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.