Promising research studies between mathematics literacy and financial literacy through project-based learning
Bibliographic record
Abstract
Financial literacy is a knowledge and attitude about finance and is a 21st-century skill. As a knowledge, a cognitive factor of a person will impact their financial literacy skill. Through a bibliometric analysis study of 274 documents published from 1994 to 2022 in the Scopus database, we found that mathematics literacy is the cognitive factor of one's financial literacy skill. The OECD and several studies offer intertwined concepts, financial literacy and mathematics, to be presented in the mathematics curriculum in primary and secondary schools. After we got the bibliometric results, we surveyed several junior high schools in Yogyakarta to learn more about this issue. A total of 15 mathematics teachers participated in this survey, randomly chosen in junior high school. We got information stating that mathematics teachers used financial terms as a social arithmetic context, did not teach financial knowledge and attitudes, and never heard 13 from 17 financial terms in the survey. Furthermore, we present a framework for implementing financial literacy in mathematics through three dimensions, content, context, and process. The dimension content consists of mathematics' and financial content. On the other hand, the dimension context is related to education and work, home and family, and individual and societal. As a cognitive process, the dimensions of the process are based on Bloom's Cognitive stages. The potential for future research is developing learning activities and implementing them in the independent curriculum, which impacts schools' ability to use project-based learning, which is the most approach to implementing financial literacy in mathematics classes.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".