Sustaining a Collegewide Teaching Academy as a Community: 10 Years of Experience With the Master Teacher Program at the University of Tennessee College of Veterinary Medicine
Bibliographic record
Abstract
On the basis of strategic initiatives and an evolving focus on educational program enhancement, faculty and staff members designed a structured program to provide leadership and resources for improving instruction and disseminating educational scholarship in a veterinary college. The University of Tennessee College of Veterinary Medicine Master Teacher Program was conceptualized in the teaching academy model as a forum for professional development and dialogue. A small leadership team worked with other faculty to develop the program proposal, including its mission, vision, values, and initial goals. Programming includes monthly meetings to discuss a range of policy, theoretical, and practical topics, as well as periodic workshops focused on current strategic initiatives or hot topics. Ten years later, the program continues to successfully connect educators and have an impact on the college and profession. Attendance has grown steadily; feedback has been positive. Participants rate the program's overall value highly, particularly its impact on their teaching, the opportunity to stay current in educational topics, and the opportunity to learn from colleagues. Discussions within the program have provided valuable input for college policy and practices. Keys to sustained success have included a historical culture emphasizing the teaching mission, alignment with college goals, consistency in leadership and programming, periodic revisions and rebranding, and attention to new faculty and staff needs. This article describes the development, growth, and perceived impact of the program and emphasizes lessons learned and actions taken to sustain its success without additional personnel and at minimal cost.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".