On Whether Task Experience of the Peer Differentially Impacts Feedback Scheduling and Skill Acquisition of a Learner
Bibliographic record
Abstract
Previous research has shown that peers without task experience provided knowledge of results (KR) as effectively as performers who self-controlled their own KR schedule (McRae et al., 2015). In the present experiment, a group of participants first practiced a motor task while self-controlling their KR during a defined acquisition period. Twenty-four hours after their last retention trial, these participants with motor experience then provided KR to a learner during their skill acquisition. Participants were required to learn a serial-timing task with a goal of 2,500 ms. Participants completed a defined acquisition period and then returned 24 h later for a retention test. In retention, learners who received KR from experienced peers were predicted to outperform learners who received KR from inexperienced peers. The results showed that performers learned the task similarly, independent of the peer's previous task experience. However, the peer groups differed in their frequency of providing KR to the learner and showed a discrepancy between their self-reported KR provision strategy and when they actually provided KR. The results have theoretical implications for understanding the impact of self-control in motor learning contexts.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".