Instructional practices and students’ reading performance: a comparative study of 10 top performing regions in PISA 2018
Bibliographic record
Abstract
Abstract This comparative study investigated the associations between instructional practices and students’ reading performance among 10 top performing regions that participated in the Program for International Student Assessment (PISA) 2018. A nationally representative sample consisting of 80,016 15-year-old students from 5 Asian regions (B-S-J-Z [China], Singapore, Macao, Hong Kong, and Korea) and 5 Western regions (Estonia, Canada, Finland, Ireland, and Poland) were included. A secondary analysis of PISA survey and assessment data was conducted. T test and ANOVA analyses revealed systematic differences in instructional practices of the 10 regions. B-S-J-Z (China) had significantly higher levels of teacher support, teacher-directed instruction, and teacher stimulation than the other sample regions. Asian regions tended to have higher levels of teacher support, teacher-directed instruction, teacher feedback, adaptive instruction, and teacher enthusiasm compared with Western regions, although variations were also found within Asian regions or within Western regions. Hierarchical linear regression (HLR) analyses indicated that reading performance was positively predicted by teacher support, adaptive instruction, teacher stimulation, and teacher enthusiasm, but negatively predicted by teacher-directed instruction and teacher feedback. This study sheds light on the effective instructional practices for optimizing students’ reading performance across different cultural contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".