Direct Measurement of Veterinary Student Learning Outcomes for the NAVMEC Professional Competencies in a Multi-User Virtual Learning Environment
Bibliographic record
Abstract
Education in veterinary medicine, as in other allied health care-health science professions and academia in general, has been subject to the public call for accountability for the quality of its student learning outcomes. A principal stakeholder in veterinary medicine is the American Veterinary Medical Association-Council on Education (AVMA-COE). AVMA-COE has adopted program accreditation standards requiring veterinary colleges to provide evidence that they are measuring and assessing the clinical competency of students before graduation and again shortly after graduation. Schools and colleges are required to develop relevant measures to validate scientific knowledge, skills, and values aligned with North American Veterinary Medical Education Consortium (NAVMEC) core competencies. Beginning in May 2012, the College of Veterinary Medicine and Biomedical Sciences at Texas A&M University modified the professional veterinary medical curriculum by including a required clinical rotation centered on veterinary emergency preparedness and response. A distinguishing major component of the instructional design of the clinical rotation includes Second Life, a commercially obtained computer-generated multi-user virtual simulation learning environment. The virtual reality situations require high-volume, mass-casualty medical triage decision making. The interpersonal communications and interactivity among students, faculty, and third-party actors enable faculty and instructor observers and simulation facilitators to evaluate students actively engaged in critical thinking and complex problem solving while demonstrating skill in the NAVMEC professional competencies. The Second Life virtual simulation has been adopted as a primary tool for direct measurement of student learning objectives outcomes achieved in this clinical rotation and is being implemented in other clinical teaching platforms.
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 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".