Nature and Quality of Interactions Between Elementary School Children Using Video-Modeling and Peer-to-Peer Evaluation With and Without Structured Video Feedback
Bibliographic record
Abstract
The purpose of this study was to examine the nature and quality of interactions between 24 students (9 male, 15 female) in an Alberta elementary physical education class using video-modeling and three different peer-to-peer (P2P) evaluation methods. Nature of interaction was determined by the duration of interaction (total, on-task time, off-task time, neutral), the type of comments (positive, constructive, negative), and quality of interaction by the category of feedback (4 categories) from both the evaluators and performers. This study compared structured paper evaluation (SP), unstructured video evaluation using the video feature on iPads (UV), and structured video evaluation using a prototype app on the iPad (SV). The SV condition provided statistically significant results for evaluator on-task, evaluator off-task, and performer on-task, along with increased positive comments from evaluators. The SP condition had significantly more depth of feedback. This study concludes that the use of SV to deliver feedback in a P2P learning environment has the potential to improve the nature of feedback during peer evaluations.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".