Integrating Indigenous Knowledge in Science Education: A Systematic Review of Strategies, Models, and Impacts
Bibliographic record
Abstract
Integrating Indigenous Knowledge (IK) has received special attention to realize science learning that is relevant, contextual, and close to students' daily lives. This study aims to systematically review empirical research on the forms of Indigenous Knowledge integrated in science learning, science learning strategies, methods, or models that integrate Indigenous Knowledge, and the impact or influence of science learning that integrates Indigenous Knowledge. The articles were collected from November to December 2024 through Scopus and ERIC databases. The number of articles that met the inclusion criteria was 43 out of 558 articles collected. The findings showed that Indigenous Knowledge can be classified into eleven categories: culture, traditional games, traditions, ethnoecology, ethno-agriculture, ethno-botany, ethno-biology, ethno-medicine, traditional technology, ethno-chemistry, and ethno-science. Implementing the IK program in science learning is divided into seven categories: project-based learning and social action, discussion and reflection-based learning, technology-based learning, field research-based learning, local knowledge-based learning, cultural practice-based learning, and reflective learning. The positive impacts of IK integration include increased appreciation of nature and biodiversity, Indigenous culture and Knowledge, thinking and soft skills, mastery of material content, and student motivation and engagement in 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 | 0.027 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".