Integrating Deep Learning into STEM-Based E-Modules: Advancing Quality Education (SDG 4) in Junior High School Science
DOI:
10.29303/jppipa.v12i2.14035Published:
2026-02-25Downloads
Abstract
The rapid evolution of digital technology in the 21st century necessitates innovative teaching materials that foster deep conceptual understanding. This study aims to develop and evaluate a STEM-based electronic module (e-module) integrated with a deep learning approach for Grade IX junior high school science, specifically focusing on static and dynamic electricity. Using the Research and Development (R&D) method with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation), the e-module was designed to incorporate interactive multimedia, STEM-based activities, and deep learning principles—emphasizing understanding, application, and reflection. Results from expert validation (media, material, and language) yielded an average score of 90.73%, indicating very high validity. Practicality testing involving students and teachers resulted in an average score of 91.82%, categorizing the module as very practical. Furthermore, the effectiveness test showed a significant improvement in student learning outcomes, with the average score rising from 62.50 to 85.63 and an N-Gain value of 0.62 (medium category). These findings demonstrate that the STEM-based e-module with a deep learning approach is a valid, practical, and effective tool for enhancing students' conceptual mastery in the digital era.
Keywords:
Development Deep learning E-Modules Science STEMReferences
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