A Strategy for Developing Educational Evaluations for Learner, Course, and Institutional Goals
Bibliographic record
Abstract
The curricula for both veterinary and human medicine are undergoing review and change as a new and highly competitive practice environment influences what abilities graduates require to be successful. Concerning many is the contention that some graduates lack skills and aptitudes necessary for economic success. If significant changes are to be considered for the curricula of either profession, it will be difficult to plan for meaningful change in the absence of high-quality information about the needs of graduates and related curriculum gaps. The purpose of this article is to argue why educators should design more effective systems of evaluation that are responsive to the needs of educational program planning. One example from a medical school is described. In this case, the authors discuss how their institution's evaluations were insufficient for answering new and important questions that go beyond traditional cognitive measures: specifically, no data set was available that allowed the institution to answer questions about practice environment and curricular innovations. More recently, institutions have become interested in learning to what extent their broad missions are accomplished or not. Similarly, academic leaders are not simply interested in performance of learners on tests of competence; they want to know more about how their graduates are doing in the practice setting. To answer questions such as these, educators must expand their systems of evaluations to address these broader themes. The authors conclude by identifying several lessons learned from their experiences in developing a new system of educational program evaluation.
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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 | 0.181 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".