A Novel Approach to Looking at the Role of Autonomy Support in the Development of Passion in Education
Bibliographic record
Abstract
The current research demonstrates a novel approach to investigating the role ofperceived teacher and parental autonomy support in college students’ ( N = 970 with376 males, 594 females) passion for science. Based on the Dualistic Model of Passionwhich posits the existence of a harmonious (HP) and obsessive (OP) passion, weadopted a 2 x 2 model (Gaudreau & Thompson, 2010) to test if low and high levels ofperceived parental and teacher autonomy support were differentially associated withstudents’ harmonious and obsessive passion. First, students' perceptions of high levels of both teacher and parental autonomy support rendered the highest means in HP and OP. Second, students who demonstrated high levels of only teacher autonomy support also displayed high levels of HP and OP. Third, OP levels were lowest when teacher autonomy support was low, while those from parents were high. Finally, perceived low support from both parents and teachers was not as ideal as having only support from parents to keep OP at the lowest levels. In sum, the results demonstrate the benefits of having both forms of autonomy support and highlight the outcomes associated with single-sided or low support. Practical implications highlight the importance of considering sources outside of students’ immediate learning environment when designing interventions based on autonomy support.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".