The efficacy of demonstrations in teaching children an unfamiliar movement skill: The effects of object-orientated actions and point-light demonstrations
Bibliographic record
Abstract
In Experiment 1, adult and child participants were instructed to imitate a video model performing a bowling action with or without a ball. Participants imitated the action with greater accuracy without a ball and in general adults were more accurate than children. In Experiment 2, adults and children were shown a video or point-light display of the bowling action. There was no difference in movement form between the adult point-light and video groups. In contrast, children were poorer at reproducing the action when viewing point-light compared with video sequences (P < 0.05). The novel point-light display hindered the children's ability to provide conceptual mediation between the presented information and action requirements. In Experiment 3, a child point-light group was provided with perceptual-cognitive training. The perceptual-cognitive training group demonstrated better movement reproduction than a group who viewed the point-light displays with no training (P < 0.05), although there were no differences between participants who received training and those who viewed a video. Children are able to perceive and use relative motion information from a display after some general training, and the effectiveness of demonstrations needs to be judged relative to the task context.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".