Comparison of Traditional Lecture-Based Learning versus Interactive Electronic Book Learning in Veterinary Student Comprehension of Inhalant Anesthetic Administration, Uptake, and Elimination
Bibliographic record
Abstract
The administration, uptake, and elimination of inhalant anesthetics is a challenging topic in the veterinary curriculum, and lecture-based learning is often insufficient to ensure that students understand these concepts. We hypothesized that the use of an interactive electronic book (e-book) would enhance student comprehension of the material. Two sequential Doctorate of Veterinary Medicine student cohorts participated in a prospective controlled study. The first cohort received traditional lecture-based learning while the second cohort was taught the topic using an interactive e-book. Student comprehension of the material was assessed twice during the course via multiple-choice questions: five questions in a midcourse quiz and seven within the final exam. At the end of the course, students also completed a Likert survey assessing their confidence regarding the topic. Averaged across assessment types, students taught using the interactive e-book scored higher than those taught via the traditional method (p < .001). Final exam scores were significantly higher in the e-book cohort compared with the lecture-based cohort (p < .001). However, there was no difference in quiz scores between groups (p = .109). No significant difference was found between groups in responses to the Likert survey. In conclusion, students using the interactive e-book had better comprehension of the material than students in the traditional lecture group as measured by their scores on multiple-choice question assessments. Future studies are needed to determine whether this advantage persists later in the curriculum when students apply these concepts in the clinical year.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".