Profiles of Classroom Management Across Five Countries: A Person-Centered Analysis of TALIS 2018 Data
Bibliographic record
Abstract
Classroom management is a crucial aspect of effective teaching. However, little is known about how teachers’ approaches vary across countries. This study identified classroom management profiles using data from the OECD’s Teaching and Learning International Survey in five countries: Brazil, Canada (Alberta), Japan, Portugal, and South Africa. We applied latent class analysis (LCA) to four behavioral indicators, testing structural invariance and exploring associations with teacher characteristics and cultural dimensions. Three profiles emerged: Rule-Enforcing, Rule-Balanced, and Rule-Avoidant, which were structurally invariant across countries but varied in prevalence. Rule-Enforcing teachers reported the highest classroom management self-efficacy, whereas Rule-Avoidant teachers reported the lowest, with differences also observed in instructional and engagement efficacy. Cross-national variation in profile prevalence aligned descriptively with Hofstede’s cultural values, suggesting that cultural context shapes how universal management dimensions are enacted. These findings support the notion that classroom management is a universal construct shaped by significant national and cultural specificities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".