Determining Critical Success Factors for Quality and Accreditation through Delphi Technique
Bibliographic record
Abstract
This is an exploratory study which inquires and investigates the difficulties associated with Quality Assurance (QA) and Program Accreditation. The study highlights specific issues faced in the adoption and implementation of QA standards, sub standards and criteria. It identifies a few critical success factors (CSFs) and indigenised QA tools for program accreditation in Saudi Universities. The CSFs include Stakeholders’ involvement, Top management support, orientation of staff with QA processes and standards, defining administrative procedures of accreditation, continuous quality improvement and assimilation of QA processes in day-to-day environment. Concurring with the Saudi Vision 2030 and National Transformation Program 2020, this study took the empirical approach and collected data from documentations and guidelines related to the National mission of QA and Accreditation initiated by agencies like National Centre for Academic Assessment and Accreditation (NCAAA) and Saudi Arabia Quality Framework (SAQF). This study suggests using the Delphi technique to evaluate the current scenario and ensure predictability judgments for a successful implementation of CSFs and best practices. As a group communication technique, the Delphi technique ideally suited this study making use of a group of individuals (e.g Faculty, Assessors) engaging themselves in resolving complex issues through a consensus. The Delphi technique is also indispensably relevant where no historical data exist, as many programs in Saudi universities are still not accredited. Finally, the Delphi Technique is also a method that helps identify risks, reduce bias in the data and estimate the outcome of events, truly representing predictability and versatility. The implications of this study include offering guidelines to programs and institutions undergo an accreditation process, by identifying true CSF and best practices.
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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 | 0.070 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".