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

<p>To construct an automatic question-and-answer system for Civics, a neural network and a self-attention mechanism are combined in this paper. Through the input layer of the neural network, the model remembers the semantic information of the Civics text that has been processed. Dynamic feature extraction is used to categorize and classify Civics questions in the system and determine the type of questions. The GLU network is used to predict the answers to the Civics and Politics questions, and the predicted results are normalized by combining the Softmax function, and the category with the highest probability is selected as the answer. The main mode of teaching afterward was to combine the automatic question-and-answer system with teaching. The results show that in the question and answer system, the highest accuracy rate is the practical cognition type of questions, with the accuracy rate fluctuating in the range of (0.8-0.9), and the accuracy rate of truth type and method type of questions fluctuates up and down in the range of 0.7. The percentage of students in the high marking stage increased by 0.1. In Civic Behavior, students’ patriotism increased by 0.15, and spirituality increased by 0.3.</p>
</abstract>

To construct an automatic question-and-answer system for Civics, a neural network and a self-attention mechanism are combined in this paper. Through the input layer of the neural network, the model remembers the semantic information of the Civics text that has been processed. Dynamic feature extraction is used to categorize and classify Civics questions in the system and determine the type of questions. The GLU network is used to predict the answers to the Civics and Politics questions, and the predicted results are normalized by combining the Softmax function, and the category with the highest probability is selected as the answer. The main mode of teaching afterward was to combine the automatic question-and-answer system with teaching. The results show that in the question and answer system, the highest accuracy rate is the practical cognition type of questions, with the accuracy rate fluctuating in the range of (0.8-0.9), and the accuracy rate of truth type and method type of questions fluctuates up and down in the range of 0.7. The percentage of students in the high marking stage increased by 0.1. In Civic Behavior, students’ patriotism increased by 0.15, and spirituality increased by 0.3.

Research on the Innovative Path of Ideological and Political Education in Colleges and Universities in the Context of Deep Learning

Applied Mathematics and Nonlinear Sciences

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

To construct an automatic question-and-answer system for Civics, a neural network and a self-attention mechanism are combined in this paper. Through the input...

Research on the Innovative Path of Ideological and Political Education in Colleges and Universities in the Context of Deep Learning

Published Online: Nov 25, 2023

Page range: -

Received: Dec 25, 2022

Accepted: Jun 05, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01235

Keywords
Neural network, Self-attention mechanism, GLU network, Automatic question and answer system, Civic education

© 2023 Muyuan Cao, published by Sciendo

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

Research on the Innovative Path of Ideological and Political Education in Colleges and Universities in the Context of Deep Learning

Published Online: Nov 25, 2023

Page range: -

Received: Dec 25, 2022

Accepted: Jun 05, 2023

DOI: https://doi.org/10.2478/amns.2023.2.01235

KeywordsNeural network, Self-attention mechanism, GLU network, Automatic question and answer system, Civic education

© 2023 Muyuan Cao, published by Sciendo

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

Keywords
Neural network, Self-attention mechanism, GLU network, Automatic question and answer system, Civic education