Abstract
This paper presents a Python-integrated framework for teaching Discrete Structures, focusing on bridging the gap between theoretical concepts and practical applications. The primary goals of this framework are to enhance student engagement, improve conceptual clarity, develop computational thinking, and provide real-world relevance to abstract topics. By leveraging Python’s intuitive syntax and versatile libraries, such as itertools for combinatorics and networkx for graph theory, the framework transforms traditional teaching methods into a hands-on, interactive learning experience. The framework aims to help students connect core topics like propositional logic, set theory, and graph traversal with their computational implementations. For instance, students can write Python programs to generate truth tables, explore set operations, or visualize graph algorithms. These activities foster active learning by providing immediate feedback and enabling experimentation, which promotes critical thinking and problem-solving skills. Another key goal is to demonstrate the real-world relevance of Discrete Structures. Students learn to apply modular arithmetic in cryptography, graph algorithms in network analysis, and recursion in algorithmic problem-solving. This interdisciplinary approach prepares students for modern technological challenges while aligning theoretical knowledge with practical skills. Initial evaluations show significant improvements in student engagement, comprehension, and performance. By integrating Python into the teaching of Discrete Structures, this framework not only bridges the gap between theory and practice but also equips students with the tools and mindset needed to excel in computational fields.
Published Version
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