INTERACTIVE SCIENCE LEARNING MEDIA DESIGN ANDROID BASED TO IMPROVE LEARNING OUTCOMES - Santi Widiastuti; Benyamin Adi Yusuf (LPPM Politeknik Pratama Kendal, 2025)
This study aims to design and evaluate the feasibility of an interactive science learning media based on Android to improve the learning outcomes of fifth-grade students at Mojodemak 1 Elementary School – Demak. This digital media is directed at enhancing student engagement, explaining abstract concepts, and providing a more effective and adaptive learning experience. The methodology adopts a Research and Development framework, employing the ADDIE model as its guiding structure. Across the development phase, the artifacts undergo iterative feedback cycles, first by subject-matter specialists who examine relevance and accuracy, followed by media professionals who assess design suitability and technical integrity. The research subjects consist of 60 students selected through purposive sampling. Analysis results indicate that the Android-based learning media is suitable for implementation, with an average learning improvement of 32%. Student interaction with the application shows a significant positive effect on the enhancement of learning outcomes. The research recommends the development of artificial intelligence (AI) features for more personalised learning. The coefficient of determination of 0.574 indicates that 57.4% of the variation in learning outcomes can be explained by interaction with the media. These findings imply that the use of interactive Android-based media substantially supports students' understanding of science concepts. The effectiveness of the media primarily stems from its appealing visual design, interactive features that encourage deeper exploration, and systematic presentation of materials tailored to the needs of students. This research contributes to advancements in educational technology, particularly in the design and evaluation of Android-based learning media for science education. Future research should centre on the design and testing of AI-enhanced tools that provide adaptive support for differentiated instruction.