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Computational thinking (CT) stands as a universal problem-solving approach applicable across diverse disciplines, transcending the domain of computer science. It embodies the mental process of structuring a problem to enable a computational solution feasible for both humans and machines. This methodology involves dissecting problems into smaller parts that are easier to understand and solve. This study delineates a meticulously designed series of CT activities within an introductory computer science course and explores their profound impact on student engagement and problem-solving proficiency. Our findings underscore the pivotal role of hands-on CT practice in augmenting students' ability to decompose problems, recognize patterns, and abstract complexities, and employ algorithms effectively. Notably, this infusion of CT not only cultivates theoretical understanding but also bridges the gap between conceptual knowledge and real-world application through the use of computational tools like Python programming. As CT continues to emerge as a cornerstone skill in diverse domains, this research presents compelling evidence advocating for its integration into introductory courses, laying a robust foundation for students to navigate the evolving technological landscape with enhanced problem-solving capabilities