Students' motivational profiles and achievement outcomes in physical education: A self-determination perspective.
Bibliographic record
Abstract
Previous studies in educational settings have examined the relations between students' intrinsic and extrinsic motivation and relevant outcomes. In most of those studies a global indicator of self-determined motivation as defined by Self-Determination Theory was created to examine the relations between motivation as a global construct, its antecedents and specific outcomes. The purpose of this paper was to extend this line of research by examining (1) whether the different types of motivation proposed by SDT can combine into distinct profiles as identified by cluster analysis and (2) the links between those profiles and objective criteria of achievement. In Study 1, motivation toward physical education was assessed at the beginning of a 10-week gymnastics teaching cycle and performance was assessed at the end of the cycle, among a sample of high school students (N= 215). Study 2 (N=210) extended the results of Study 1 by controlling the initial performance of the students, measuring the effort they exerted and recording the grade obtained. Cluster analyses revealed three motivational profiles: self-determined, non self-determined and moderate levels of both types of motivation. Path analysis showed that the self-determined profile was related to the best level of achievement. The results are discussed in terms of their implications for the assessment of students' motivational profile and the consequences that those profiles may have for educational outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".