Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)

English Teaching Evaluation Model Based on Analytic Hierarchy Process

Authors
Yiran Chen1, *, Qianchun Ma2, Jiaxin Li3
1German Institute of Engineering, Chongqing College of Mobile Communication, Chongqing, China
2Digital Finance and Business School, Chongqing Vocational College of Media, Chongqing, China
3The Forge Business School, Chongqing College of Mobile Communication, Chongqing, China
*Corresponding author. Email: 787783488@qq.com
Corresponding Author
Yiran Chen
Available Online 22 September 2023.
DOI
10.2991/978-94-6463-242-2_16How to use a DOI?
Keywords
English Teaching Evaluation; Analytic Hierarchies Process; Evaluation indicators; Accuracy Performance
Abstract

English teaching is a worldwide and essential education component for human society and talent innovation system. Therefore, the correct evaluation for English teaching methods has become a necessary condition for education system and can assist the supervisors to adjust the education methods to reach more excellent impacts. Currently, existing evaluation methods including machine learning methods, neural network and deep learning to quantify the performance of English teaching. However, these learning methods are limited to analyze complex situations with different teachers and students, which is also a black-box for analyzing the detail reasons that causes the evaluation results. In this paper, we utilize the analytic hierarchy process to evaluate the English teachings and assist the students to improve grades while learning English. Initially, we select the indicators that can affect the English teaching and utilize the collected data to measure the hierarchies of these indicators to obtain an analytic hierarchies process model. Subsequently, we simulate the established model with testing data to investigate the most important indicators with detail accuracy. From our extensive experimental results and comparison performances, we can conclude that our proposed model can investigate the most influence parameters with acceptable identification accuracy.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
22 September 2023
ISBN
978-94-6463-242-2
ISSN
2589-4900
DOI
10.2991/978-94-6463-242-2_16How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Yiran Chen
AU  - Qianchun Ma
AU  - Jiaxin Li
PY  - 2023
DA  - 2023/09/22
TI  - English Teaching Evaluation Model Based on Analytic Hierarchy Process
BT  - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
PB  - Atlantis Press
SP  - 125
EP  - 131
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-242-2_16
DO  - 10.2991/978-94-6463-242-2_16
ID  - Chen2023
ER  -