Multilevel student-perceived teaching practices profiles: Associations with competence beliefs, task value, behavioral engagement, and academic achievement
Bibliographic record
Abstract
The study assesses student perceptions of their teachers' practices and their associations with motivational and academic achievement. Multilevel latent profile analyses (3710 grade 9 students, 245 classrooms) identified five profiles at the student level: Average with focus on rules (20.50 %), Average with focus on need-support (30.45 %), Differential treatment (18.86 %), Need-support and differential treatment (11.70 %), and High-on-all (18.50 %). Students corresponding to the High-on-all profile reported the most positive outcomes. We identified three profiles at the classroom level: Mostly differential treatment (20.75 %), which was associated with the worst outcomes, Average and high-on-all (41.20 %), and Mostly need-supportive (38.06 %). This study has implications for initial and continuing teacher training. By identifying profiles of teaching practices perceived by students and classrooms, the study informs what combinations of practices are positively associated with different aspects of student motivation and achievement, according to their perceptions. The findings also contribute to understanding that some practices (e.g., differential treatment), generally thought to deplete student motivation, might not need to be proscribed as long as they are counterbalanced with high levels of other positive practices (e.g., need supportive practices and rule enforcement). • We identified combinations of rule, differential treatment, need-support practices. • Students and classrooms have relatively coherent perceptions of teaching practices. • Positive global climate is associated with student motivation, engagement, and achievement. • Positive practices (rules, need-support) may compensate for differential treatment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".