Cooperative and Competitive Dyad Learning Environments Influence Psychosocial, But Not Motor, Outcomes in a Relative-Timing Task
Bibliographic record
Abstract
Research investigating the behavioral and psychosocial outcomes of individuals practicing motor skills in pairs (dyad practice) has generated mixed evidence regarding its effectiveness when compared with individuals practicing alone. The purpose of the present study was to investigate the motor performance, learning, and psychosocial outcomes associated with competitive or cooperative dyad practice contexts versus practicing alone. Participants practiced a relative-timing keypress sequence task alone, or in competitive or cooperative dyads. Relative timing errors for each movement component in the sequence were measured across the 40 practice trials and in a 24-hr retention test. Psychosocial measures of positive and negative affect, motivation, and efficacy were also collected. There were no group differences in timing errors during practice or retention, although partners in the cooperative group were more alike (correlated) in performance compared with partners in the competitive group. The cooperative group also reported higher levels of interest/enjoyment and commitment to continue practicing compared with the competitive group, but these outcomes were not different from the alone group. Overall, although there were indications that cooperative dyad practice led to greater task engagement and task “sharing” than competitive dyad practice, these effects did not impact the acquisition and retention of the motor skill.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".