The Transition from Letter Grading to Modified Pass/Fail Grading at a College of Veterinary Medicine: A Narrative Inquiry of Student Experiences
Bibliographic record
Abstract
Compared with traditional letter grading, pass/fail grading is an assessment approach that can alter the educational environment and enhance student well-being without compromising education quality. Little is known about the experiences of students during the transition from traditional grading to pass/fail grading. The onset of the COVID-19 pandemic resulted in an abrupt move to modified pass/fail (MPF) grading at North Carolina State University College of Veterinary Medicine (NCSU-CVM), followed by a decision to permanently adopt MPF grading for the entire core pre-clinical doctor of veterinary medicine (DVM) curriculum. This study employed a narrative inquiry of surveys and interviews to facilitate deep understanding of student perspectives during the transition to MPF grading. Focus was placed on understanding what this transition meant for DVM students in terms of life and learning quality. Our analysis identified seven key themes that captured student experiences, which were generally positive, during this transition: education culture, student perceptions of instructor impact, shift from extrinsic to intrinsic valuation of curricular content, competitiveness for external merit-based opportunities, use of letter grading and MPF in a single semester, student recommendations, and well-being. Through exploration of these themes and presentation of concerns identified in students' stories, this study provides guidance for other programs considering revision of their own assessment frameworks.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".