Application of Saudi’s National qualifying Framework in System Analysis & Design Course
Bibliographic record
Abstract
In higher education, research on quality assurance is one of the prominent fields at present. The National Qualifications Framework (NQF) is an important element in accreditation and quality assurance system in the Kingdom of Saudi Arabia. It is designed by National Commission for Academic Accreditation and Assessment (NCAAA) to ensure that the quality of higher education is equivalent to high international standards. In Saudi Arabia, quality assurance is still a relatively new concept and the Saudi universities seem not to effectively implement it because of certain obstacles. Curriculum development using NQF is one of the core and challenging contexts in quality assurance. This paper presents an application of Qualification Framework in curriculum development for system analysis and design course at Jazan University in Saudi Arabia. The objective of the research shall be to present a model course after applying NQF standards. The research shall begin with identification of the problems, finding out the reasons and to present a model curriculum. The research shall include a literature review. The method of research shall be descriptive, empirical and qualitative approach. Document analysis – mainly NCAAA guides and brainstorming interactions with the educators shall be used as a research instrument. The paper is expected to help educators in better planning of their course learning outcomes and most importantly helps in mapping the assessment methods and questions. This will also help to assess and ensure that the graduates’ knowledge level and skills acquired are as defined in the learning outcomes in the curriculum. The educators can use this paper as a model to apply NQF for the curriculum development of other courses at higher education level.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".