Empirical Validation of a Model for Predicting Students' Sense of Belonging and School Engagement as a Function of Classroom Management Practices
Bibliographic record
Abstract
Over the last two decades, several studies have overlooked at-school belonging and engagement, two dimensions that are associated with several positive outcomes. However, the relative influence that contexts and interventions may have on these components has received much less attention. In this study, school belonging and engagement were examined as a function of the implementation and application of classroom rules. The study took place in two Moroccan schools, and participants were 238 students from 9th grade (101 boys, 137 girls; Mage = 15.1) living in the cities of Casablanca and Témara. They all completed a questionnaire that allowed to measure their belonging and engagement in conjunction with the manner in which rules are implemented and applied. Correlational and structural equation modeling methods were used to analyze the aforementioned relationships. Results showed that implementation of classroom rules had a positive effect on school belonging, which, in turn, had a positive effect on school engagement. These results indicated the need to conduct further empirical research to measure the contribution of classroom management practices on school belonging.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".