Instructional practices and science performance of 10 top-performing regions in PISA 2015
Bibliographic record
Abstract
This study analysed 10 top-performing regions in PISA 2015 on their science performances and instructional practices. The regions include Singapore, Japan, Estonia, Taipei, Finland, Macao, Canada, Hong Kong, China and Korea. The science performances of the 10 regions and their teaching practices are described and compared. The construct of enquiry-based instruction as developed in PISA 2015 is revised into two new constructs using factor analysis. Then, the relationships of the teaching practices with science performance are analysed using hierarchical linear modelling. Adaptive instruction, teacher-directed instruction and interactive application are found positively associated with performance in all regions, while investigation and perceived feedback are all negative. The regions except Japan and Korea tend to have a high frequency of teacher-directed instruction facilitated by more or less authoritative class discussion in class. A fair amount of practical work is done, but not many of them are investigations. The cultural influences on teaching practices are discussed on how an amalgam of didactic and constructivist pedagogy is created by the Western progressive educational philosophy meeting the Confucian culture. The reasons for investigation’s negative association with performance are also explored.
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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) | 0.000 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".