Rethinking the Learning and Evaluation Environment of a Veterinary Course in Gross Anatomy: The Implementation of an Assessment and Development Center and an E-Learning Platform
Bibliographic record
Abstract
Today's students belong to an interactive generation and receive information through multiple channels. In addition, veterinary medicine curricula are changing due to trends such as student-centered education and competence-based learning. In consequence, we were stimulated to rethink the way in which veterinary gross anatomy was taught and assessed. As a first step, the learning goals for the students participating in the veterinary gross anatomy course were clearly defined. Students had to acquire knowledge of and insight into the structure, the function, and the interrelationships of gross anatomical structures in various species. They also had to be competent in observing, palpating, and exposing the anatomical structures. Additionally, they had to attain some general skills and attitudes. Next, a learning environment was developed enabling students to accomplish these goals. The three main components of this new environment were, first, the reorientation of classic cadaveric dissections towards attaching an increased importance to the attainment of course-specific and general skills and attitudes; second, the incorporation of an e-learning platform; and third, an increase in the number of student-lecturer interactions during lecture hours. Finally, the assessment and examination were adjusted to complement the goals defined earlier and the redesigned learning environment. An assessment and development center (ADC) was introduced, where students demonstrated their skills and insights by completing job-relevant assignments within a set time limit. This ADC was used as a means of evaluating students as well as of giving them feedback. Students were enthusiastic about this way of teaching although they experienced it as difficult.
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.
How this classification was reachedexpand
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 | 0.006 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".