The impact of video performance technology and peer-to-peer learning on table tennis skill acquisition in elementary students
Bibliographic record
Abstract
Introduction: Peer-to-peer (P2P) learning promotes collaboration, critical thinking, and active student participation, with recognized benefits in classroom settings. However, its integration into physical education (PE), particularly in combination with video performance technology-tools for learners to record and evaluate motor skills through structured video feedback-remains underexplored. Multimedia tools like video feedback have shown promise in enhancing motor skill acquisition, but their effectiveness in PE environments is not yet fully understood. Thus, we aimed to evaluate the impact of combining video performance technology with P2P learning on the table tennis skills. Methods: A quasi-randomized control trial was conducted with 73 Grade 6 students from four PE classes. Participants were divided into four groups: Instruction Sheets Group, iPad Camera Group, Instructed Video Group (using the Move Improve® app), and a Traditional Learning Group. Over 2 weeks, all groups completed seven 45-minute table tennis sessions focusing on grip, stance, forehand, and backhand strokes. Pre- and post-assessments were conducted, and a mixed-design ANOVA was used to evaluate performance improvements across groups. Results: All groups demonstrated significant skill improvements. The Instructed Video Group and Traditional Learning Group showed the greatest skill improvements. The Move Improve® app, which provides structured video demonstrations and guided peer feedback, helped students effectively analyze and refine their movements. Conclusion: Integrating video technology with P2P learning can match the effectiveness of expert-led instruction and provides additional benefits such as improved engagement and self-assessment. These findings support the broader use of multimedia tools to enhance skill development in PE, especially where expert instruction is limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".