STEM-Based Deep Learning in Food Security Education for Sustainable Development Goals: A Systematic Review of Scientific Literacy and Self-Efficacy
DOI:
10.29303/jppipa.v12i6.15022Published:
2026-06-25Downloads
Abstract
This study aims to synthesize research on STEM-based deep or meaningful learning in food security and sustainability education, with a particular focus on its relationship with scientific literacy and self-efficacy. A systematic literature review was conducted using the PRISMA 2020 framework across Scopus, ERIC, Google Scholar, and SINTA-indexed sources. The screening process resulted in nine core studies that met the predefined inclusion criteria. The findings reveal that existing research is structurally fragmented across three domains: contextual STEM learning, pedagogical processes, and cognitive–affective outcomes. While STEM-based learning is widely applied in sustainability contexts, food security is predominantly treated as a thematic background rather than an epistemic learning domain. Furthermore, scientific literacy and self-efficacy are typically examined as separate constructs, limiting the understanding of how cognitive and affective dimensions interact in learning processes. In response to these gaps, this study proposes an integrative framework that connects food security as an epistemic context, STEM as an interdisciplinary structure, deep learning as a cognitive mechanism, and scientific literacy and self-efficacy as interconnected outcomes. In conclusion, this study highlights the need for integrative, context-driven STEM learning models to support meaningful and transferable learning in sustainability education.
Keywords:
Deep learning Food security Scientific literacy Self-efficacy STEM educationReferences
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