Exploration of personalized teaching paths in English education based on big data analysis

Abstract

In the wave of data-driven educational innovation, personalized teaching has become a core issue in the field of English education. This study aims to explore the construction and implementation of personalized teaching paths in English education through big data analysis techniques. With the advancement of technology, traditional English education methods are gradually unable to meet the diverse and dynamic learning needs of students. Although personalized teaching has received widespread attention, existing research still faces challenges in integrating complex factors such as student preferences, learning habits, and contextual information to achieve teaching optimization. This study is divided into two main parts. Firstly, it applies probabilistic association analysis to conduct detailed research on student learning preferences, thus revealing individual learner differences. Secondly, it develops a new algorithm model that can integrate students’ personalized preferences and real-time contextual information to recommend the most suitable personalized learning interests. This method not only helps to improve students' learning motivation and efficiency but also provides a new theoretical basis and practical scheme for the design of personalized teaching paths in English education.