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

<p>In this paper, a web-based teaching platform is constructed based on the background of big data, which is modeled by using a neural network algorithm. First, the neural network structure is output, and the error function is minimized by dynamic iterations using the activation function as neurons. Then, the error values were trained with implicit nodes, the activation function was modeled nonlinearly, and the sample set was extracted to define the cost function. Finally, the optimal particle is trained iteratively using the gradient descent method to derive the optimal solution, thus completing the construction of a web-based teaching platform based on the big data background. The experimental results show that after using this platform for online ideological education teaching, the percentage of students visiting online courses every day is 35%, which is 29% higher than that before using it. Therefore, to improve the construction level of ideological and political education online courses, it is necessary to strengthen the construction of ideological and political education online course platform resources, improve the informatization level of ideological and political education teacher teams, and promote the integration of ideological and political education online courses and classroom teaching.</p>
</abstract>

In this paper, a web-based teaching platform is constructed based on the background of big data, which is modeled by using a neural network algorithm. First, the neural network structure is output, and the error function is minimized by dynamic iterations using the activation function as neurons. Then, the error values were trained with implicit nodes, the activation function was modeled nonlinearly, and the sample set was extracted to define the cost function. Finally, the optimal particle is trained iteratively using the gradient descent method to derive the optimal solution, thus completing the construction of a web-based teaching platform based on the big data background. The experimental results show that after using this platform for online ideological education teaching, the percentage of students visiting online courses every day is 35%, which is 29% higher than that before using it. Therefore, to improve the construction level of ideological and political education online courses, it is necessary to strengthen the construction of ideological and political education online course platform resources, improve the informatization level of ideological and political education teacher teams, and promote the integration of ideological and political education online courses and classroom teaching.

Exploring the implementation path of teaching Civics in online courses in colleges and universities under the background of big data

Applied Mathematics and Nonlinear Sciences

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

In this paper, a web-based teaching platform is constructed based on the background of big data, which is modeled by using a neural network algorithm. First,...

Exploring the implementation path of teaching Civics in online courses in colleges and universities under the background of big data

Publié en ligne: 07 oct. 2023

Pages: -

Reçu: 06 nov. 2022

Accepté: 17 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00530

Mots clés
Network teaching model, Neural network algorithm, Error function, Gradient descent method, Dynamic iteration

© 2023 Liyuan Zhu, published by Sciendo

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

Exploring the implementation path of teaching Civics in online courses in colleges and universities under the background of big data

Publié en ligne: 07 oct. 2023

Pages: -

Reçu: 06 nov. 2022

Accepté: 17 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00530

Mots clésNetwork teaching model, Neural network algorithm, Error function, Gradient descent method, Dynamic iteration

© 2023 Liyuan Zhu, published by Sciendo

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

Mots clés
Network teaching model, Neural network algorithm, Error function, Gradient descent method, Dynamic iteration