Optimization Principles in Pedagogical Method Selection: A Topology Optimization-Inspired Framework for Teaching STEM Concepts in Indonesian High Schools

Muhammad Ihsan; Andri Afrizal; Andy Prasetyo Wati

doi:10.56806/jh.v6i4.400

Authors

Muhammad Ihsan Engineering Faculty, Universitas Gajah Putih, Takengon, 24552
Andri Afrizal Mechanical Engineering Department, Sekolah Tinggi Iskandar Thani, Aceh
Andy Prasetyo Wati Faculty of Economics and Business, Universitas Negeri Malang, Malang

DOI:

https://doi.org/10.56806/jh.v6i4.400

Keywords:

educational optimization, pedagogical frameworks, STEM education, topology optimization, resource-constrained education

Abstract

Pedagogical optimization frameworks are becoming more popular as developed countries struggle to improve STEM (Science, Technology, Engineering and Mathematics) education quality. This systematic review combines STEM teaching method selection and optimization approachment research. We investigate theoretical foundations, methodological approaches, empirical data, and implementation issues from 2010–2024 peer-reviewed articles. Cognitive learning theories (including Bloom's taxonomy and constructivism), effectiveness research on teaching methods, operations research optimization techniques, and resource-limited educational constraints are all examined in our analysis. The findings show numerous results. First, active learning methods outperform traditional instruction (effect sizes from d=0.40 to d=0.75), but their efficacy varies throughout Bloom's taxonomy levels. Optimization frameworks are useful for timetabling and resource allocation, however pedagogical technique selection is underdeveloped. Third, resource limits in developing countries require context-adapted solutions, not just scaled-down versions of well-resourced ways. Fourth, engineering optimization methods in educational science are promising but have gotten little attention. A review finds several serious shortcomings. Optimization methodologies are rarely empirically validated in teaching situations. Research on simultaneous optimization across several learning objectives with realistic constraints is uncommon. These deficiencies include evidence-based optimization frameworks, rigorous testing across varied settings, and substantial scalability and cost-effectiveness research, especially in resource-limited contexts.

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