The role of formative evaluation in the teaching/learning process at ISPITS in Morocco: Exploratory study of teachers involved in the health environment option
Bibliographic record
Abstract
Formative evaluation (FE) of teaching and learning (TL) is a pedagogical innovation in many educational systems around the world, such as Switzerland, France, and Quebec. In Morocco, the Higher Institutes for Nursing and Health Technology (ISPITS) has introduced FE into its training curriculum, including the Health and Environment (HE) option. Our exploratory study of teachers of this option at ISPITS (N = 60) aims to examine the current state of practices relating to this type of assessment at these institutes. The results of this research revealed that 75% of teachers do not use formative assessment tools, 55% find that it increases their workload, and 48% report its interest to both teachers and learners. Half of the teachers agree that formative assessment should be operationalized systematically in the training process. Our study reports the gap between what competent bodies designed and validated and the actual practices used. Consequently, we believe that narrowing this gap will undoubtedly contribute to the development of learners’ specific skills in environmental responsibility and protection.
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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 | 0.027 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".