Specifications Grading in a Cardiovascular Systems Course: Student and Course Coordinator Perspectives on the Impacts on Student Achievement
Bibliographic record
Abstract
This study investigated students’ and a course coordinator’s perceptions about specifications (spec) grading in a cardiovascular systems course and assessed its effects on student performance. Spec grading was hypothesized to result in lower perceived student stress about the course, improved student performance, and less work for the course coordinator. The study used a mixed methods approach consisting of student pre-, peri-, and post-course surveys; student focus group interviews; analysis of student course evaluations; and course coordinator reflection. Participants were from a cross-section of one course in the veterinary professional curriculum. Results demonstrated significantly more A grades assigned to students than in the previous year’s course, where traditional grading was used ( p = .024). The focus group produced two primary themes: pros and cons. Pros included flexibility, student control over grades, generally lower perceived stress, opportunities to resubmit assignments, and more motivation to learn. Cons included confusion about the process, some disorganization, perceived higher workload for the professor, and communication concerns. The course coordinator’s positive perceptions included students being less combative about grades than with the traditional system, students appreciating opportunities to resubmit assignments, and students demonstrating improved learning outcomes. Negative course coordinator perceptions were that reduced student stress was inconsistently achieved and that there was increased time commitment compared with traditional grading. Course evaluation themes included skepticism about spec grading in the beginning, varying stress experiences, improved learning, and increased workload. In conclusion, spec grading variably reduced student stress and did not result in less work for the course coordinator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".