability to work at their own time and pace, and ease of repeating information. Students cited lack of discipline and poor time management as obstacles to completing the module. Changing the anatomy and physiology of the hypothalamus and pituitary gland from static images and text to an animated video significantly improved student's preference for the blended learning approach. Blended learning and the analogy was preferred by 47% of students over the traditional lecture format (21% preferred traditional lecture and 32% were indifferent) and it was more effective in helping students master this important physiological concept.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".