<![CDATA[The increasing demand for computational thinking (CT) in mathematics education requires instructional designs that meaningfully connect abstract mathematical concepts with algorithmic reasoning. Grounded in the theory of concept image, this study investigates how Python-integrated learning activities can foster students’ mathematical understanding while simultaneously supporting the development of computational thinking. Using a Design-Based Research (DBR) methodology, this study was conducted across three iterative cycles involving undergraduate mathematics students and how Python-integrated learning activities can enrich students' mathematical understanding while simultaneously developing CT skills. The study was conducted across three iterative cycles in a Calculus II, Integration course involving twenty-six mathematics education students PGRI University of Yogyakarta. Data were collected through concept image mapping, CT performance assessments, classroom observations, and the analysis of Python code artifacts. The finding indicate that Python-assisted dynamic visualization facilitated a transition from static, procedural understanding toward deep, relational mental representations. Programming activities were proven to strengthen abstraction and algorithmic reasoning capabilities, where code serves as an externalization of students' concept images. This study yields three key instructional design principles: Concept-First Coding, Representational Fluidity, and Reflective Alignment. In conclusion, Python integration designed as a "cognitive bridge" effectively transforms mathematical intuition into formal-computational understanding that is transferable to complex problem-solving contexts. By leveraging programming as a representational medium, educators can create rich, interactive learning ecosystems where students actively construct knowledge, refine mental models, and develop transferable cognitive competencies. Future directions may include expanding these practices across disciplines, refining assessment models for CT in text-based programming, and investigating long-term retention and applicability of learned skills beyond academic settings.]]>
