PROBLEM BASED LEARNING (PBL) BERBASIS ETNOSAINS DAN ETNOMATEMATIK
Bibliographic record
Abstract
This paper aims to see how PBL is integrated with ethnoscience and ethnomamatics. PBL as a contextual model of students often does not find a new direction in approach. This research introduces a new direction of PBL learning schemes through ethnoscience and ethnomamatics. Descriptive qualitative research of Cronin et al., (2008), namely (1) choosing a review topic, (2) finding and selecting appropriate articles, (3) analyzing and synthesizing literature, and 4) conducting a review of writing in a Mix with the development of Thiagarajan's definition. , design, development and dissemination. The results of this study (a) Ethnoscience-based PBL has three conceptual steps (1) A new conceptual framework that is a synergy between ethnoscience and PBL to improve contextuality and meaning in science learning, (2) Steps for developing ethnoscience-based learning, and 3) steps to integrate ethnoscience into PBL and how to apply it in science learning. (b) A PBL-based ethnomathematics approach may improve understanding of mathematics. The integration of teacher performance, student activities, and infrastructure utilization can improve understanding of mathematics based on ethnomathematics designed with a Four-D model (research design) which consists of (1) defining, (2) designing, (3) developing, and (4) spreading.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".