English Teaching Evaluation Model Based on Association Rule Algorithm and Machine Learning

Author:

Yin Liwen1ORCID,Xu Zhe2

Affiliation:

1. School of Foreign Studies, Hebei North University, Zhangjiakou, Hebei 075000, China

2. School of Information Science and Engineering, Hebei North University, Zhangjiakou, Hebei 075000, China

Abstract

At present, Chinese colleges and universities have a clear understanding of the importance of teaching quality evaluation. They regard the evaluation of teaching quality as an important part of teaching management. This paper aims to study how to analyze and study English teaching evaluation based on association rule algorithm and machine learning and study the model. This paper raises the question of English teaching evaluation. This question is based on modelling studies. So it expounds the concepts and related algorithms of association rule algorithm and machine learning. This paper designs and analyzes a case study of the English teaching evaluation model. The experimental results show that taking a university as the empirical object for specific analysis, according to the evaluation system established in the research, the final score of the questionnaire is 89.2 points, and the English teaching evaluation result is a good grade.

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

Reference20 articles.

1. Teaching evaluation practices in engineering programs: current approaches and usefulness;K. A. Villanueva;International Journal of Engineering Education,2017

2. Research on the reform of ideological and political teaching evaluation method of college English course based on “online and offline” teaching;X. Wu;Journal of Higher Education Research,2022

3. Research on Classroom Teaching Evaluation and Instruction System Based on GIS Mobile Terminal

4. The Correlation between Teaching Evaluation and Lecturers’ Performances

5. Smart teaching evaluation model using weighted naive bayes algorithm;L. Liu;Journal of Intelligent and Fuzzy Systems,2020

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