Curriculum Redesign in Veterinary Medicine: Part I
Bibliographic record
Abstract
Curricular review is considered a necessary component for growth and enhancement of academic programs and requires time, energy, creativity, and persistence from both faculty and administration. At Texas A&M College of Veterinary Medicine & Biomedical Sciences (TAMU), the faculty and administration partnered with the university's Center for Teaching Excellence to create a faculty-driven, data-enhanced curricular redesign process. The 8-step process begins with the formation of a dedicated faculty curriculum design team to drive the redesign process and to support the college curriculum committee. The next steps include defining graduate outcomes and mapping the current curriculum to identify gaps and redundancies across the curriculum. Data are collected from internal and external stakeholders including veterinary students, faculty, alumni, and employers of graduates. Data collected through curriculum mapping and stakeholder engagement substantiate the curriculum redesign. The guidelines, supporting documents, and 8-step process developed at TAMU are provided to assist other veterinary schools in successful curricular redesign. This is the first of a two-part report that provides the background, context, and description of the process for charting the course for curricular change. The process involves defining expected learning outcomes for new graduates, conducting a curriculum mapping exercise, and collecting stakeholder data for curricular evaluation (steps 1-4). The second part of the report describes the development of rubrics that were applied to the graduate learning outcomes (steps 5-8) and engagement of faculty during the implementation phases of data-driven curriculum change.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".