A meta‐analysis of the association between teacher support and school engagement
Bibliographic record
Abstract
Abstract School engagement is a multidimensional concept describing how students behave, feel, and think. Previous meta‐analyses suggest that school engagement may be underpinned by specific aspects of teacher support. However, given that school engagement is also multifaceted, it is important to examine how each aspect of school engagement is related to different aspects of teacher support. Thus, a meta‐analysis was conducted to ascertain the magnitude of the association between different domains of teacher support and various dimensions of school engagement. We also considered the moderating roles of study (e.g., study design) and sample characteristics (e.g., school level). Of the 1249 studies identified from three databases, 141 studies (i.e., 525,129 students) met the inclusion criteria. Results indicated that teacher support was positively associated with school engagement, but the magnitude of this association differed depending on which aspects of teacher support and school engagement were examined. Significant moderating effects were evident for sample language discrepancy (i.e., discrepancy between the language spoken by the student at home and in school), school level, sex, study design, and informants. Current findings emphasize a need to adopt a comprehensive approach when examining teacher support and school engagement. Findings also suggest the importance of fostering an emotionally supportive school context to promote school engagement among students. Implications for educational research and practice are discussed.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".