Impact of Virtual Patients as Optional Learning Material in Veterinary Biochemistry Education
Bibliographic record
Abstract
Biochemistry and physiology teachers from veterinary faculties in Hannover, Budapest, and Lublin prepared innovative, computer-based, integrative clinical case scenarios as optional learning materials for teaching and learning in basic sciences. These learning materials were designed to enhance attention and increase interest and intrinsic motivation for learning, thus strengthening autonomous, active, and self-directed learning. We investigated learning progress and success by administering a pre-test before exposure to the virtual patients (vetVIP) cases, offered vetVIP cases alongside regular biochemistry courses, and then administered a complementary post-test. We analyzed improvement in cohort performance and level of confidence in rating questions. Results of the performance in biochemistry examinations in 2014, 2015, and 2016 were correlated with the use of and performance in vetVIP cases throughout biochemistry courses in Hannover. Surveys of students reflected that interactive cases helped them understand the relevance of basic sciences in veterinary education. Differences between identical pre- and post-tests revealed knowledge improvement (correct answers: +28% in Hannover, +9% in Lublin) and enhanced confidence in decision making ("I don't know" answers: -20% in Hannover, -7.5% in Lublin). High case usage and voluntary participation (use of vetVIP cases in Hannover and Lublin >70%, Budapest <1%; response rates in pre-test 72% and post-test 48%) indicated a good increase in motivation for the subject of biochemistry. Despite increased motivation, there was only a weak correlation between performance in final exams and performance in the vetVIP cases. Case-based e-learning could be extended and generated cases should be shared across veterinary faculties.
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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 | 0.003 | 0.011 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".