Intrinsic, identified, and controlled types of motivation for school subjects in young elementary school children
Bibliographic record
Abstract
BACKGROUND: There are two approaches to the differential examination of school motivation. The first is to examine motivation towards specific school subjects (between school subject differentiation). The second is to examine school motivation as a multidimensional concept that varies in terms of not only intensity but also quality (within school subject differentiation). These two differential approaches have led to important discoveries and provided a better understanding of student motivational dynamics. However, little research has combined these two approaches. AIMS: This study examines young elementary students' motivations across school subjects (writing, reading, and maths) from the stance of self-determination theory. First, we tested whether children self-report different levels of intrinsic, identified, and controlled motivation towards specific school subjects. Second, we verified whether children self-report differentiated types of motivation across school subjects. SAMPLE: Participants were 425 French-Canadian children (225 girls, 200 boys) from three elementary schools. Children were in Grades 1 (N=121), 2 (N=126), and 3 (N=178). RESULTS: Results show that, for a given school subject, young elementary students self-report different levels of intrinsic, identified, and controlled motivation. Results also indicate that children self-report different levels of motivation types across school subjects. Our findings also show that most differentiation effects increase across grades. Some gender effects were also observed. CONCLUSION: These results highlight the importance of distinguishing among types of school motivation towards specific school subjects in the early elementary years.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".