An extended challenge-based framework for practice design in sports coaching
Bibliographic record
Abstract
The challenge-point framework as a model for thinking about motor learning was first proposed in 2004. Although it has been well-cited, surprisingly this framework has not made its way into much of the applied sport science literature. One of the reasons for this omission is that the original framework had not been encapsulated into a paper accessible for sports practitioners. The framework had mostly a theoretical focus, providing a mechanistic summary of motor learning research. Our aims in this paper were to explain and elaborate on the challenge point framework to present an applied framework guiding practice design. We connect the framework to other theories that involve predictive coding, where information is attended when it disconfirms current predictions, providing a strong signal for learning. We also consider how two new dimensions (learners' motivation and practice specificity) need to be considered when designing practice settings. By moving around the different dimensions of functional difficulty, motivation, and specificity, coaches can optimize practice to achieve different learning goals. Specifically, we present three general "types" of practice: practice to learn, to transfer to competition, and to maintain current skills. Practical examples are given to illustrate how this framework can inform coach practice.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".