Middle Leading Practices of Facilitation, Mentoring, and Coaching for Teacher Development: A Focus on Intent and Relationality
Bibliographic record
Abstract
While educational institutions are increasingly acknowledging the importance of middle leaders for improving teaching, there is little research on middle leaders’ specific leading practices compared with, for example, principals. Evidence delineating and describing specific middle leading practice is scant. Drawing on practice theory, this article presents preliminary results from the first phase of a four-year Australian project examining the “flow of influence” of middle leading practices on teacher development. Thematic analysis of interviews reveals the multidimensionality of middle leading, and specifically, ways in which the practices of facilitating, mentoring, and coaching are nuanced and distinctive in their arrangement, intent, and relationality. Results have important implications that cannot be ignored by school leaders and policymakers seeking to improve broader systemic support for building and refining middle leading practices. RésuméBien que les établissements d’enseignement reconnaissent de plus en plus l’importance des cadres intermédiaires pour l’amélioration de l’enseignement, il existe peu de recherches sur le leadership de ces cadres relatives, par exemple, à celui des directeurs d’école. Les descriptions et analyses des pratiques spécifiques aux cadres intermédiaires sont rares. Cet article s’inspire de la théorie d’entraînement pour présenter les résultats préliminaires de la première étape d’un projet australien s’échelonnant sur quatre ans qui examine les « flux d’influence » des cadres intermédiaires sur le développement de l’enseignement. Une analyse thématique d’entretiens révèle le caractère multidimensionnel de la direction intermédiaire et, plus particulièrement, les manières dont la facilitation, le mentorat et l’accompagnement, en ce qui a trait à leur structure, leurs intentions et leur relationnalité, sont nuancés et distincts. Les résultats de cette recherche ont des implications importantes que ne peuvent pas ignorer les dirigeants et responsables des écoles qui cherchent à offrir un meilleur appui systémique pour améliorer et peaufiner les pratiques propres aux cadres intermédiaires. Keywords / Mots clés : coaching, facilitating, mentoring, middle leadership, practice architectures / accompagnement professionnel, facilitation, mentorat, cadres intermédiaires, architectures des activités
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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 | 0.013 | 0.016 |
| 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 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".