Impact of Virtual Patients as Optional Learning Material in Veterinary Biochemistry Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it