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Record W4223927090 · doi:10.1177/27527263221091304

Understanding Chinese students’ success in the PISA financial literacy: A praxeological analysis of financial numeracy

2022· article· en· W4223927090 on OpenAlexafffund
Alexandre Cavalcante, Hui‐Yu Huang

Bibliographic record

VenueAsian Journal for Mathematics Education · 2022
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
FundersMitacs
KeywordsNumeracyFinancial literacyMathematics educationCurriculumPraxeologyLiteracyPedagogyFinancePsychologyEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.390
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes2
Has abstractyes

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