Integrated teaching of college English reading and writing under the background of big data

Abstract

At present, reading and writing are divided into two independent courses in English teaching at most universities. According to the views of many scholars at home and abroad, it is more efficient to learn a language through integration of input and output, which advocates the integrated teaching of English reading and writing. The purpose of this paper is to verify whether the integration of reading and writing is helpful to the improvement of college students' ability through comparative experiment and random sampling method, and put forward an integrated learning mode of online reading and writing based on data algorithm. The main content of this paper is to elaborate the current situation of mobile reading and mobile writing in the era of big data, and then extract two groups of college students with similar learning conditions from two universities for empirical analysis, to study the differences in students' performance through comparison in the traditional reading and writing teaching mode and the new mode of integration of reading and writing teaching, and finally put forward the application of clustering algorithm based on K-Modes under the background of big data English learning scoring system, so that students can learn independently and efficiently on the Internet. The experiment shows that the average score of students in the integration of reading and writing is generally higher than that in traditional teaching. Under the integration of reading and writing, the students' reading accuracy is as high as 90%, which is 10% higher than that in the traditional way, and the score of writing is 5 points higher than that in the traditional way.