Facilitators’ self-efficacy: a catalyst for growth in professional learning networks
Bibliographic record
Abstract
Purpose Facilitator self-efficacy or the confidence in one’s ability to effectively guide and support a group, plays a pivotal role in determining the success of professional learning networks (PLNs). However, limited research has examined how facilitator self-efficacy influences network dynamics and valued outcomes within PLNs. Thus, the study aims to fill this gap by exploring the relationship between facilitators’ self-efficacy and the effectiveness of PLNs. Design/methodology/approach The study employs a convergent mixed-methods design, starting with a quantitative phase that surveyed 295 facilitators. Data were collected electronically through a structured survey and analyzed with structural equation modeling (SEM) using SmartPLS 4 software. Following this, a qualitative phase involved semi-structured focus groups with ten facilitators to explore their experiences and contextual factors influencing self-efficacy. Findings The findings reveal that facilitators’ self-efficacy in collaborative practices is positively associated with participants’ perceptions of professional growth and knowledge sharing within PLNs. Self-efficacy in goal implementation was found to be a strong predictor of the achievement of PLN objectives. The facilitators’ confidence in group management significantly influenced the overall effectiveness of the PLN, enhancing members’ engagement and active participation. Originality/value The study highlights the crucial influence of facilitators’ self-efficacy on PLN outcomes and provides practical recommendations for supporting facilitators in educational and professional development. The study explores the role of facilitators’ self-efficacy in enhancing PLN effectiveness.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".