Influence of Instructional Design on Students' Innovative Thinking: A Multi-Algorithm Approach

The cultivation of innovative thinking among college students is crucial for developing high-caliber national strategic talents, making innovation courses a vital component of university education. However, traditional instructional designs often lack the personalized approach needed to effectively foster innovation. To address these shortcomings, a novel network instructional system is proposed, integrating support vector machines and K-means clustering algorithms to personalize learning experiences. This system, implemented in an undergraduate course, features a virtual reality classroom, real-time chat, and a comprehensive evaluation system. It facilitates personalized learning paths, open data sharing, real-time discussions, and diverse virtual activities. The system’s reliability is demonstrated using standard datasets. Its effectiveness in meeting diverse student needs and overcoming the limitations of traditional methods has been evaluated since 2022 using student recognition analysis, final exam pass rates, competition success rates, and classroom engagement assessments. The results designate that this advanced network instructional system is more operative than outdated designs in stimulating learning interests and enhancing innovative thinking. The adoption of this approach promises to advance educational research, improve college students’ creative capabilities, and donate to the development of exceptional innovative talent, thereby promoting sustainable societal progress.