Evaluating the Effectiveness of Peer-Scheduled Practice on Motor Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.035 |
| 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 | 0.002 |
| 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 it