The role of the researcher-facilitator in professional learning networks
Bibliographic record
Abstract
Purpose In the research reported here we looked at data from three professional learning network (PLN) studies to answer the research question: What do researcher-facilitators do in PLNs and how do their roles vary across PLNs? Design/methodology/approach In this research we used a multiple case study design focused on three individual PLNs, each one constituting an embedded case. To better understand the role of the PLN facilitator, we analyzed interview and artifact data to generate findings about how PLN facilitation was structured to support learning. Findings Drawing from our analyses we identified four themes. Researcher-facilitators nurtured collaboration and distributed leadership; selected and offered theory, research and related resources; supported cycles of goal setting, action and reflection; and designed and implemented structures that built from teacher and student data. These three case studies show how PLN researcher-facilitators provided opportunities for teachers to step back from their practice and make evidence- and theory-supported meaning of their experiences. This study also advances understanding about how facilitators can position resources to support knowledge construction within PLNs. The third case study specifically illustrated how researcher-facilitators supported PLN members’ data-informed reflective inquiry. These case studies show the promise of providing educators with opportunities to enact agency, leadership and, at the same time, access supports. Originality/value The cross-case analysis of case studies offers much-needed empirical research regarding the role researcher-facilitators play within PLNs. Specifically, our study recasts the role of researchers, moving them away from unidirectional knowledge generators to instead facilitating opportunities for educators to bridge research/theory, evidence about student learning and practice.
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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 | 0.178 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".