Engklek: Ethnomathematics-Based Traditional Games in The Development of Teaching Materials to Build Mathematical Literacy Skills
Bibliographic record
Abstract
Changes in Indonesia’s educational curriculum emphasize the importance of students' reasoning skills in mathematics. However, Indonesia's low ranking in the PISA assessment, particularly in mathematics, highlights students limited mathematical literacy. One major contributing factor is the fear and anxiety students feel towards mathematics, which is often viewed as intimidating. This study aims to review literature on the impact of integrating geometry-based mathematics learning with the traditional game engklek. The research method employed is a Systematic Literature Review (SLR) using the SALSA framework and a descriptive analysis approach. Data were gathered from 17 articles published between 2014 and 2024, covering reputable international journals, accredited national journals, and journals indexed by DOAJ and Copernicus. The analysis identified 9 articles reporting that geometry topics requiring reasoning and literacy skills are better understood by elementary and preschool students when taught through traditional games like engklek. Furthermore, 8 articles presented experimental studies showing that innovative designs integrating engklek into geometry instruction significantly improve problem-solving skills, numerical literacy, and geometric literacy. Effective instructional models for geometry learning include Realistic Mathematics Education (RME), RME with the Hypothetical Learning Trajectory (HLT) approach, PMRI, didactic designs, and the development of student worksheets (LKPD). This review concludes that integrating geometry learning with engklek ethnomathematics offers an engaging and culturally relevant approach to overcoming students’ fear of mathematics while improving their mathematical literacy and reasoning skills
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".