PISA and Sustainable Development Goals: Comparing Science Curricula in Secondary Schools in Indonesia, Singapore, Australia, and Canada in the Content Aspect Based on the PISA 2025 Framework
Bibliographic record
Abstract
Continuous curriculum updates are crucial for enhancing the quality of education, improving citizens' global competitiveness, and supporting Sustainable Development Goal (SDG) 4, particularly the goal of achieving quality education for all. International studies, such as PISA, have attracted many researchers interested in comparative curricula across PISA-participating countries. Unlike previous studies, this research compares the science curricula of Indonesia, Singapore, Australia, and Canada based on the science content tested in the PISA 2015, PISA 2021, and PISA 2025 frameworks. The method used in this study is content analysis. We searched for documents from the four countries on their respective Ministries of Education websites. After obtaining the documents, two researchers independently conducted the coding analysis. After that, the researchers validated the content analysis through inter-rater agreement. The results show that the science curricula of Indonesia, Singapore, Australia, and Canada do not specifically cover all content in the PISA 2015, PISA 2021, and PISA 2025 frameworks. Specifically, Singapore's curriculum documents do not cover Earth and Space System content, while Labrador, Alberta, and British Columbia cover all themes. The Indonesian science curriculum encompasses all themes assessed in PISA questions and the PISA 2025 framework, although it does not yet cover all topics. However, it remains challenging to pinpoint the reasons for the differences in PISA results among the four countries in this comparative study.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".