Growing the Top: Examining a Mentor–Coach Professional Learning Network
Bibliographic record
Abstract
Abstract International educational research has shown that high quality coaching, mentoring, and induction for beginning teachers can enhance development and retention of highly effective teachers and, ultimately, increase student success. In Canada, like many jurisdictions, teacher induction programs have grown in popularity as a means to support beginning teachers, yet programs vary greatly in terms of delivery and effectiveness. This chapter presents the findings from a qualitative case study that examined one bespoke teacher induction program in the Western Québec School Board (WQSB). Specifically, it reports on the experience of mentor–coaches (MC) who are part of the school district’s Mentoring and Coaching Fellowship (MCF). In the district, mentoring and coaching are viewed as distinct, yet interconnected components of an effective induction program. In the WQSB, teaching fellows and MCs learn together in a social and situated context (Lave & Wenger, 1991) as they focus on four key elements: the practice of teaching, navigating school and district culture, what it means to be a teacher, and the formation of a teaching identity. Research has shown effective coaching and mentoring programs not only enhance teaching and learning, but also they offer powerful benefits to veteran teachers. With mentoring and coaching practice highly diverse and inconsistent depending on the quality of the relationship and the context, it is clear that effective selection, support and professional learning and development for MCs is essential. This chapter examines the strengths and challenges of the school district’s Mentor–Coach Professional Learning Network (MC PLN) from the perspective of network members. Data collected from questionnaires, focus groups and semi-structured interviews were abductively analyzed with and against Brown and Poortman’s (2018) five supporting conditions for effective PLNs. Study findings indicated that the MC PLN offers valuable professional learning and development for participants and is a critical feature in a powerful induction program that also focuses on “growing the top.” However, challenges also emerged that highlight the need for the district to ensure ongoing attention to the PLN’s structure and processes in order to sustain MC motivation, engagement, and commitment.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".