Evaluation of a Preschool Educational Program Using the CIPP Evaluation Model – A Case Study of the Éveil et Créativité Collection within the FMPS Network in Morocco
Bibliographic record
Abstract
This study is conducted in the context of Morocco’s national ambition to universalize equitable and high-quality preschool education. It provides a holistic evaluation of the Éveil et Créativité pedagogical collection, developed and implemented by the Moroccan Foundation for PreSchool (FMPS) for the final year of preschool in its public network. Grounded in the CIPP evaluation model, the study adopts a mixed-methods approach. It combines confirmatory factor analysis (CFA) to validate the evaluation framework and a directed thematic analysis of qualitative feedback from over 10,007 educators. The evaluation covers five key pedagogical dimensions: curriculum alignment, quality of educational resources, child engagement, temporal organization, and openness to innovation. The integrated FMPS model—combining program design, educator training, and operational management—creates a consistent context that strengthens the reliability of the data collected. The findings confirm strong educator support for the program, especially regarding children’s engagement, curriculum alignment, and content richness. Participants also highlighted the clarity of the learning sequences and the user-friendliness of the tools provided. Suggestions for improvement focused on time management and the visual quality of printed materials, challenges that were especially evident in rural and multi-grade classrooms. Applied at a national scale, this approach shows how CFA can strengthen the methodological rigor of CIPP-based preschool program evaluations. Overall, the findings demonstrate the solidity of a scientifically grounded, integrated pedagogical system, supported by coherent governance and open to future enhancements.
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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 | 0.026 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".