Recommendations for Big Data-Driven English Learning Behavior Analysis and Personalized Teaching Strategy

In this paper, with the help of big data analytics, students’ learning behavior patterns are deeply mined, so as to provide personalized learning support for students. The massive data generated by students in the learning process is first mined. Then the K-means algorithm is used to cluster the students’ behaviors. Finally, personalized push of learning resources for different types of learners based on collaborative filtering algorithm and customized learning path based on genetic algorithm. Research design teaching practice to verify the application effect of the method in this paper. Taking 150 students in a class of school A as an example, the collected behavioral data of 148 students are clustered and analyzed, which can be divided into 4 types of learners, and the method of this paper can recommend resources that meet the knowledge point needs and learning preferences of different groups of students, and recommend appropriate learning paths for 4 types of learners based on genetic algorithms. After practicing teaching, the average English score of the experimental class is 7.49 higher than that of the traditional teaching class (control class), and there is a significant difference (P=0.002). It shows that personalized teaching based on students’ learning behavior analysis can effectively improve the quality of English teaching.