Design of college English oral training system based on collaborative filtering algorithm

Existing English oral training systems often rely on static rules and lack the ability to dynamically adjust recommended content. They also fail to effectively promote interaction and cooperative learning among students, resulting in a mismatch between practice materials and actual needs and a lack of social interaction. To this end, this paper designs a college English oral training system based on collaborative filtering algorithm. The system collects students’ practice records and grades and other learning data to build a user feature model, and then uses collaborative filtering algorithm to analyze the learning preferences of similar users and dynamically recommend personalized oral practice content. The system also recommends learning partners based on user similarity to enhance interactivity. Experimental results indicate that the recommendation accuracy of the system reaches 92%, and the average oral test score in group cooperation scenes reaches 96 points. This shows that the method proposed in this paper effectively improves the students’ oral training effect and promotes cooperation and communication among learners.