The impact of Codevelopment Action Learning participation on facilitators’ skills and development
Bibliographic record
Abstract
Action Learning (AL) is widely used to address complex challenges while building collaboration and leadership. Codevelopment Action Learning (CAL), a structured Canadian variant of AL, has been shown to benefit participants by strengthening self-efficacy, professional identity, and transversal skills. However, little is known about its impact on facilitators, even though they are key to sustaining group learning. Most writings assume that facilitation skills must be mastered before practice, overlooking the possibility that they may evolve during facilitation itself. This study investigates the development of facilitation skills among 58 new CAL facilitators from 15 organizations, who collectively led 263 sessions between 2017 and 2021. Using qualitative content analysis of post-session questionnaires, the study identified significant learning outcomes across CAL’s four facilitation functions and specific behaviors, including questioning, feedback, active listening, and emotional support. Facilitators also reported varied learning outcomes related to the topics discussed by participants, such as knowledge of organizational contexts, professional roles, and shared experiences. The findings challenge assumptions that facilitators enter fully equipped for their role, showing instead that facilitation skills and broader learning deepen progressively through practice. This empirical evidence extends the AL literature and informs facilitator training by emphasizing targeted development alongside experiential learning.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".