Yingliang Wan¹, Hong Tao^2*

1.School of Data Science, Xianda College of Economics and Humanities, Shanghai International Studies University; 2.School of Business, Xianda College of Economics and Humanities, Shanghai International Studies University

Abstract：

Against the dual background of the booming digital economy and the deepening construction of "New Liberal Arts," the major of Big Data Management and Application bears the responsibility of cultivating composite talents equipped with both data thinking and technology implementation capabilities. With the breakthrough of Generative Artificial Intelligence (GenAI) technologies, the high-order ability to solve complex data engineering problems using modern technical means has become key to talent cultivation. However, current Python programming instruction in non-engineering contexts often falls into the trap of emphasizing "syntax over logic," leading to a cognitive gap where students struggle to bridge business requirements with technical implementation. Based on the Outcome-Based Education (OBE) philosophy, combined with Constructivism and Scaffolding Theory, this study constructs a blended Python teaching model oriented toward complex engineering problem-solving abilities. This model integrates computational thinking with management science decision-making logic through a three-stage progressive process of "pre-class self-study, in-class project inquiry, and post-class system practice." Adopting an Action Research approach, this study conducted a one-semester empirical teaching experiment with two classes from the Big Data Management and Application major at a university. Through triangulation analysis of student project code, in-depth interview records, and classroom observation logs, the study found that this model significantly improved students' capabilities in modeling ill-structured problems and optimizing systems, effectively resolving the pain point where management students "understand the theory but cannot implement it."

Key Words：

complex engineering problems; blended teaching; Python programming; big data management; OBE; generative AI