A course-based approach to conducting program review
Bibliographic record
Abstract
Abstract External program review is often considered the “gold standard” approach to evaluating academic programs in higher education. Despite the importance of program review, skepticism persists regarding its ability to meaningfully engage faculty and have sustaining impact on program improvement. This paper builds on the academic Program Review Learning Community conceptual model and presents a process for applying a project management facilitation technique that leverages a course structure. The Program Review Course allows for multiple programs to be reviewed concurrently as part of an interdisciplinary cohort. Within the cohort, faculty teams gain access to templates, information sessions, workshops, and discussion forums, with opportunities for engagement both within and across multiple disciplines. Using learning modules, this approach is designed to create efficiency and collaboration in delivering program review tools and customized supports. Utilizing a course structure for program review fits well with a new way to think about program review because successful learning communities are often embedded within institutional structures. Use of this approach is expected to increase clarity and consistency, improve administrative feasibility, provide guidance for those new to program review, promote collegiality within and across programs, and involve team members in the creation of action planning. The authors propose that a course-based approach for conducting program review, with teams of disciplinary faculty joining an interdisciplinary cohort and supported by quality assurance practitioners and educational developers is a novel way to provide structure and academic development while meeting legislative requirements.
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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 | 0.100 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
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".