Distribution of Practice Combined with Observational Learning Has Time Dependent Effects on Motor Skill Acquisition
Bibliographic record
Abstract
Studies of the benefits of a distributed practice schedule on motor skill acquisition have typically found that distribution of practice results in better learning. However, less research has focused on how the benefits of distributed practice are impacted by timing during acquisition. To examine how timing of skill acquisition interacts with distribution of practice we had two groups of participants complete either an extensive massed or distributed training schedule to learn a speed stacking sequence across ten sessions. For participants in both groups, we provided observational learning to facilitate skill acquisition. Analysis of speed stacking time on a retention test revealed an overall benefit for the distributed relative to the massed practice group. Interestingly, our analysis of the benefits of distributed practice during training only showed performance benefits in the early session (session one) and later sessions (sessions eight, nine, and ten) of skill acquisition but not mid-way through it (sessions two through seven). Our results support previous findings highlighting the learning benefits of a distributed practice schedule but suggest that these benefits occur differentially throughout acquisition. Our work also replicates research demonstrating that observational learning is more beneficial when it is yoked to actual 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".