Former et accompagner de futures personnes enseignantes en formation initiale dans le développement d'une relation de qualité avec leurs élèves : une recherche-action en France et au Québec
Bibliographic record
Abstract
For nearly thirty years, scientific literature has emphasized the importance of the teacher-student relationship (Birch et Ladd, 1997 ; Davis, 2003 ; Hamre et Pianta, 2007 ; Pianta, 1999 ; Sabol et Pianta, 2012). It highlights the effects of a quality teacher-student relationship on students' educational success, their well-being, as well as on teachers' well-being and their persistence in the profession (Espinosa, 2016 ; Goyette et Martineau, 2020 ; Veldman et al., 2013) . However, research and international studies (Algan et al., 2018 ; TALIS, 2018) show that this relationship is not of sufficient quality in many educational contexts. Among the explanatory factors is notably the lack of training for teachers regarding the quality of this relationship (Biémar et al., soumis). While numerous training programs exist for practicing teachers, few address initial teacher education. Within the framework of action research with future teachers, this thesis proposes to design and implement a training and support program aimed at developing the quality of teacher-student relationships through interactions, which are concrete manifestations of this relationship. The TTI (Teaching Through Interactions) model (Hamre et Pianta, 2007) and the CLASS tool (Pianta et al., 2008b) constitute the theoretical and methodological foundations of this approach. This research is situated within a dual context: initial teacher training in France and Quebec. The results show that the implementation of the training and support system, in which future teachers felt fully engaged, fostered a significant change in their practices. They deepened their knowledge about the importance of this relationship, recognized as a cornerstone of their profession (Bucheton, 2019 ; Lenoir, 2002), developed their reflexivity by becoming aware of their practices, analyzing them and putting them into perspective, while benefiting from exchanges with their peers and theoretical support from research. Working in two distinct contexts has enriched the data analysis, highlighted inspiring practices, and demonstrated that the practices of future teachers reflect the structural and cultural components of an educational system. This research thus confirms the relevance of integrating, in initial teacher training, a training and support system that values the teacher-student relationship, encourages reflexivity, supports the evolution of practices, and emphasizes the human dimension of this profession, in service of student learning and educational success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".