Analysis of Higher Vocational English Learners’ Behavioral Characteristics and Teaching Content Optimization Strategies Based on Big Data Mining

Abstract

In the context of the big data era, the study of the massive data accumulated in the information construction of higher vocational colleges and universities can provide convenience for the teaching and management of colleges and universities. By improving the K-means clustering algorithm and Apriori algorithm in big data mining technology, the article mines the English learning behaviors and laws of higher vocational English learners and explores the correlation between learners’ behavioral characteristics and teaching performance. Finally, through empirical testing, this paper proposes an optimization strategy for teaching content in higher vocational English education. In the comparative analysis of reading and writing pre-test and post-test scores between the experimental class and the control class, the pre-test score of reading comprehension of the students in the experimental class is 31.25, and the post-test score is 32.84, and the average score of reading comprehension has increased by 1.59, which can be obtained that the English reading comprehension of the students has been improved after teaching with the teaching content optimization strategy proposed in this paper.