Methods Used to Assess Student Performance and Course Outcomes at the National Center of Excellence in Dairy Production Medicine Education for Veterinarians
Bibliographic record
Abstract
Between 2012 and 2014, three cohorts of senior veterinary students participated in an 8-week dairy production medicine course created by the National Center of Excellence in Dairy Production Medicine Education for Veterinarians. One goal of this course is to better prepare veterinary students to serve the increasingly complex needs of the dairy industry. In this article, we describe the assessment methods and student performance outcomes of those first three cohorts. A combination of assessment methods was used, including pre- and post-testing; instructor observations and scores on individual and group projects, including a final integrative project; and peer evaluation. Student feedback, collected via anonymous survey, provided insight into students’ perceptions about the course and their learning. Performance and feedback suggest that the course was successful in preparing students for careers using skills in dairy production medicine. Pre- and post-testing was conducted for most topic modules in the course. The mean (median) pre- and post-test scores were 47% (50% ) and 83% (88%), respectively. The mean improvement in score was significant ( p < .002) for all modules and cohorts. Students indicated a moderate or high degree of confidence in performing dairy production medicine skills after each module. Of students in cohorts 1, 2, and 3, respectively, 55%, 75%, and 82% felt they could provide dairy production medicine services (e.g., records analysis, problem investigation, protocol and standard operating procedure design) either alone or with some mentoring, immediately after graduation. In addition, assessment results and student feedback enabled timely course modifications during these first three cohorts.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".