The Art and Science of Leadership in Learning Environments: Facilitating a Professional Learning Community across Districts
Bibliographic record
Abstract
A professional learning community (PLC) is one of the most promising strategies for effecting change in educational practices to improve academic achievement and wellbeing for all students. The PLC facilitator’s role in developing and leading blended (online and face-to-face) PLCs with members from Ontario’s school districts was examined through a qualitative case study. The research involved a document analysis of 36 reflections from 6 facilitators, observations, and a 2-hour, open ended, semi-structured interview with 6 facilitation coaches associated with the Elementary Teachers’ Federation of Ontario. Facilitators shared leadership with PLC members to develop collaborative cultures, shared goals and artifacts, and guided them using dialogue and open-ended questioning to promote deep thinking, inquiry, and reflection. They scheduled meetings, set deadlines, monitored progress, and contacted members between meetings to encourage attendance. This research provides insight into the facilitators’ strategies for encouraging the production of shared goals and artifacts, and the organizational culture that promotes collaborative work.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".