Coach Developers as ‘Facilitators of Learning’ in a Large-Scale Coach Education Programme: One Actor in a Complex System
Bibliographic record
Abstract
The Canadian National Coaching Certification Program (NCCP) redesigned its coaching education programmes to utilise a learner-centred, problem-based approach. The purpose of this paper is to document the design, delivery, and subjective assessment of this large-scale coach education programme through the perspectives of the different actors, with a special focus on the coach developers (CDs) as an essential and important group within the coach education system. A constructivist view of learning, supported by the work of Jennifer Moon on the impact of short courses and workshops, guided this work. Part 1 of the results provides an overview of the studies conducted on the revised NCCP. Part 2 examines the perspectives of 26 CDs concerning their training and experiences delivering the redesigned NCCP. Conclusions include the importance of considering the cognitive structures of the CDs and coach learners; issues related to covering the module content versus addressing coaches’ learning needs; ensuring adequate time for questions and reflection to enable deep learning; specific training for CDs to support post-workshop learning; and the importance of taking a systems approach to coach development that considers all the actors involved in a national programme being delivered at the provincial/territorial or sport organisational level.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".