Proceedings of the 2016 International Conference on Education, Management Science and Economics

Research on the Evaluation of Teaching Quality Based on CGSAB

Authors
Jiatang Cheng, Yan Xiong
Corresponding Author
Jiatang Cheng
Available Online December 2016.
DOI
https://doi.org/10.2991/icemse-16.2016.80How to use a DOI?
Keywords
Teaching quality, evaluation, gravitational search algorithm, chaotic series, BP neural network
Abstract
Teaching quality evaluation is an important work in teaching management. In order to improve the accuracy of teaching quality evaluation, according to the evaluation data at a certain university, an evaluation model based on BP neural network optimized by gravitational search algorithm (GSABP) is proposed. For the GSA algorithm is easy to fall into the local optimal, the ergodicity of chaotic sequence is used to generate the initial population of GSA, and then the chaotic gravitational search algorithm (CGSA) is presented. The experimental results show that, compared with BP neural network and GSABP algorithm, the model using CGSABP has high credibility and strong generalization ability, which provides a feasible method for the accurate evaluation of teaching quality.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Education, Management Science and Economics
Part of series
Advances in Social Science, Education and Humanities Research
Publication Date
December 2016
ISBN
978-94-6252-275-6
ISSN
2352-5398
DOI
https://doi.org/10.2991/icemse-16.2016.80How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jiatang Cheng
AU  - Yan Xiong
PY  - 2016/12
DA  - 2016/12
TI  - Research on the Evaluation of Teaching Quality Based on CGSAB
BT  - 2016 International Conference on Education, Management Science and Economics
PB  - Atlantis Press
SP  - 318
EP  - 320
SN  - 2352-5398
UR  - https://doi.org/10.2991/icemse-16.2016.80
DO  - https://doi.org/10.2991/icemse-16.2016.80
ID  - Cheng2016/12
ER  -