Ethnoscience-Based Biomechanics Learning (2020–2026): A Bibliometric Mapping of Thematic Clusters, Emerging Trends, and Research Frontiers
DOI:
10.29303/ijcse.v4i3.1734Published:
2026-09-30Downloads
Abstract
This study maps the research landscape of Ethnoscience-Based Biomechanics Learning for the 2020–2026 period through bibliometric analysis to identify thematic clusters, emerging trends, and research frontiers. Data were retrieved from Scopus using a query combining the terms ethnoscience/cultural knowledge, biomechanics/motion analysis, and learning/education. The initial search yielded 1,574 documents, which were then gradually refined to 388 documents (years 2020–2026, selected document types, English language, and open access). Mapping was conducted with VOSviewer using keyword co-occurrence analysis, occurrences and total link strength (TLS) indicators, as well as network visualization, overlay (average publication year), and density. The results show that publication output rose steadily from 22 documents in 2020 to 109 documents in 2025, the highest annual count observed within the analyzed period (2020–2025); the largest contributions come from China, Italy, and the United States. The keyword network forms four clusters: (1) digital cultural heritage and technology (AI/ML, 3D modeling, VR/AR, gamification) as enablers; (2) the core cluster of culture–education/learning–biomechanics (motion analysis, simulation, prediction, training); (3) physical activity–public health–ergonomics as the outcome domain; and (4) psychometrics–reliability–reproducibility as the foundation for methodological rigor. The overlay indicates a shift in the frontier toward integrating AI/deep learning, motion analysis, and immersive technologies for visualization and motion feedback. This study recommends strengthening curriculum/training design and reliable and replicable multi-source evaluation. These findings provide a roadmap for cross-disciplinary collaboration for teachers, researchers, and educational technology developers.
Keywords:
Ethnoscience Biomechanics Learning Bibliometric Mapping Vosviewer Artificial IntelligenceLicense
Copyright (c) 2026 Intan Kusuma Wardani, Joni Rokhmat, Aliefman Hakim, AA Sukarso
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