When quantity is not enough: Disentangling the roles of practice time, self-regulation and deliberate practice in musical achievement
Bibliographic record
Abstract
Past research has referred to either the concepts of self-regulation or deliberate practice to explain the relationships between learning strategies and musical achievement and performance. In addition, even though most scholars agree that formal practice time plays an important role in musical achievement, empirical investigations have failed to show consistent associations between practice time and achievement. The aim of this article is to suggest an integrative framework in which self-regulation, deliberate practice strategies and practice time are simultaneously taken into account in the prediction of musical achievement. In this framework, we propose that formal practice should be defined as a goal-directed and focused period of practice that includes both self-regulation and deliberate practice strategies. We further posit that practice time will predict musical achievement only if associated with formal practice. This integrative framework was tested in a 4-month prospective study using structural equation modelling. Results revealed that this integrative model was a better predictor of musical achievement than traditional methods of measurement. The suggested integration of self-regulation and deliberate practice within a single framework provides a more complete picture of the associations between learning strategies, practice time and musical achievement.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".