Identification and analysis of learner behavior patterns in English digital teaching environment

This study aims to explore the identification and analysis of learner behavior patterns in English digital teaching environment. Through detailed data collection and preprocessing of the online learning platform, this study uses advanced data analysis techniques, such as K-means clustering algorithm, to classify and identify learners’ behavioral patterns. Research has found that learners’ behavioral patterns are diverse, and these patterns are significantly correlated with learning outcomes. Based on these findings, this article proposes personalized teaching strategies for different behavioral patterns, aiming to optimize teaching methods and improve learning effects. This study also discusses the changing role of teachers in digital teaching environments and proposes directions for future research, including expanding the scope of data, synthesizing qualitative research, and conducting long-term follow-up studies. Although this study has certain limitations, its results are of great significance for understanding and optimizing the digital English teaching environment.