A Model for Increasing Reform Implementation and Teacher Efficacy: Teacher Peer Coaching in Grades 3 and 6 Mathematics.
Bibliographic record
Abstract
This study examined the effects of peer coaching on mathematics teaching practices and teacher beliefs about their capacity to have an impact on student learning. Twelve teachers in grades 3 and 6 participated in a brief but intensive professional development program over six months. The program focused on effective math‐ ematics teaching strategies and peer coaching opportunities. Data sources included classroom observations, teacher self‐assessments, interviews, and field notes. Data were analysed using a two‐level qualitative coding strategy with multiple inter‐ preters. Findings showed that teachers implemented key strategies for effective mathematics teaching, especially in facilitating student interaction and improving the quality of tasks assigned. Key words: mathematics, teaching strategies, professional development Cette étude a trait aux effets de l’enseignement mutuel en mathématiques sur les méthodes pédagogiques des enseignants et leurs croyances quant à leur capacité d’avoir un impact sur l’apprentissage de leurs élèves. Douze enseignants en 3e et 6e années ont participé à un programme de perfectionnement intensif sur une période de six mois. Le programme était axé principalement sur des stratégies efficaces pour l’enseignement des mathématiques et les possibilités d’enseignement mutuel. Les sources des données étaient multiples : observation de la classe, auto‐évaluation de l’enseignant, entrevues et notes prises sur le terrain. Les données ont été analysées à l’aide d’une stratégie de codage qualitatif à deux niveaux avec de multiples interprètes. Les résultats indiquent que les enseignants ont mis en œuvre des stratégies clés pour enseigner efficacement les mathématiques, notamment en facilitant les interactions entre les élèves et en améliorant la qualité des tâches assignées. Mots clés : mathématiques, stratégies pédagogiques, perfectionnement professionnel.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".