Monitoring the Curriculum through the Student Perspective
Bibliographic record
Abstract
Student input was deliberately included as part of the curriculum implementation and assessment plan at the University of Minnesota College of Veterinary Medicine. The new curriculum included design features to encourage deeper learning such as a spiral curriculum with cross-course integration, increased open time, and more active learning. Student well-being was seen as a simultaneous need. To gather overall perceptions of workload and well-being, student volunteers from each cohort were surveyed weekly starting in 2013. Survey questions asked about out-of-class work time, level of integration, extracurricular activities, student well-being habits, paid employment, and other factors. Survey questions were combined with course data to get a full picture of week quality, total course work time, extracurricular activities, and the effects of integration. Many of our hypotheses about curricular and extracurricular impacts on week quality were disproven. Week quality was most positively affected by student factors of sleep and exercise, whereas the curricular factors of out-of-class work time, total course work time, and examination hours had the strongest negative effects. A surprising finding was that open time, in-class hours, and paid employment hours had a minimal effect on week quality. Students identified excessively heavy semesters and uneven semester workloads that resulted in early revisions to the new curriculum. Student feedback provided a view of the curriculum that was not otherwise available and resulted in early and significant impacts on the new curriculum, and they provided insight into whether planned changes had occurred and how effective various factors were in reaching the curricular goals.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".