Learner-Centered Coach Education: Practical Recommendations for Coach Development Administrators
Bibliographic record
Abstract
Despite a well-established understanding of the complexity inherent to both learning and sport coaching, programs designed to educate coaches have until recently been guided by pedagogical approaches aligned with rather simplistic views of learning. Thanks to the critical and innovative efforts of coaching scholars to uncover the shortcomings of traditional programs and their guiding epistemic traditions, coach education is becoming increasingly infused with constructivist, learner-centered (LC) strategies to help meet the complex needs of coaches. Although many LC informed recommendations have been offered, rarely do they provide coach development administrators (CDAs) with concrete, practical suggestions. Furthermore, the recommendations are scattered throughout the literature, which makes an already arduous task of bridging research and practice even more difficult for CDAs. Guided by the LC literature, a practical learner-centered teaching (LCT) framework, and previous recommendations presented in the coach education literature, this Best Practices paper presents a theoretically robust and empirically supported collection of practical recommendations for CDAs to support three critical areas of LC coach education: program design, facilitation, and coach engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".