Chen Jianyong

Changzhou Vocational Institute of Textile and Garment

Abstract：

With the widespread application of artificial intelligence technology in education, constructing intelligent online learning intervention systems has become a crucial approach to enhancing educational quality. This study designs and constructs an Artificial Intelligence-based Intelligent Online Learning Intervention Model (AIIM) integrating machine learning algorithms, grounded in learning analytics and self-regulated learning theory. The model aims to enhance students' learning outcomes and self-regulation abilities through real-time learning behavior monitoring, risk early warning, and personalized intervention strategies. Employing a quasi-experimental design, the study selected 120 students from an English course at a vocational college as research subjects over a 16-week experimental period. Results indicate that experimental group students significantly outperformed the control group in login frequency (42.6% increase), learning duration (33.3% increase), and assignment completion rate (38.4% increase). Final exam scores improved by an average of 8.3 points, with significant enhancements in learning satisfaction and self-regulation abilities (p<0.01). This study validates the effectiveness of AI technology in online learning interventions, providing theoretical foundations and empirical support for the development and application of intelligent education systems.

Key Words：

Artificial Intelligence; online learning; intervention model; learning analytics