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Record W4293481139 · doi:10.29329/ijpe.2022.459.5

Comparative Analysis of The History of Mathematics Content in The Secondary School Mathematics Textbooks of Turkey, Singapore, Ireland and Canada

2022· article· en· W4293481139 on OpenAlexaboutno aff
Nazan Mersin

Bibliographic record

VenueInternational Journal of Progressive Education · 2022
Typearticle
Languageen
FieldMathematics
TopicHistory and Theory of Mathematics
Canadian institutionsnot available
Fundersnot available
KeywordsCivilizationMathematics educationContent analysisNorthern irelandMathematicsSocial scienceGeographySociologyArchaeologyEthnology

Abstract

fetched live from OpenAlex

This study seeks to offer a comparative analysis of the History of Mathematics (HoM) elements identified in the secondary school mathematics textbooks of different countries. Drawing on document analysis method, this study analyzes the secondary school mathematics textbooks of Turkey, Singapore, Ireland and Canada. The HoM elements in the textbooks are examined in terms of famous mathematicians they present, civilization they are related to, content type, associated learning area and whereabouts they are inserted in the text. This study concludes that the textbooks of Ireland present the highest number of HoM elements quantitatively, and Ireland is followed by Turkey, Singapore and Canada, respectively. The most mentioned mathematicians in the HoM elements are Al-Khwarizmi and Pythagoras; further, the most mentioned civilization is Ancient Egypt. Further, Singapore and Canada prioritize discussion-project whereas Ireland and Turkey focus on history of concepts. Moreover, Turkey, Ireland and Canada present the highest number of HoM elements in the learning area of geometry and measurement. Singapore has the highest number of HoM elements in the area of numbers and operations. This study reveals that the countries do not sufficiently incorporate HoM into their textbooks. The countries with relatively higher number of HoM elements like Ireland use HoM for motivational purposes, rather than for teaching purposes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.048
GPT teacher head0.312
Teacher spread0.264 · 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 designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

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