English MOOC Data Analysis Model Based on Modified Particle Swarm Optimization (PSO) Algorithm

With the development of big data and artificial intelligence technology, online education has become one of the hot topics in the current education reform. This study aimed to conduct in-depth analysis of English MOOC systems by changing Particle Swarm Optimization (PSO) algorithms. PSO algorithm is more suitable for processing large-scale and complex English MOOC system data. The improved PSO algorithm was applied to the data analysis of the English MOOC system, including learner behavior, learning outcomes, course design, and other aspects. Through comparative analysis, it was found that the improved PSO algorithm has higher computational efficiency and accuracy when processing English MOOC system data. Through the experiment, it was found that the number of students satisfied with the system was 497, 300 teachers were 279, and 200 English major teachers were 182. These findings have important reference value for optimizing the design and operation of English MOOC systems and improving their teaching effectiveness.