Making Sense with Students: Student Engagement in Quality Assurance
Bibliographic record
Abstract
The majority of higher education quality assurance policy implementation studies focus on the macro level perspective. This study seeks to address a gap in understanding the full picture of implementation including how universities enact policy in their protocols and internal processes. I focus on student engagement in cyclical review processes, a critical part of quality assurance of academic programs, and use Ontario universities as a case to describe implementation. A conceptual framework combining institutionalism and sensemaking allows me to describe how institutional elements shape policy implementation at different levels of analysis. I find that requirements are articulated in ways that make sense across the system and describe how they are coherently taken up in coherent ways in 20 different university protocols. An embedded case allows me to describe how requirements are enacted in one university. With the support of theory, I show how policy implementation is shaped by organisational structures, norms, and understandings. More pragmatically, the study offers policy makers and university administrators insight into program-level quality assurance models and deepens understanding of student engagement in university processes.
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 | 0.034 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".