Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how peer coaching was introduced in one school in Egypt and to identify barriers and opportunities for successful implementation. Design/methodology/approach The methodology included semi-structured interviews with eight teachers, participant observation of their classes and meetings, and three focus group meetings with teachers and school administrators. Findings Ladyshewsky’s (2017) five key aspects of peer coaching are considered in the findings: establishing peer partners, building trust between the partners, identifying specific areas to target for learning, training on non-evaluative questions and feedback, and supporting each other as new ideas are attempted. Each aspect of these is reviewed in light of the implementation process in the school. Practical implications The study provides practical suggestions for teachers and school administrators that include considerations for implementation. Numerous connections are made to research on peer coaching that is relevant to the implementation of peer coaching in schools in Egypt and other countries in the Global South. Originality/value The study provides an examination of the implementation of peer coaching in a school in Egypt. Thus, it contributes to the limited literature on peer coaching in the Global South. The discussion and conclusion sections consider further questions and research opportunities for effective practices in peer coaching in international contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".