Study of the Training Accreditation in a Moroccan Engineering School: Strengths, Weaknesses, Opportunities and Threats
Bibliographic record
Abstract
This work aims to improve engineering training quality in Morocco, especially the learning outcomes assessment.At first, we have to examine what should be evaluated. In this context, we reviewed the different accreditation related to the Higher National School of Electricity and Mechanics (ENSEM) between 2007 and 2018 using the SWOT decision-making method. We considered the Mechanical Design and Integrated Production (CMPI) branch as the study sample.Then, we compared the ENSEM CMPI program to a list of learning outcomes chosen after our benchmark analysis.Our objective through this study is to highlight the strengths and weaknesses of the Moroccan engineering accreditation system, especially regarding the learning outcomes. This work will allow us to propose improvements in the quality of engineering training, principally in the assessment of learning outcomes, to enable the Moroccan diplomas to align with the international level and meet the great challenges facing globalization.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".