A Model to Build Capacity through a Multi-Program Curriculum Review Process
Bibliographic record
Abstract
Curriculum reviews are becoming more prevalent in higher educational institutions as a means to address quality assurance and improve program offerings. However, the review process can be structured so that instructors experience professional learning benefits as they work with program-level learning outcomes, map their courses, and analyze curriculum data with their colleagues. This paper shares an approach that was used to conduct a 1-year, complex, multi-program curriculum review in a faculty’s graduate unit. This approach enhanced the instructors’ continuing growth and their ability to carry out a curriculum review. To illustrate the dynamic nature of the curriculum review process, a three-level and three-phase curriculum review model has been developed.Based on our experience when implementing the model with an array of instructional teams, we identified four key recommendations for practice that promoted a professional learning environment while implementing a multi-program curriculum review: (1) mentoring and distributed leadership, (2) standardizing flexible structures and processes, (3) customizing the process for deep inquiry, and (4) collaborating. Curriculum reviews are becoming more prevalent in higher educational institutions as a means to address quality assurance and improve program offerings.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".