Knowledge brokering pivotal in professional learning: quality use of research contributes to teacher-leaders’ confidence
Bibliographic record
Abstract
Educational networks and knowledge brokering play a critical function in supporting educators to keep abreast of scholarly literature and contemporary research that inform practice and policy in schools and districts. In this article, we leverage a quality use of research-evidence framework within a design-based study to elucidate the pivotal role of knowledge brokering in how teacher leaders utilized research during their participation in a professional learning series. In a survey administered to K-9 teacher leaders in Western Canada at the end of a year-long professional learning series, participants (n = 374/500) provided their reflections about how the series supported their learning. The analysis revealed developments across individual, organizational, and system-level components. A significant contribution of this study is that meaningfully integrated research evidence in professional learning can support teacher leaders’ individual confidence in practice, confidence in collaboration at the school level, confidence in leading professional conversations at the organizational level, and confidence in staying updated with educational research at the system level fostering a culture of support and continuous improvement. Knowledge brokering is a pivotal function of relational professional learning networks and when embedded in design-based professional learning for teacher leaders, this powerful combination can contribute to quality research use and can serve to strengthen the theory-to-practice connections in educational 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 | 0.358 | 0.543 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".