Educational Research Report Veterinary Educator Teaching and Scholarship (VETS): A Case Study of a Multi-Institutional Faculty Development Program to Advance Teaching and Learning
Bibliographic record
Abstract
Content expertise in basic science and clinical disciplines does not assure proficiency in teaching. Faculty development to improve teaching and learning is essential for the advancement of veterinary education. The Consortium of West Region Colleges of Veterinary Medicine established the Regional Teaching Academy (RTA) with the focus of "Making Teaching Matter." The objective of the RTA's first effort, the Faculty Development Initiative (FDI), was to develop a multi-institutional faculty development program for veterinary educators to learn about and integrate effective teaching methods. In 2016, the Veterinary Educator Teaching and Scholarship (VETS) program was piloted at Oregon State University's College of Veterinary Medicine. This article uses a case study approach to program evaluation of the VETS program. We describe the VETS program, participants' perceptions, participants' teaching method integration, and lessons learned. A modified Kirkpatrick Model (MKM) was used to categorize program outcomes and impact. Quantitative data are presented as descriptive statistics, and qualitative data are presented as the themes that emerged from participant survey comments and post-program focus groups. Results indicated outcomes and impacts that included participants' perceptions of the program, changes in participant attitude toward teaching and learning, an increase in the knowledge level of participants, self-reported changes in participant behaviors, and changes in practices and structure at the college level. Lessons learned indicate that the following are essential for program success: (1) providing institutional and financial support; (2) creating a community of practice (COP) of faculty development facilitators, and (3) developing a program that addresses the needs of faculty and member institutions.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".