Economic Education Concepts in School Mathematics Textbooks for Middle Stage in Saudi Arabia
Bibliographic record
Abstract
The world faces some economic challenges, which may cause a financial crisis. The school curricula must pay attention to its role in the field of economic education for students, to prepare them to deal with current and future economic challenges. By using descriptive analysis method, the research aimed to identify the contributions of school mathematics textbooks to economic education, through the economic concepts, for middle stage students in Saudi Arabia. The researcher identified five areas of concepts that contribute to denoting and promoting economic education: financial transactions, consumption, investment, savings, and rationalization. According to the methodological steps of the content analysis method, the results showed that the school mathematics textbooks highly contribute to promoting economic education in concepts related to the field of financial transactions (65.88%), with very large percentage. Promoting the concepts of consumption came in second place (18.83%), while the prompting economic education for students was very low at three concepts, investment, saving, and rationalization (7.08%, 6.03%, 2.18%). The research recommended the need to pay attention to all areas and concepts of economic education in mathematics textbooks at the middle stage, in balanced proportions, and to reconsider the frequency and percentages of economic education concepts included in the current mathematics textbooks, and mathematics textbooks should be developed to promote the economic education for students.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".