Indigenous Ecological Knowledge in STEM Education: A Geographical Perspective and Cultural Integration
Bibliographic record
Abstract
This research explored incorporating Indigenous pedagogies, which are teaching methods and strategies that are rooted in Indigenous cultures and traditions, alongside traditional ecological knowledge (TEK) into STEM teaching. It focused on secondary schools in different geographical areas of the Tekari Block of Gaya in Bihar. Using mixed methods that include the quantitative evaluation of one hundred students alongside qualitative data from thirty educators and holders of Indigenous knowledge, the study explored the impact of blending conventional education frameworks on students’ scientific literacy and achievement at the grade level. The research focuses on two primary goals: understanding the impact of traditional ecological knowledge on modern scientific perception and examining the effect of indigenous teaching practices on learner outcomes within STEM frameworks. Research indicates that learners who engage with integrated Indigenous-STEM curricula demonstrate the following outcomes: (1) complexity of ecological systems is transcended with enhanced traditional knowledge perspectives, (2) strong bridging of theoretical concepts with real-world practices, (3) increased participation in STEM fields, especially among Indigenous learners, and (4) more comprehensive problem-solving skills in devising solutions for multidisciplinary environmental issues. The study hypothesises that integrating Indigenous methods-experiential learning, storytelling, and the passing down of knowledge between generations-alongside contemporary STEM teaching methodologies will not just create a learning experience, but a more constructive one for all students. Furthermore, some observations suggest that this could improve student retention of scientific material and increase interest in STEM subjects. These outcomes enrich culturally responsive STEM education models and deepen the strategies anchored on empirical evidence for the use of Indigenous knowledge systems within the conventional education framework. The research also illustrates the ways the Canadian Indigenous traditional ecological knowledge systems can complement science to nurture sustainable environmental education.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".