Integrating STEM and Deep Learning in Renewable Energy Learning to Foster Elementary Students’ Scientific Literacy
DOI:
10.29303/jppipa.v12i6.15602Published:
2026-06-25Downloads
Abstract
This study examined the implementation of STEM-based Deep Learning in fostering elementary school students’ scientific literacy on the topic of renewable energy. A quantitative approach with a one-group pretest-posttest design was employed. The participants were 29 sixth-grade students from an elementary school in East Jakarta. Data were collected using a 20-item scientific literacy test covering conceptual and contextual understanding of renewable energy and were analyzed using the Rasch Model with the stacking technique in Winsteps. The results showed an overall increase in students’ scientific literacy after the learning intervention, as indicated by higher posttest measures compared to pretest measures for most students. STEM-based Deep Learning was implemented through contextual and project-based activities that engaged students in exploring renewable energy concepts, collaborating in groups, assembling simple renewable energy models, and presenting their work. These learning experiences encouraged students to connect scientific concepts with real-life situations and actively participate in the learning process. The findings suggest that STEM-based Deep Learning can support the development of scientific literacy in elementary science learning, particularly on renewable energy topics. Further studies involving larger samples and comparison groups are recommended to strengthen the evidence regarding its effectiveness.
Keywords:
Deep learning Elementary school Renewable energy Scientific literacy STEMReferences
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