Implementing Quality Assurance in Saudi Arabia: A Comparison between the MESO and the MICRO Levels at PSU
Bibliographic record
Abstract
Quality assurance in higher education remains to be one of the most prominent fields of research at the present. In the Saudi Arabian higher education institutions (HEIs), quality assurance is a relatively new concept and Saudi universities seem not to effectively implement quality assurance caused by the certain obstacles. As such, there are two objectives to be addressed; first, to explore the current quality assurance mechanisms. Second, to identify factors that enhances or hinder the effectiveness of the internal quality assurance system in Saudi Arabian HEIs. A case study involving Prince Sultan University was used to examine these questions. Data was collected using semi-structured interviews with both meso and micro levels, as well as document analysis and observation. A grounded theory approach based on that advocated by Strauss and Corbin was taken to analysis the data. The findings of this study support the perceived use of many different standards based evaluative processes, which provide feedback from the various stakeholder perspectives. The findings also demonstrate perceived supportive factors of the commitment/support of leadership and management, awareness and orientation of employees/faculty. In addition, the findings also report that/faculty resistance and infrastructure limitations focused on financial and human capital constraints were perceived as inhibitive factors to QA.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".