Facilitation of Value Creation in Professional Learning Networks
Bibliographic record
Abstract
Professional learning networks (PLN) in Higher Education represent new social configurations for networked workplaces in which education, research and innovation can be combined. Here academic staff engages with others outside of their everyday organisational community. This study identifies and conceptualizes essential behaviours that facilitators of professional learning networks use to promote value creation of various kinds. The two-phase study started with an empirical field study on the value creation stories of 11 participants within 3 professional networks to investigate essential facilitator behaviours. A panel study including 30 researchers, lecturers and practitioners representing a wide range of learning and innovation networks, was conducted to validate and enrich the findings derived from the field study. From the field study 54 facilitator behaviours were identified. The panel study raised 68 complementary statements on essential facilitator behaviours. Qualitative data analysis lead to five themes of facilitator behaviour. Facilitators’ contributions to value creation in networked workplace contexts can be understood as the interplay of five foci of facilitative behaviour: 1. relationship, 2. space, 3. ownership, 4. direction, 5. result. Findings concerning facilitator behaviours are synthesised in an conceptualisation of the process dynamic of value creation in networked workplaces: The Facilitator Compass. This paper provides insight on what plays a major role in the success of professional networks: the way they are facilitated. While the role of a facilitator is acknowledged in literature and in practice, this study adds to the knowledge base by showing how academic staff can navigate for value creation in networked workplaces.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".