<![CDATA[Educational transformation in the digital age requires fundamental changes in how teachers understand the nature of science and the learning process. In this context, the philosophy of science paradigm becomes an important foundation that determines the direction of pedagogical innovation, including the application of deep learning and coding in elementary schools. This study aims to analyze how the philosophy of science paradigm plays a role in directing the transformation of deep learning and coding in elementary schools receiving BOS Kinerja (School Performance-Based Funding). The focus of the study is directed at teachers' perspectives on knowledge, technology, and reflective practices in building a 21st-century learning ecosystem. The method used is a qualitative approach with a case study design, conducted in high-ranking elementary schools based on the results of the National Education Report Card. Data were collected through in-depth interviews, participatory observation, and document analysis, then analyzed thematically using the stages of reduction, categorization, and interpretation of meaning. The results of the study show that teachers who have a constructivist and humanistic paradigm are able to transform learning into a more meaningful, reflective, and collaborative process. Deep learning encourages the development of higher-order thinking and social empathy, while the application of coding plays a role in fostering computational thinking and creativity in students. The support of the BOS Kinerja policy strengthens innovation through the provision of resources and performance-based management. The conclusion of this study confirms that a paradigm shift in the philosophy of science among teachers is the key to the success of learning transformation in elementary schools. Philosophical awareness of the nature of science, values, and technology is the main foundation for sustainable and meaningful education. Keywords: Keywords: philosophy of science, deep learning, coding, elementary school, BOS Performance]]>
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