Comparison of Students’ Math Achievement in Two Nordic Countries: Multi-level Analysis of PISA Results
Bibliographic record
Abstract
The present study was aimed to investigate whether Finnish and Danish students’ math achievement differed and which student-level factors, if any, explained the achievement gaps and whether teacher participation in decision making and teacher morale, among the school-level factors, explained the achievement gaps in Finland and Denmark. To this end, this study used both student- and school-level datasets of two countries from the Program for International Student Assessment (PISA) 2012 study and employed hierarchical linear models (HLM) – a fully unconditional, a partially unconditional, and a fully conditional model to address the hierarchical structure of research units and importance of predictors of math achievement at each level. Results indicated that Finnish students outperformed Danish peers in math achievement and that between-school homogeneity and gender equity were more salient in Finland than in Denmark. However, the findings of HLM showed that neither teacher participation in decision making nor teacher morale was associated with student math achievement in Finland and Denmark. The findings indicated that neither teacher participation in decision making nor teacher morale translated directly into improved student achievement among Finnish and Danish 15-year-old students, when adjusting for various student and school-level factors. Implications and future studies related to database linking and potential mechanisms, such as school principals’ leadership traits and practices, in the relationships between teacher participation in decision making, teacher morale, and student achievement were briefly 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".