The Evolution and Learner-Centered Status of a Coach Education Program
Bibliographic record
Abstract
The history of coach education in Western countries, much like higher education, has been shaped by societal influences and external drivers. The resulting trajectory includes a notable movement and shift in focus related to educational paradigms. Being learner-centered (LC) has become a central theme and mission by many coach education programs. The purpose of this case study was twofold: to explore the evolution of the historically rich coach education program of golf in Canada, and to assess the LC status of the most recently developed context of the program using Blumberg’s (2009) framework for developing and assessing learner-centered teaching (LCT). A series of program documents and interviews with seven coach development administrators involved in the program were analyzed. Findings revealed the turbulent epistemic evolution of the program and its pedagogical approaches, as well as the combination of internal and external drivers that triggered the shift from one extreme (instructor-centered teaching) to another (LCT) until finding a functional equilibrium. Moreover, the assessment of the program confirmed its claims of being LC. Discussions are presented on leading a LC change, facilitating learning, and using the framework to assess 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".