Self-determination theory and motivation for music
Bibliographic record
Abstract
Learning the skills to be a musician requires an enormous amount of effort and dedication, a long-term process that requires sustained motivation. Motivation for music is complex, blending relatively intrinsic and extrinsic motives. The purpose of this study is to investigate the motivation of musicians by considering how different aspects of motivational features interact. An international sample of 188 musicians was obtained through the use of an online survey. Four scales drawn from Self-Determination Theory (intrinsic, identified, introjected, and extrinsic regulation) were utilized along with other motivational constructs, including motivational intensity, desire to learn, willingness to play, perceived competence, and musical self-esteem. To integrate the variables into a proposed model, a path analysis was conducted among the motivation variables. Results showed that the intrinsic motives are playing the major role in the maintenance of the motivational system, while extrinsic motives are less influential. Support was found for a feedback loop, whereby desire to learn feeds into increased effort at learning (i.e., motivational intensity), leading to the development of perceived competence, which is then reflected back into increasing desire to learn. Increases in these variables help to create a virtuous cycle of motivation for music learning and performance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".