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

Jiangshui Xiang

doi:10.1155/2021/1407407

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.

Highlights

Mobile Information Systems teaching administrator weighs the student-teachers evaluation scores as the teacher’s evaluation result, it will lead to the irrationality of the evaluation results, which may make the evaluation results unreasonable
Managers make decision-making mistakes. erefore, establishing a scientific evaluation system and its practical use is of great significance to the management of college English teaching [3]. e flipped teaching in classroom is usually described as activities being conventionally carried out by students present outside the class, afterwards they shift back to the classroom session; on the contrary, the conventional classroom approach used is executed outside the class and normally before the class. e data mining method in the education sector consists of statistics, concept visualization, applying classification, creating clustering with associative based analysis, applying anomaly identification, and involves text-based mining [4]
This paper proposes a research on the evaluation of college English flipped classroom teaching model based on data mining algorithms

Summary

Jiangshui Xiang

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. Under the shackles of the original one-way theoretical teaching model, students often passively accept the teacher’s theoretical transmission, lacking deeper thinking and practice, so their own subject quality, independent learning ability, and innovative and creative consciousness are difficult to achieve ideal state. E integration of science provides a solid theoretical support for the production of videos, ensuring that the videos produced can fully cover the key points and difficulties involved in classroom theory teaching and lay a solid material foundation for students’ independent learning To achieve this goal, English teachers need to deeply explore the elements closely related to their teaching content in real life and rationally integrate them, scientifically analyze them, and use them efficiently to realize the integration of professional teaching content with daily life cases. It can effectively attract the attention of the majority of students and enhance their enthusiasm and Before class: learn to play games without purpose

English flipped classroom teaching

Test set

Evaluat ion result ranking