Facilitators’ self-efficacy: a catalyst for growth in professional learning networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it