Evaluating the Effectiveness of Peer-Scheduled Practice on Motor Learning
Bibliographic record
Abstract
Giving learners a choice over how to schedule practice benefits motor learning. Here we studied peer scheduling to determine whether this benefit is related to the adaptive nature of practice or decisions about how to switch between skills. Forty-eight participants were paired and assigned to self- or peer-scheduled groups. Within each pair, one person (Actor) physically practiced 3 keystroke sequences, each with different timing goals. Self-scheduled Actors chose the sequence before each practice trial while their Partner watched. Peer-scheduled Actors had their practice directed by their Partner. Both peer schedulers and self-schedulers showed performance-dependent practice, making decisions to switch based on timing error. However, peer schedulers generally chose to switch more than self-schedulers although this was not related to retention for either group. Importantly, self-scheduled Actors did not differ in retention from peer-scheduled Actors, but the Actors generally performed with lower error in retention than that of their partners. Peer-scheduled practice was rated as more motivating and enjoyable than self-scheduled practice. In view of the lack of difference in retention and the positive ratings of peer-scheduled practice, we conclude that it is the adaptive nature of practice that is important for learning and that peer-directed practice is an effective alternative practice method to self-directed practice.
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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 | 0.014 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".