Comparative Analysis of The History of Mathematics Content in The Secondary School Mathematics Textbooks of Turkey, Singapore, Ireland and Canada
Bibliographic record
Abstract
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.
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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 | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".