Teaching Quality Evaluation Model of University Teachers Based on Neural Network

DOI

10.2991/978-94-6463-264-4_72How to use a DOI?

Keywords

university teachers; teaching quality; BP neural network; Evaluation index

Abstract

Considering the structural characteristics and the adaptive and self-learning functions of neural networks, a teaching quality evaluation system for high-level teachers was developed using the BP neural algorithm. The system's mathematical model was established, with each evaluation index serving as input and teaching effectiveness as output. The model demonstrated feasibility and applicability in terms of convergence speed and network adaptability. Experimental results indicated that the mathematical model effectively addressed the complexity and interference of human factors in traditional analysis and evaluation of teaching processes. The model exhibited features such as convenience, accuracy, reliability, and speed, with high 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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TY  - CONF
AU  - Bin Tang
AU  - Changbo Wang
AU  - Haibing Cai
AU  - Xiaohu Liu
AU  - Zhenyu Liu
PY  - 2023
DA  - 2023/09/28
TI  - Teaching Quality Evaluation Model of University Teachers Based on Neural Network
BT  - Proceedings of the 2023 3rd International Conference on Education, Information Management and Service Science (EIMSS 2023)
PB  - Atlantis Press
SP  - 625
EP  - 632
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-264-4_72
DO  - 10.2991/978-94-6463-264-4_72
ID  - Tang2023
ER  -