Evaluation of the College English Flipped Classroom Teaching Model Based on Data Mining Algorithms

Abstract

Aiming at the problem of the inability to classify data due to the excessive amount of teaching resources, which leads to the college English flipped classroom teaching model’s low resource sharing rate and the poor accuracy of score statistical analysis, a university-based data mining algorithm is designed. Research on the evaluation of english flipped classroom teaching model is conducted, the strategy of applying the flipped classroom in college English teaching is analyzed, the characteristics and advantages of this model are explored, the data mining algorithm to practical teaching is applied, and the decision tree C4.5 classification technology is used to achieve accurate classification of massive student test scores. The classification technology selects classification attributes based on the information gain rate. It uses the postpruning method to process data to improve the accuracy of data classification. Finally, the statistical analysis results of the business logic layer are transmitted to the user through the browser application layer using the WEB server. The experimental results show that using this article’s evaluation method, the college English flipped classroom teaching model can achieve a high resource sharing rate, high accuracy of performance statistics analysis, and a good teaching effect.