End-point trajectory matching as a method for teaching kicking skills
Bibliographic record
Abstract
The aim in this experiment was to determine whether demonstrations that focus on end-point related information, in this case, ball-trajectory information (BALL), are more effective in teaching motor skills than more traditional demonstrations which focus on movement technique (BODY). Nineteen participants with low-level soccer experience practised a left-footed, soccer-chip shot, which required them to land a ball on a target, while clearing a height barrier. Information concerning how to achieve the task goal was manipulated. Participants either received demonstrations of an expert performing the skill (i.e., BODY, n=10) or they received a demonstration of the expert's ball flight path (BALL, n=9). The participants were asked to match the criterion flight or form to achieve the task goal. Feedback concerning ball flight and movement form was controlled, although all participants received KR. Trials were videotaped for analyses and feedback and movement kinematics were collected using 3D cameras on a selection of trials. Both groups improved during acquisition although there was no significant difference between the groups in terms of outcome attainment (i.e., height success and radial error). In retention, the BALL group showed more accurate performance relative to the BODY group, when demonstrations and feedback were withheld (p <05). Only in acquisition were any differences between the two groups noted in terms of movement kinematics. The BODY group showed a closer approximation to the model in terms of how various joint displacement angles were obtained (but not the actual angles) in comparison to the BALL group. These results provide initial evidence to support the use of end-point template matching strategies for teaching complex movement skills, such as those common in sports which require the accurate displacement of some external object (such as a ball or disc).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".