A naturalistic case study of co-regulatory scaffolding with a mature coach-athlete dyad in figure skating
Bibliographic record
Abstract
Self-regulated learning (SRL) processes are used frequently by elite athletes and are important for optimizing practice efforts during talent development (McCardle et al., 2017). Before someone becomes self-regulated, they are co-regulated by a more experienced other, e.g., a coach (Glaser, 1996). Scaffolding, a form of co-regulation, has three conceptual characteristics: contingency control; fading; transfer of responsibility (van de Pol & Elbers, 2010). While popular in education, no studies have assessed scaffolding in sport. We explored scaffolding in a naturalistic, instrumental case study with an experienced female coach (aged 53, national level) and her competitive male figure skater (aged 15, provincial level) using a) participant observation, field notes, and recording and analysis of dialogue at 16 practices, and b) three separate interviews with the coach and skater at early-, mid-, and late-season. Data were thematically analyzed (Braun & Clarke, 2006). Deductive interpretations, guided by scaffolding characteristics, proved difficult due to overlap of conceptual constructs. Inductive analysis revealed complex, sport-specific nuances. Co-regulation occurred at both micro- and macro-levels. Micro-level co-regulation was illustrated by a co-regulatory – a pivotal interaction zone described by contributing roles and expectations for each actor, shared roles, and embodied transitory process related to the skater's SRL. A mature, enriched interface was predicated on prerequisite conditions for coach and athlete. Fading differed from education because of non-linear aspects when the coach would return to refine an element if it was incorrect. Findings suggest scaffolding manifests in sport, with self-regulation married to co-regulation, and more specifically, the interface.
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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 | 0.013 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".