Analysis of Student Behavior in English Online Education Under the Perspective of Big Data

This study examines student behavior in English online education using big data analytics. By collecting and analyzing learning behavior data from an online platform, the research employs a genetic K-means algorithm for classification. The analysis reveals distinct collective traits and individual differences among students, successfully segmenting them into three profiles: apathetic learners, average learners, and proactive learners. Each profile exhibits unique learning behaviors, efficiencies, and outcomes. This segmentation provides valuable insights for optimizing English online education, supporting personalized teaching strategies. The research underscores the importance of enhancing self-directed learning abilities and overall effectiveness. It offers methodological guidance for improving online education based on scientific data analysis, contributing to both theoretical understanding and practical application in the field.