Modelling Activities in Qualifying Training for Future Primary School Teachers: An Attractive Way to Train Competent and Innovative Teachers
Bibliographic record
Abstract
This paper investigates the outcomes of implementing a model-based training approach at the Centre Régional des Métiers de l’Éducation et de la Formation de Tanger Tetouan Al Hoceima (CRMEFTTH). The primary objective is to enhance the scientific knowledge of future primary school teachers, correct misconceptions about scientific concepts, and develop their creative and innovative thinking abilities, essential for science teaching. The approach involves trainee-teachers producing and utilizing scientific models, following a structured process prioritizing reflection and creativity. The training process begins with a pre-test to assess trainees' existing knowledge, followed by modelling activities where groups manipulate models based on specific instructions. This phase concludes with a post-test to evaluate the impact of analogue modelling on the understanding and appropriation of scientific concepts. Additionally, a satisfaction questionnaire is analyzed to assess the effectiveness of the approach. The results demonstrate that this modelling approach significantly enhances the training of novice teachers by improving their comprehension and representation of scientific concepts. The analysis of the questionnaires revealed that trainee teachers were highly satisfied with their achievements and the skills they developed, including observation, conceptualization of phenomena, production and evaluation of models, imagination, and team spirit. The implications of this study suggest that implementing modelling-based training approaches in teacher education programs can significantly improve the quality of science teaching in primary schools. By equipping future teachers with the necessary skills and knowledge to effectively construct scientific concepts to young learners. This approach has the potential to transform science teaching at primary level.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".