Understanding and measuring student engagement in school: The results of an international study from 12 countries.
Bibliographic record
Abstract
The objective of the present study was to develop a scale that is appropriate for use internationally to measure affective, behavioral, and cognitive dimensions of student engagement. Psychometric properties of this scale were examined with data of 3,420 students (7th, 8th, and 9th grade) from 12 countries (Austria, Canada, China, Cyprus, Estonia, Greece, Malta, Portugal, Romania, South Korea, the United Kingdom, and the United States). The intraclass correlation of the full-scale scores of student engagement between countries revealed that it was appropriate to aggregate the data from the 12 countries for further analyses. Coefficient alphas revealed good internal consistency. Test-retest reliability coefficients were also acceptable. Confirmatory factor analyses indicated that the data fit well to a second-order model with affective, behavioral, and cognitive engagement as the first-order factors and student engagement as the second-order factor. The results support the use of this scale to measure student engagement as a metaconstruct. Furthermore, the significant correlations of the scale with instructional practices, teacher support, peer support, parent support, emotions, academic performance, and school conduct indicated good concurrent validity of the scale. Considerations and implications regarding the international use of this student engagement in school measure 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".