Design and Implementation of an English Mobile Learning System Based on Weighted Naive Bayes

Abstract

This study employs the Weighted Naive Bayes algorithm in order to enhance the efficiency of personalized English learning in a mobile learning environment. By introducing a weighting factor, the traditional Naive Bayes classifier is optimized for an English mobile learning system design. Meanwhile, the application of the Naive Bayes algorithm and weighting techniques in mobile learning is analyzed in details, including algorithm selection, optimization, weight distribution, and model training. The results indicate that the English mobile learning system, optimized with the Weighted Naive Bayes algorithm, significantly improves learning outcomes, accuracy of personalized recommendations, and security of the learning process. Thus, it can effectively support English teaching and learning in a mobile learning environment.