Peer-to-Peer Learning: The Impact of Order of Performance on Learning Fundamental Movement Skills Through Video Analysis With Middle School Children
Bibliographic record
Abstract
Purpose: Through video analysis, this paper explores the impact that order of performance has on middle school students’ performance of fundamental movement skills within a peer-to-peer learning model. Order of performance refers to the order in which a student performed a skill while paired up with a peer. Method: Using a mobile application, Move Improve®, 18 students (eight males and 10 females) completed a standing jump and hollow body roll in partners assigned to order of performance (evaluator/performer). An independent samples t test was conducted to evaluate the differences in the mean scores between students who performed first and those who performed second for each skill. Results: There was a significant difference in standing jump scores (p < .01), where students who performed second had a higher average score than their peers who went first. Although not statistically significant (p = .293), results for hollow body roll also showed a similar performance pattern for students who went second compared with those who performed first. Conclusion: The order of performance within a peer-to-peer learning model may have a significant effect on performance scores for standing jump but not for hollow body roll. Reasons for the discrepancy may be due to a combination of skill familiarity, skill complexity, and training of observational learning.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".