Examining Learner-Controlled Role-Switching in Dyad Practice for the Learning of a Speed Cup-Stacking Task
Bibliographic record
Abstract
Dyad practice has proven to be an efficient, and in some cases, a more effective method of promoting motor learning compared to individual practice. Further, providing individuals control over their own or another learner's practice environment has also been shown to be superior for skill learning relative to individuals without control. The purpose of the experiment was to assess learner-controlled role-switching in dyad practice conditions. In dyads, partners either alternated actor and observer roles on a trial-to-trial basis, or under novel learner-controlled conditions wherein either the actor or the observer was given control over when the partners should switch roles. Participants practiced a speed cup-stacking task and learning was assessed in 24-h retention and transfer tests. Although there were no learning differences between dyad conditions, paired learners effectively chose when to switch roles with their partner, without undermining learning. The results also highlight the dynamic nature of dyad practice as the observers chose to switch roles more frequently than the actors, yet both dyad groups adopted comparable switching strategies by alternating roles following relatively 'good' and 'bad' trials. This experiment provides further support for dyad practice as an efficient and effective method of skill 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".