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Educational Effects of Ethnoscience, Indigenous Knowledge, and Local Wisdom in Science and STEM Education
Lalu Usman Ali1,2, Muhammad Fajar Marsuki1,3, Sony Yunior Erlangga1,4
1Doctoral Program in Natural Science Education, Universitas Sebelas Maret, Surakarta, Central Java, 57126, Indonesia.
Abstract:
The integration of ethnoscience, Indigenous knowledge, local wisdom, and culturally contextualized pedagogy has been proposed as a means of improving the relevance of science and STEM education, yet the magnitude and consistency of its educational effects remain uncertain. This updated systematic review and meta-analysis synthesized 31 study-level estimates comprising 5,164 observations. The review followed PRISMA 2020, and intervention effects were expressed as bias-corrected standardized mean differences (Hedges' g) and synthesized using an inverse-variance random-effects model with restricted maximum-likelihood estimation. Potential variation was examined across educational level, outcome type, instructional model, science subject, cultural context, research design, intervention duration, and media type. The pooled estimate favoured the intervention conditions (g = 1.17, 95% CI [0.88, 1.46], p < .001), but between-study heterogeneity was substantial (I 2 = 91.5%, τ 2 = 0.5766). Educational level, outcome type, instructional model, science subject, cultural context, and intervention duration did not show statistically detectable between-category differences. Media type produced a significant omnibus test, although several categories contained few studies and imprecise estimates, so this finding was treated as exploratory. The funnel plot showed possible asymmetry but did not establish publication bias. Overall, the evidence indicates a positive average association between culturally contextualized science interventions and educational outcomes, while the magnitude of effects varies considerably across studies. The findings support cautious, context-sensitive implementation and highlight the need for stronger designs, clearer intervention descriptions, and more consistent moderator coding.
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