A proposed NCAAA-based approach to the self-evaluation of higher education programs for academic accreditation: A comparative study using TOPSIS
Bibliographic record
Abstract
Quality standards must be fulfilled to satisfy a base level of quality. Despite using this idea as a foundation, evaluations of academic programs still rely on the evaluators' experiences and may differ from one evaluator to the next. As a result, more precise evaluation approaches must be created to ensure quality is accurately reflected. The main goal of this research paper is to propose and evaluate an approach to assessing higher educational programs using the Self-Evaluation Scale (SES) developed by the Saudi National Commission for Academic Accreditation and Evaluation (NCAAA). The proposed approach is a breakdown of the original performance criteria and standards into sub-criteria and elements to ensure the required data quality. The second goal is to compare the NCAAA's original performance criteria and the proposed evaluation sub-criteria. A comparison framework that uses the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is developed. Data from eight programs offered in a Middle Eastern University was used for the application and comparison between the two evaluation approaches. Results show that both approaches provide different quality performance rankings. The proposed approach demonstrated more conservative and accurate overall quality performance ratings, indicating that application decisions for accreditation are affected.
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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".