Observation interventions for motor skill learning and performance: an applied model for the use of observation
Bibliographic record
Abstract
Using the 5 Ws and 1 H journalistic approach of Beveridge Mackie (2011), we reviewed the observation intervention research that targeted sport skills or daily movement tasks. Through this review, it became apparent that while there is much research that examines observation of a live or video (what), skilled model (who) for enhanced skill learning (why) in laboratory settings (where), there is a need for not only a wider scope of research, but also a deeper one. Following the review of literature, an applied model for the use of observation is advanced. Through this applied model, we propose that practitioners should first assess the observer's characteristics and the task characteristics for which any observation intervention is being created. The practitioner should then gain an understanding of the context and the desired outcomes of the learner and use this advance information to vary the characteristics of: (1) who is observed; (2) what is observed and what instructional features will accompany the intervention; (3) when it is observed; and (4) how the observed information should be delivered. Future research directions are also forwarded with regard to identified gaps in the literature.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| 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".