Quality Management in Higher Education: Review and Perspectives
Bibliographic record
Abstract
This paper is a review which presents a summary of 52 studies from 2006 to 2016 in Quality Management (QM) within Higher Education Institutes (HEIs). The aim of this paper is to submit evidence regarding the level of QM in HEIs, particularly in developing countries, and also to enhance the research in the field of QM. The findings reveal that from 2013 onward there is an increased interest in the items of QM mainly in Arabic countries. Moreover, the findings include Critical Success Factors (CSFs), obstacles and benefits that confirm and supplement previous literature. The type (private or public) and age of university, transformational leadership, integration, respect of a person, character, constructive conflict, creative tension, enthusiasm, awareness and orientation of employees and faculty and resource allocation are CSFs that this study reveals. Also, infrastructure limitations focused on human and financial capital, limited involvement of stakeholders and measurement of a complex range of performance indicators are barriers which enrich the analysis. Moreover, the extra benefits of QM practices are that QM is appropriate to the purpose of HEIs, meets the expectations and the new roles of HEIs, and lastly, the implementation of QM practices can solve problems and propose solutions.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".