Comparative Study between the Best-Performing Education Systems of the Five Continents and That of Morocco
Bibliographic record
Abstract
This study aims to investigate the most successful educational systems worldwide and the Moroccan educational system to determine the causes constraining the socio-educational development in Morocco. This comparative study relies on a qualitative methodology based on an inductive approach called the empirical-inductive approach, which starts from facts and real and observable data. This scientific method allowed us to identify our subject in terms of the best-performing educational system. The sample includes one or two educational systems from five continents: Africa (Morocco), America (Canada and the United States), Asia (Japan), Europe (France and Finland), and Oceania (Australia) through the comparison of the most common criteria between each country mentioned: the school system (cycles), teacher training, the number of hours a teacher works, teacher salaries and the number of students per class. This study provides a clear picture of the most successful education system that has been able to manage the masses and reveals the secret of its successful educational management policies. It is the Canadian education system that is a member of the Organization for Economic Cooperation and Development (OECD) according to the Program for International Student Assessment with an OECD average of 520 (score above the OECD average of 487), and Morocco is in 75th place about four steps from the last with an OECD average of 359 (score well below the OECD average 487). The above is more than a reason to be inspired by the education system of this leading country in the field, which would lead to better results in terms of structural transformation, inclusive development, and efficient management of Moroccan education networks in the coming days.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".