Research on the Optimization of an Intelligent Teaching Model for Linux Courses Based on Constructivism and Game Theory

Currently, there is a mismatch between traditional Linux teaching and dynamic learning needs. Therefore, this study proposes a dual drive intelligent teaching model based on constructivism and game theory, and constructs a collaborative optimization framework of “task chain‐resource game‐container scheduling”. Firstly, design a cognitive transition task chain represented by weighted directed graphs, dynamically adjust node weights through reinforcement learning, and reduce the standard deviation of high‐order task completion time; Secondly, establish a tripartite game engine and use multiagent Q‐learning to solve Nash equilibrium; Finally, develop a lightweight Docker cluster and voice interaction system to reduce container response latency through a dual objective optimization algorithm. Empirical research has shown that the experimental group achieved a score range of 90+in the Linux certification exam, accounting for 36%. The code quality Abstract Syntax Tree (AST) similarity increased by 19.2%, and the system maintained a task completion rate of 92.7% in high concurrency scenarios. This model provides a replicable and scalable paradigm for current intelligent teaching.