Researcher, Coach Developer, and Coaches’ Perspectives on Learner-Centered Teaching in a Rugby Coach Education Program
Bibliographic record
Abstract
In the sports coaching field, learner-centered teaching (LCT) has been advocated as a viable approach to increasing learners’ involvement in the learning process. However, implementing LCT is not a simple undertaking as coach developers, and coaches have encountered dilemmas when it comes to shifting to LCT in coach education. This study aimed to investigate how LCT principles were implemented in a rugby coach education program through the perspectives of the researcher, the coach developer, and coaches. Participants included the researcher (i.e., first author), a coach developer, and 10 rugby coaches. The researcher observed three coach education courses, gathered descriptive and reflective field notes, and conducted individual semistructured interviews with both the coach developer and the 10 coaches. Findings shed light on the strategies adopted by the coach developer and the extent to which these strategies aligned with LCT principles. Coaches discussed how they enjoyed their active role in the courses and the approaches used by the coach developer to leverage learning. The discussion highlights the importance of coach developers in facilitating a learning process that is challenging, motivating, and supports coaches throughout the courses.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".