2026 ASEE Annual Conference & Exposition

This study examines the integration of Python programming as an instructional tool to foster computational thinking (CT) across K–12 and higher education. As engineering and computing disciplines increasingly prioritize problem-solving, abstraction, and algorithmic reasoning, educators face the challenge of designing experiences that foster these skills without overwhelming novice learners. Python’s simplicity, readability, and versatility make it ideal for cultivating CT and bridging conceptual understanding with practical implementation.

Drawing evidence from international studies and classroom interventions, this study examines how Python-based instruction directly supports the four core principles of CT: decomposition, abstraction, pattern recognition, and algorithmic thinking. Furthermore, it explores how Python instruction promotes creativity, collaboration, and reflective problem-solving. Across a broad spectrum of educational contexts from K-12 courses to higher education, active learning models paired with Python consistently yield substantial gains in both conceptual and procedural CT competencies.

The proposed framework emphasizes using Python as a pedagogical medium, rather than solely a programming language. It highlights the importance of scaffolding, formative feedback, and collaborative debugging to build student confidence and persistence. Research indicates that structured Python integration can enhance learners’ self-efficacy and the transfer of computational methods to mathematical and scientific reasoning tasks. However, challenges remain in aligning assessment practices with CT outcomes and preparing educators with sufficient technological and pedagogical expertise. Thus, ideal strategies are explored for educators seeking to embed CT through Python in existing curricula. These include designing modular instructional units centered on real-world problem contexts, employing collaborative learning tools, and developing learning outcomes that capture both algorithmic accuracy and creative problem-solving processes.

By positioning Python as an inclusive and cross-disciplinary platform for computational thinking, this research contributes actionable strategies to the ASEE community. It serves to broaden participation in computing education and equip future engineers and educators with the cognitive tools essential for innovation in the digital age.