The Co-Regulatory Coaching Interface Model: A Case Study of a Figure Skating Dyad
Bibliographic record
Abstract
Very little research has investigated co-regulated learning (CRL; Hadwin et al., 2011) in the context of sport coaching for skill acquisition. Although research indicates self-regulated learning (SRL) helps elite competitive athletes optimize their skill acquisition (McCardle et al., 2019), coaching literature has yet to examine how co-regulated learning experiences in joint work between a coach and athlete are associated with SRL competencies in an athlete. Thus, the objective of this instrumental case study was to describe the nature of joint work between an experienced female coach (aged 53, national level) and a male figure skater (aged 15, provincial level) in a naturalistic environment. Season-long data collection involved analysis of recorded dialogue at 16 practices and three interviews with each participant. Using inductive reflexive thematic analysis, we developed higher-order themes related to macro- and micro-levels of CRL, and implications of the coach’s progression on the development of SRL. The Co-regulatory Coaching Interface Model, representing micro CRL interactions, outlines contributions from each member and dialogue processes facilitating skill acquisition. SRL was both an expected contributor to, and a consequence of interface interactions. We discuss coach-athlete dyadic processes, what they mean for athletes’ self-practice time, and how the model contributes a new perspective on collaborative work between coaches and athletes that has not been emphasized in the coaching science on talent development.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".