Integrating Deep Learning into STEM-Based E-Modules: Advancing Quality Education (SDG 4) in Junior High School Science - Dewi Kartina; Rayendra; Zelhendri Zen; Abna Hidayati (Jurnal Penelitian Pendidikan IPA, 2026)
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.