Learning a Motor Skill from Video and Static Pictures in Physical Education Students—Effects on Technical Performances, Motivation and Cognitive Load
Bibliographic record
Abstract
The purpose of the current study was to compare the effectiveness of a video and three different formats of static pictures (simultaneous-permanent pictures, sequential-transient pictures and sequential-permanent pictures) on the acquisition and retention of a complex judo skill in novice young adults. One hundred and thirty-three first-year students in the certificate in Physical Education (PE) were randomly assigned to either: a static-simultaneous-permanent pictures condition (n = 30), a static-sequential-transient pictures condition (n = 29), a static-sequential permanent pictures condition (n = 36) or a video condition (n = 38). They were instructed to observe and reproduce a complex judo technique (Ippon-Seoi-Nage) immediately after the learning phase (including a sequence of three trials—the acquisition phase) and after one week without observation (the retention phase). The results showed that the continuous video generated better learning performances than all static pictures formats. Moreover, it has been shown that sequential-permanent pictures presentation was more effective than static simultaneous-permanent pictures and sequential-transient pictures. In addition to the human movement effect, complementary explanations in terms of cognitive load theory, perceptual continuity, mental animation and intrinsic motivation are suggested. Implications of the results for the effective design of instructional materials within PE context are discussed.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".