Understanding Chinese students’ success in the PISA financial literacy: A praxeological analysis of financial numeracy
Bibliographic record
Abstract
The goal of this article was to investigate how Chinese mathematics curriculum policies and textbook tasks could help explain the results obtained by Chinese students in the 2012 and 2015 Programme for International Student Assessment (PISA) financial literacy exams. Inspired by the Anthropological Theory of the Didactic, we conducted a praxeological analysis of financial numeracy tasks from middle school textbooks and the PISA. We conceptualized the term financial numeracy as the use, production, and communication of mathematical information in financial situations. The analysis permitted us to contrast the solution to PISA tasks with that of financial tasks from middle school mathematics textbooks. Our results show that, despite the lack of attention to mathematics in the curriculum policies for financial literacy, the mathematics textbooks seem to support the performance of students in the PISA by (a) incorporating more mathematically complex content, (b) tackling equivalent financial concepts, (c) providing students with enough time to consolidate their understanding throughout middle school, and (d) designing pedagogy that revisits these concepts over the years. The implications of this study inform mathematics education research and practice. If we are to incorporate financial literacy in mathematics curricula, this process should be done with intentionality and in connection to multiple mathematical concepts and processes.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".