Institutional change through departmental quality assurance self-studies
Bibliographic record
Abstract
Purpose In 2010, the Ontario Universities Quality Assurance Council was established and became responsible for monitoring the quality of university programs, and each university was tasked with establishing institutional quality assurance purposes. The purpose of this study is to evaluate the effectiveness of the quality assurance process at facilitating change at one Canadian institution. Design/methodology/approach To better understand the impacts of quality assurance, the authors analyzed 39 self-study documents, which were completed for all academic programs at Queen’s University. Focus groups were also conducted with key stakeholders to gain more insights into the institutional change that resulted from completing these self-studies. Findings After the analysis of the self-studies and focus groups, three themes emerged as impacts of completing self-studies: teaching and learning, identity and collaboration and resource allocation and strategic planning. This study demonstrates that self-studies completed by departments have value beyond simply meeting the provincial mandate, as they are effective in catalyzing positive institutional change. Research limitations/implications The self-study documents were created for the purpose of institutional quality assurance process, not this research study, therefore limiting the data that could be collected. Practical implications Four considerations are provided at the end of this study to spark conversations at other institutions when reviewing and assessing the impact of their quality assurance processes. Originality/value To the best of the authors’ knowledge, this is the first time self-studies have been analyzed to evaluate the quality assurance process.
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 | 0.072 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".