Impact of Satisfactory/Unsatisfactory Grading on Student Motivation to Learn, Academic Performance, and Well-Being
Bibliographic record
Abstract
Satisfactory/unsatisfactory (S/U) grading is often proposed to ameliorate stress by reducing the competitive nature of letter grading. Though explored considerably in human medical programs, minimal literature focuses on the veterinary school setting. The purpose of this study was to evaluate the impact of S/U grading on veterinary students' motivation to learn, academic performance, and well-being. Cornell University's COVID-19 pandemic response provided a unique opportunity to compare S/U and letter grading on the same population of students during a single pre-clinical foundation course, with the first half being graded S/U (spring semester 2020), returning to letter grades in the second half (fall semester 2020). Students were retroactively surveyed on the effect of S/U versus letter grading on their overall educational experience and well-being, with 67.8% class participation. The majority of respondents (71.3%) stated that S/U grading had a positive impact on their overall learning experience. More than half (53.8%) perceived that they learned the same amount of information and had the same level of motivation (58.8%), even though most (61.3%) stated that they spent less time preparing for S/U assessments than letter grade assessments. Positive impact factor effects for S/U grading included decreased stress, more time for self-care, improved learning, and increased learning enjoyment. S/U grading did not negatively impact academic performance. In conclusion, this study demonstrates that, in our particular study population and setting, S/U grading conferred well-being and learning experience advantages to students without any reduction in motivation for learning or academic performance.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".