Participating in a Learner-Centered Coach Education Program: Composite Vignettes of Coaches’ and Coach Educators’ Experiences
Bibliographic record
Abstract
Given the inextricable roles of the coach learner and coach educator in learner-centered (LC) coach education, research into their perceptions and experiences in these programs appears to be a priority. As such, building on Paquette and Trudel’s examination of Canada’s golf coach education program relative to its alignment with learner-centered approaches, the present study examined coaches’ and coach educators’ perspectives of their experiences participating in the abovementioned program that was found to have a LC design. In-depth semi-structured interviews were conducted with 16 participants (6 coach educators and 10 coaches), and data were analyzed using a thematic analysis. The finalized themes were used as a narrative skeleton for the creation of the four composite vignettes. The vignettes represented the experiences of four composite characters relative to their learning orientations to learner-centered teaching (LCT) and instructor-centred teaching (ICT): LCT Coach Educator, LCT Coach, ICT Coach Educator, ICT Coach. As influenced by their cognitive structures, the vignettes depict the composite coaches’ varied engagement and perceptions of the program, as well as the coach educators’ varied delivery of the program and adherence to the program’s LC design. These diverse experiences are discussed in relation to the impact of LC coach education.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".