Learning Effects of Self-Controlled Practice Scheduling for Children and Adults: Are the Advantages Different?
Bibliographic record
Abstract
The benefit of providing learners control over their repetition schedule during multi-task learning has been limited to adult samples. Recently, differences in self-controlled strategies, such as frequent requests for knowledge of results by children (10 years) compared to adults have been reported. The purpose of the present experiment was to assess the benefits of a self-controlled repetition schedule during multi-task learning for children compared to adults. Twenty-four children (M age = 11.7 yr., SD = 2.0) and 24 adults (M age = 22.0 yr., SD = 2.2) completed 36 acquisition (12 per sequence) and 12 retention trials (4 per sequence) over two days for a key-pressing task. Half the adults (n = 12) and half the children (n = 12) chose the order in which to practice the three sequences during acquisition. The remaining participants practiced under the repetition schedule of a self-controlled counterpart. The dependent variables were the total time to complete the trial and the success of the motor trial (successful or unsuccessful). No differences were observed in the total number of times participants switched from one sequence to another in acquisition for the children and adults in the self-controlled condition. In retention, the proportion of error trials did not differ between the children and adults. The main effect for self-controlled vs yoked conditions indicated superior learning for the self-controlled conditions independent of age.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".