A state-of-the-art review of Canadian literature on teacher professional learning and leadership (2010-2020)
Bibliographic record
Abstract
This article presents a state-of-the-art review of the last decade (2010–2020) of research on Teacher Professional Learning and Leadership produced by researchers in Canadian universities and published by peer-reviewed journals indexed in Web of Science or Scopus database. The review aims to succinctly summarize key ideas, themes, and findings during the decade from 2010 to 2020 from Canadian research and to assist in identifying the major trends, strengths, and gray areas in the Canadian research. Thirty-one articles were selected for their specific focus on teacher professional learning and leadership. We present our findings using three broad thematic headings. First, evidence from examples of approaches to teachers’ professional learning and leadership in Canada, includes sub-sections on: teachers’ professional learning and leadership development programs; and teachers’ organizations and professional learning. Second, processes for developing professional learning and leadership and intended outcomes, includes sub-sections on: professional capital; social capital, collaborative learning, and professional learning communities; mentoring; teacher voice and agency; de-privatizing teaching practice; building relational trust; distributed leadership; and ethical leadership and decision-making. Third, professional learning informing educational change, policies, and practices, includes sub-sections on: educational change; technology; teacher evaluation and accountability; assessment practices; and diversity, inclusion and multi-culturalism.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.022 | 0.041 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".