<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the development of the network, online education breaks the defects of time and space, and becomes a way that more and more students choose to learn. This paper combines grey correlation analysis, optimizes BP neural network using PSO particle algorithm, and constructs GRA-PSO-BP model. The initial education evaluation indexes are improved by this intelligent algorithm model to construct an online education quality evaluation system, and then the optimized index system is used as a guide to evaluating the online education quality using this model. The results show that: the dispersion of the data of each index is <italic>R</italic><sup>2</sup> = 0.92, which is greater than 0.75, and there is a strong connection between the indexes. The results show that the dispersion of the data of each index is greater than 0.75, and there is a strong connection between the indexes. The 10 colleges and universities scored 3.77, which is a satisfactory grade. The online education quality of these 10 colleges and universities are ranked from high to low as G5, G10, G9, G4, G6, G1, G3, G2, G7 and G8. The intelligent algorithm model and online education quality evaluation index system constructed in this paper can provide help for online education quality evaluation.</p>
</abstract>

With the development of the network, online education breaks the defects of time and space, and becomes a way that more and more students choose to learn. This paper combines grey correlation analysis, optimizes BP neural network using PSO particle algorithm, and constructs GRA-PSO-BP model. The initial education evaluation indexes are improved by this intelligent algorithm model to construct an online education quality evaluation system, and then the optimized index system is used as a guide to evaluating the online education quality using this model. The results show that: the dispersion of the data of each index is R2 = 0.92, which is greater than 0.75, and there is a strong connection between the indexes. The results show that the dispersion of the data of each index is greater than 0.75, and there is a strong connection between the indexes. The 10 colleges and universities scored 3.77, which is a satisfactory grade. The online education quality of these 10 colleges and universities are ranked from high to low as G5, G10, G9, G4, G6, G1, G3, G2, G7 and G8. The intelligent algorithm model and online education quality evaluation index system constructed in this paper can provide help for online education quality evaluation.

The Construction of Online Education Quality Evaluation System Based on Intelligent Algorithm

Sanquan College of Xinxiang Medical University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the development of the network, online education breaks the defects of time and space, and becomes a way that more and more students choose to learn. This...

The Construction of Online Education Quality Evaluation System Based on Intelligent Algorithm

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 24. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0123

Schlüsselwörter
GRA algorithm, PSO particle algorithm, BP neural network, Education quality evaluation

© 2024 Xianfang Xiao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Construction of Online Education Quality Evaluation System Based on Intelligent Algorithm

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 24. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0123

SchlüsselwörterGRA algorithm, PSO particle algorithm, BP neural network, Education quality evaluation

© 2024 Xianfang Xiao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
GRA algorithm, PSO particle algorithm, BP neural network, Education quality evaluation