Exploring creative thinking skills in PISA: an ecological perspective on high-performing countries
Bibliographic record
Abstract
INTRODUCTION: Creative thinking is a growing focus in educational reforms worldwide. Extensive research explores its development, measurement, various conceptions, and pedagogical approaches like creative teaching and teaching for creativity. A significant development in this area was the inclusion of creative thinking as an innovation domain in the 2022 Programme for International Student Assessment (PISA). The PISA 2022 assessment sought to evaluate the ability of 15-year-old students across 64 jurisdictions to generate, evaluate, and improve original and diverse ideas. METHODS: This paper examines the key findings of the PISA 2022 creative thinking results from three high-performing jurisdictions in different parts of the world: Singapore, Canada, and Finland. We use Bronfenbrenner’s ecological perspective to understand the interplay of various systemic influences on students’ creative thinking abilities within these educational contexts. RESULTS: Our analysis, informed by Bronfenbrenner’s framework, highlights how different ecological systems may contribute to their observed outcomes. DISCUSSION: While acknowledging the complexities and potential pitfalls of directly transferring educational policies between countries, this paper discusses the implications of these findings for school education. We suggest that researchers, policymakers, and educators can gain valuable insights by examining the policies, contexts, and practices of these high-performing nations through an ecological lens, fostering a deeper understanding of how to nurture creative thinking in diverse educational settings.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".