A Neo-Institutionalist Approach to Understanding Drivers of Quality Assurance in ODL
Bibliographic record
Abstract
In recent years, quality assurance (QA) in higher education has received increasing attention by academics, learners, institutions, and governments alike. Many open universities (OUs) have taken steps to re-define or re-orient their systems and practices to integrate quality. While there is a growing body of literature on QA best practices, there has been little investigation into the factors that influence institutions to improve or adopt QA and how these factors impact on the specific manifestations of institutional QA. This paper examines the challenges of QA implementation in OUs and, using a neo-institutionalist lens, it advances a framework for understanding drivers of institutional QA implementation. The framework is applied to the case of the Open University of Mauritius (OUM). Existing literature, institutional records, interviews and reports are analysed to assess how exogenous and endogenous factors have influenced QA implementation at OUM, with a focus on addressing the specificities of open and distance learning (ODL). A better understanding of the drivers of change for QA can help OUs plan the implementation of QA mechanisms in a more comprehensive way and to systematically develop a culture of quality that responds to the ideological and practical context of ODL.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".