Cyclical Program Reviews at Smaller Post-Secondary Institutions: Can the Time and Effort be Justified?
Bibliographic record
Abstract
Cyclical program reviews (CPRs), also called periodic or academic reviews at institutions of higher education, are undertaken to ensure that academic programs meet a variety of objectives related to teaching and learning, as well as professional credentialing, quality assurance, and institutional requirements. Preparing, reviewing, and implementing a CPR requires significant time and effort for those assigned to this task, especially if the program has never previously been through a CPR. Much has been written on how to undertake a CPR (Bresciani, 2006), what measures are useful in assessing programs (Jayachandran et al., 2019), and some of the problems that external reviewers encounter with CPRs (Halonen & Dunn, 2017). This article, however, provides new insights concerning important considerations that should be addressed when preparing to undertake a CPR—from the perspectives of both administrators and faculty at smaller institutions where the number of faculty may be small and resources for the CPR process are often limited. Drawing on a case study of CPRs in several social sciences programs and a broader survey of those involved in CPRs from 2015-2020 at a small Western Canadian university, the authors identify key issues in preparing a CPR, such as the timing of the review, the advantages and disadvantages of an individual approach versus a team approach in preparing the CPR, the role of administrators in the CPR process, the importance of institutional templates in preparing the CPR, and the need for clearly identified program learning outcomes to guide the CPR process. This article also examines how a pandemic can impact the CPR process.
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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 | 0.281 | 0.607 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".