Students Needing Remediation in Pre-Clinical Course Failures in a DVM Program: A 10-Year Analytic Study
Bibliographic record
Abstract
Remediation of pre-clinical course failures in the DVM program at Purdue University College of Veterinary Medicine began in 2010. We set out to understand whether some students were more likely than others to use remediation opportunities and succeed. Student demographics, undergraduate (UG) experiences, including institution attended and major studied, UG performance as measured by grade point average (uGPA), and extent of academic difficulties in DVM years 1−3 were studied at univariate levels to determine which students more often failed ≥1 courses, remediated ≥1 courses, and were successful in all remediation attempts. Among 815 students in DVM classes 2014–2023, 157 failed ≥1 courses. Risk factors associated with failing ≥1 courses and with unsuccessful remediation were identified using multiple logistic regression analysis. Unsuccessful remediation, resulting in student's academic attrition, was defined as not succeeding at remediation of all failed courses, including being ineligible for or not attempting remediation. Risk factors were considered statistically significant at p value <0.05. Lower uGPA, having attended a minority-serving institution, and being an underrepresented minority or an international student were associated with increased likelihood of failing ≥1 courses. However, the only factors associated with unsuccessful remediation were failing ≥3 courses in DVM years 1–3 and failing at least one course in DVM year 1. No demographic or UG educational background is associated with unsuccessful remediation. Taken together, our models suggest that being at risk of failing ≥1 courses in DVM years 1–3 did not inevitably put students at risk of attrition when remediation opportunities were provided. However, an increasing number of course failures and failures beginning in DVM year 1 increased the risk of unsuccessful remediation. Early intervention to minimize academic difficulties in DVM program may mitigate risk of student attrition.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".