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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Teaching and Learning Physics Journal of Innovation in Applied Natural Science
Bambang Heriyadi
Universitas Jambi
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Energy Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics

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Rekonstruksi Pembelajaran Fisika Berbasis Kontekstual dengan pendekatan STEM Tradisi Mandi kasai Lubuklinggau serta Implikasinya terhadap Asesmen Pembelajaran Yaspin Yolanda; Bambang Heriyadi; Pinta Murni; Muhammad Naswir
Journal of Teaching and Learning Physics Vol. 11 No. 2 (2026): Journal of Teaching and Learning Physics (August 2026)
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jotalp.v11i2.55714

Abstract

The problem is that the use of ethnoscience as a learning resource has not been optimized, so local traditions have not been scientifically reconstructed. In addition, the limited availability of contextual STEM assessment tools leaves teachers without clear guidance, making physics instruction less challenging. The objectives of this study are (a) to analyze physics concepts within the Mandi Kasai tradition, (b) to map the relationships between these concepts within the STEM (Science, Technology, Engineering, Mathematics) framework, and (c) to develop context-based learning assessments relevant to local culture. The method used was qualitative research with an ethnographic approach, involving observation, interviews with 3 physics teachers, 3 traditional leaders, and 4 community members who practice the tradition, as well as documentation and a literature review. Data analysis was conducted through the stages of reduction, presentation, and conclusion. The results of the study indicate that the mandi kasai tradition integrates various physics concepts such as heat, heat transfer, changes in the state of matter, and fluids. The resulting assessment is valid based on expert validation. A recommendation for future research is to develop an ethnoscience-based physics module.
Insights into Artificial Intelligence and Ethnoscience in STEM: Systematic Literature Review (SLR) for Pre-Service Physics Teachers Yaspin Yolanda; Maison; Damris. M; Bambang Heriyadi; Imam Arif Pribadi
Journal of Innovation in Applied Natural Science Vol. 2 No. 2 (2026): Journal of Innovation in Applied Natural Science
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jinas.v2i2.228

Abstract

Background: Integrating Artificial Intelligence (AI), ethnoscience, and STEM education is increasingly important for preparing pre-service physics teachers to address technological, interdisciplinary, and culturally responsive demands. However, research in Indonesia remains fragmented, with AI, STEM, and ethnoscience investigated as separate domains, limiting integrated approaches to physics teacher preparation. Aims: This study identifies publication trends, dominant themes, methodological characteristics, and gaps in integrating AI, ethnoscience, and STEM within Indonesian pre-service physics teacher education. Methods: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, an integrative literature review examined publications from 2015 to 2026 from Google Scholar, IEEE Xplore, Scopus, and ERIC. Following title, abstract, and full-text screening, 51 articles were selected. Data were analyzed using descriptive bibliometrics, thematic analysis, and gap analysis. Results: Three trends emerged. First, research emphasizes AI- and STEM-enhanced learning, conceptual understanding, and 21st-century competencies. Second, ethnoscience studies promote local wisdom, contextual learning, cultural literacy, and instructional resources, but remain disconnected from AI-supported STEM learning. Third, comprehensive frameworks integrating AI, ethnoscience, and STEM for pre-service physics teacher education remain scarce. Developmental and qualitative approaches dominate existing studies, while mixed-methods, longitudinal, and intervention-based research remains limited. Gaps persist in examining pre-service teachers’ AI literacy, TPACK, prior knowledge, and capacity to design culturally contextualized STEM learning supported by AI. Conclusion: The findings reveal a research gap and provide a foundation for developing empirically validated, culturally responsive frameworks integrating AI, STEM, and ethnoscience to strengthen AI literacy, TPACK, STEM competencies, and contextual pedagogical practice in physics teacher education.  
Co-Authors -, Maison Damris. M Imam Arif Pribadi Muhammad Naswir Pinta Murni Yolanda, Yaspin