Quality Improvement, Quality Assurance, and Benchmarking: Comparing two frameworks for managing quality processes in open and distance learning
Bibliographic record
Abstract
<P>Managing quality processes become critically important for higher education institutions generally, but especially for institutions involved in open and distance learning. In Australia, managers of centers responsible for open and distance learning have identified two frameworks that potentially offer ways of conceiving of the application of quality processes: the Quality Framework published in Inglis, Ling, and Joosten (1999); and the Benchmarking Framework published in McKinnon, Walker, and Davis (2000). However, managers who have been considering applying one or other framework within their institutional contexts have had to face the issue of how they should choose between, or combine the use, of these frameworks. Part of their dilemma lies in distinguishing among the related functions of quality improvement, quality assurance, and benchmarking. This article compares the frameworks in terms of their scope, institutional application, structures, and method of application, and then considers what implications the similarities and differences between the frameworks have for their use.</P>
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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 | 0.103 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".