Amateur ice hockey coaching and the role of video feedback
Bibliographic record
Abstract
Amateur minor hockey coaches have recently begun to capture and play back video recordings to provide their teams with visual feedback of their play as a learning tool. Yet what is not clear is whether such video feedback is useful and how video feedback systems could be designed to better match the needs of amateur hockey coaches and players. As such, we wanted to understand coaches' current practices for communicating and teaching and their current use of video feedback (if at all). We observed games and practices and conducted in situ interviews with amateur coaches. Our results show that teaching and learning at highly competitive levels of minor hockey focuses on decision-making and comprehension of the game rather than individual physical movement. One-on-one teaching happens opportunistically and in very short time periods throughout games and practices. However, video feedback is currently used in a much different context, often away from the ice because of technological limitations. Based on these findings, we suggest video feedback systems be designed for use within the context of games and practices while balancing the individual needs of players with coaching goals.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".