The impact of self-as-a-model interventions on children's self-regulation of learning and swimming performance
Bibliographic record
Abstract
We compared two self-as-a-model interventions: self-modelling (viewing oneself perform an adaptive behaviour) and self-observation (viewing oneself perform at current skill level). Operating within Zimmerman's (1989 Zimmerman, B. J. 1989. A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81: 329–339. [Crossref], [Web of Science ®] , [Google Scholar], 2000 Zimmerman, B. J. 2000. “Attainment of self-regulation: A social cognitive perspective”. In Handbook of self-regulation, Edited by: Boekaerts, M., Pintrich, P. R. and Zeidner, M. 13–39. San Diego, CA: Academic Press. [Crossref] , [Google Scholar]) theory of self-regulated learning, we examined the effect of the modelling interventions on three self-regulatory processes (self-efficacy, intrinsic motivation, and self-satisfaction), as well as physical performance. Thirty-three children were randomly assigned to one of three experimental groups. The two self-as-a model groups received the modelling intervention just before physical practice, whereas the control group received physical practice only. Analyses of the retention scores revealed significant differences for all dependent measures. Post hoc testing showed consistently that the self-modelling group performed better than the self-observation and control groups, and that the two latter groups performing similarly. These results provide support for the implementation of self-modelling interventions with children when teaching motor skills.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".