Exploring Deep Learning in Science Education through the SmartEdu Application among Pre-Service Elementary School Teachers

Abstract

The rapid advancement of science and technology requires educational systems to address the limited integration of deep learning practices that effectively develop 21st-century competencies in prospective elementary school teachers. This study aims to examine how the SmartEdu application supports the implementation of deep learning in science education. A qualitative exploratory design was employed, involving classroom observations, interviews, and document analysis, with data analyzed through thematic analysis. The results demonstrate that SmartEdu facilitates the integration of theoretical knowledge with authentic problem-solving, structured discussions, and reflective evaluation, significantly enhancing conceptual understanding, higher-order thinking skills, and learner autonomy. In conclusion, SmartEdu effectively promotes meaningful, contextual, and sustainable science learning while strengthening critical, creative, collaborative, communicative, and reflective competencies. These findings imply that technology-supported deep learning frameworks can serve as a strategic model for improving teacher education programs.