Reform and innovation of Civic Education combined with deep and intensive learning

Abstract

In order to respond to the call for reform and innovation of Civic Education, this paper establishes a new teaching mode of Civic Education by combining a deep reinforcement learning model with a Civic Education platform. Firstly, the modules and functions of the platform are designed, in which the modules are divided into the pre-class module, in-class module, and after-class module, and the functions are divided into front and back office management system functions. Then the deep learning based on value function and strategy gradient combines the Civic Education platform with deep reinforcement learning and designs a deep reinforcement learning model for Civic Education, which meets the requirements of Civic Education reform and innovation. Finally, the teaching practice of Civic Education reform and innovation was carried out, and the average attendance rate of students was obtained, which was 92.7% in the first round and 95.3% in the second round. 79 students (72.5%) had an excellent attendance rate in the first round and 85.4% in the second round. At the beginning of the semester, 67.64% of the students’ thinking structure belonged to low-order thinking, only 7.65% of the students’ thinking reached the structure of the association and abstract expansion, at the end of the semester, 60.59% of the students’ thinking reached the structure of the association and abstract expansion. It is concluded that the reform and innovation of Civic Education can improve students’ attendance and improve their thinking ability.

Keywords

• civic education
• deep reinforcement learning
• civic education platform
• value function
• strategy gradient
• thinking structure
• 97b20