Meta-Analytic Findings in the Self-Controlled Motor Learning Literature: Underpowered, Biased, and Lacking Evidential Value
Bibliographic record
Abstract
The self-controlled motor learning literature consists of experiments that compare a group of learners who are provided with a choice over an aspect of their practice environment to a group who are yoked to those choices. A qualitative review of the literature suggests an unambiguous benefit from self-controlled practice. A meta-analysis was conducted on the effects of self-controlled practice on retention test performance measures with a focus on assessing and potentially correcting for selection bias in the literature, such as publication bias and p-hacking. First, a naïve random effects model was fit to the data and a moderate benefit of self-controlled practice, g=.44 (k= 52, N= 2061, 95%CI [.31, .56]), was found. Second, publication status was added to the model as a potential moderator, revealing a significant difference between published and unpublished findings, with only the former reporting a benefit of self-controlled practice. Third, to investigate and adjust for the impact of selectively reporting statistically significant results, a weight-function model was fit to the data with a one-tailed p-value cutpoint of .025. The weight-function model revealed substantial selection bias and estimated the true average effect of self-controlled practice as g=.107 (95%CI [.047, .18]). P-curve analyses were conducted on the statistically significant results published in the literature and the outcome suggested a lack of evidential value. Fourth, a suite of sensitivity analyses were conducted to evaluate the robustness of these results, all of which converged on trivially small effect estimates. Overall, our results suggest the benefit of self-controlled practice on motor learning is small and not currently distinguishable from zero.
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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 | 0.411 | 0.699 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".