Comparison of the Application of Machine Learning Technology in Online Teaching

This study delves into the application of machine learning (ML) techniques, with a focus on reinforcement learning (RL), to enhance personalized and adaptive learning in online education. It contrasts three key methods: RL-driven adaptive learning algorithms, sentiment analysis, and predictive analytics. Each method aims to improve critical educational outcomes such as student engagement, academic performance, and the timing of intervention strategies. By analyzing real- world case studies across diverse online learning environments, the research evaluates the relative effectiveness of these ML techniques. Results show that RL-based adaptive learning systems significantly enhance both student performance and engagement, demonstrating their ability to create more dynamic and individualized educational experiences. In comparison, while sentiment analysis and predictive analytics contribute to timely interventions and behavioral insights, RL stands out for its capacity to tailor learning paths in real time. The study concludes by proposing a comprehensive framework for integrating ML, particularly RL, into online education. This framework offers practical insights for educators, administrators, and policymakers, emphasizing the potential of RL to revolutionize personalized learning in digital classrooms.