Malay Ethnomathematics Module Using Batik Nagori Kuansing for Elementary Geometry
Bibliographic record
Abstract
This study aims to develop and examine the effectiveness of a Riau Malay ethnomathematics-based learning module integrating the Batik Nagori Kuansing motif for elementary plane geometry instruction. The study employed a Research and Development approach using the ADDIE model, consisting of analysis, design, development, implementation, and evaluation stages. The module’s validity was assessed by three expert validators and achieved an Aiken’s V index of 0.87, indicating a high level of validity. Practicality was evaluated through teacher and student questionnaires, with results showing highly practical categories, as reflected by teacher responses of 87.84% and student responses of 91.76%. To measure effectiveness, a pretest–posttest design was implemented involving an experimental group and a control group. The results revealed a statistically significant improvement in the experimental group’s learning outcomes after using the ethnomathematics-based module, while no significant improvement was observed in the control group. These findings demonstrate that the ethnomathematics-based module integrating Batik Nagori Kuansing motifs is valid, practical, and effective in enhancing elementary students’ understanding of plane geometry. The integration of culturally meaningful motifs supports students in connecting abstract geometric concepts with real-life cultural contexts. The developed module is therefore suitable for implementation in elementary mathematics learning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".