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The Development of a Deep Learning-Based STEAM Project Module to Enhance Students’ Environmental Literacy through an Eco-Enzyme Initiative

Authors: Novita, Fitriani; Dafik; Maman, Rumanta;

The Development of a Deep Learning-Based STEAM Project Module to Enhance Students’ Environmental Literacy through an Eco-Enzyme Initiative

Abstract

The global environmental crisis demands innovative educational approaches to build environmental literacy from an early age. This study aims to develop a deep-learning-based project module integrated with STEAM (Science, Technology, Engineering, Arts, Mathematics) through an eco-enzyme project to improve the environmental literacy of fifth-grade students at MIN 1 Sidoarjo. The research employed the 4D development model (Define, Design, Develop, Disseminate) with a qualitative– quantitative approach. Data were collected through questionnaires, interviews, observations, and pretest–posttest assessments, then analyzed descriptively and statistically (N-Gain). Validation results from experts in content, media, and pedagogy indicated that the module was highly valid (average scores of 4.26, 3.8, and 4.3). Small- and large-scale trials demonstrated that the module was practical (average student response of 3.4) and effective in enhancing environmental literacy, with significant improvements in both cognitive (N-Gain = 0.70) and affective (N-Gain = 0.72) domains. The eco-enzyme project also strengthens the dimensions of the Pancasila Student Profile, particularly creativity, independence, and collaboration. The implications of this study affirm that integrating STEAM and deep learning within a contextual project module can create meaningful learning, foster 21st-century skills, and cultivate students’ ecological awareness. Recommendations include implementing similar modules in elementary schools and developing educational policies that support project-based environmental learning.

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Keywords

Deep learning, eco-enzyme, Environmental Literacy, project module, STEAM.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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