Development of a Self-Assessment, Performance Measurement and Quality Insurance Repository Case of Two Higher Education Institutions in Morocco
Bibliographic record
Abstract
The self-assessment repositories are used in a perspective of quality management. They are intended to guide higher education institutions in building their training offer and enable the evaluation and performance measurement based on explicit and consistent objectives. These are essential tools for posterior training evaluation, facilitating a development based on changes affecting the science and socio economic fields.The self-assessment thus enables a diagnosis, and identification of the strengths and possible improvement actions.The purpose of this is to increase the institutional progress capacity and evolution through a self-reflection.In this regard, the aim through this article is the development of a self-assessment repository for the training institutions adapted to the Moroccan higher education specificities. To do this, we first recalled the state of the art in terms of the main standards and benchmarks used as the basis of our research: ISO 29990, ISO 9001, AERES repository, NF Training Service, Aqi- Umed, CTI self-assessment Guide and eduqua Manual 2012. We underlined, then, the self-assessment issues in higher education and the major elements that feed the interest and approach adopted in the case of our study. We presented the proposed repository, including the evaluation axes and criteria, and explained the choice for modifing certain references or criteria related to the particularity of Moroccan context and the appropriate evaluation methodology in order to reach results and thus allow the evaluator to find the required information and help its analysis and objective judgment.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".