Exploration of Information Practice of Constructed English Learning Platform Based on K-means Algorithm and Improved Apriori Algorithm

Abstract

This paper explores the application of the constructed English learning platform based on k-means algorithm and improved Apriori algorithm in information teaching. Through the loss function optimization, the learning rate adjustment, the precise selection of the initial center point, and the improvement of the Apriori algorithm, this study realizes the accurate analysis of the learner characteristics and the personalized recommendation of the learning path. The experimental results show that the application of these techniques significantly improves the accuracy and efficiency of learners' clustering, the accuracy and user satisfaction of dynamic learning path recommendation, and the timeliness and personalization of interactive learning feedback. In addition, generic metrics such as improved user retention and overall satisfaction further demonstrate the success of the platform in enhancing user engagement and satisfaction. This study provides an effective technical path for the construction of personalized learning environment, and provides an important theoretical and practical basis for further optimizing the function of learning platform, improving user learning experience and promoting personalized learning practice.