Teacher structure as a predictor of students’ perceived competence and autonomous motivation: The moderating role of differentiated instruction
Bibliographic record
Abstract
BACKGROUND: An important pedagogical practice is the provision of structure (Farkas & Grolnick, 2010, Motiv. Emot., 34, 266). According to self-determination theory (SDT; Deci & Ryan, 1985, Intrinsic motivation and self-determination in human behavior, Plenum, New York, NY), structure allows students to develop perceived competence in different school subjects, which in turn facilitates the development of autonomous motivation towards these subjects and limits the development of controlled motivation. AIMS: In this study, we test a mediated moderation model that posits that teacher structure has a stronger positive effect on students' autonomous motivation (and a negative effect on controlled motivation) in French class when differentiated instruction is used, and that this moderation effect is mediated by perceived competence. SAMPLE: To test this model, we used a sample of 27 elementary school teachers and 422 students from Quebec, a province of Canada. METHODS: Data for teachers and students were collected with self-report measures. The method used was a correlational one with a single measurement time. RESULTS: Results revealed that (1) the effect of teacher structure on students' autonomous motivation was positive only when differentiated instruction strategies were frequently used, and this moderated effect was partially mediated by perceived competence, and (2) teacher structure was negatively associated with students' controlled motivation only when differentiated instruction was provided infrequently, and this moderated effect was not explained by perceived competence. CONCLUSIONS: These findings are discussed in the light of the literature on SDT and on differentiated instruction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".