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Research and design of auxiliary teaching system for college students’ Civics and Political Science courses in the context of deep learning

Yongchao Yin

Yongchao Yin

Business Department, Huanghe Science and Technology UniversityZhengzhou, China
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Yin, Yongchao
  
Mar 17, 2025
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Published Online: Mar 17, 2025
Received: Oct 10, 2024
Accepted: Jan 31, 2025
DOI: https://doi.org/10.2478/amns-2025-0236
Keywords
© 2025 Yongchao Yin, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Architecture of ideological and political assisted teaching system
Architecture of ideological and political assisted teaching system

Figure 2.

Overall module design of ideological and political auxiliary teaching system
Overall module design of ideological and political auxiliary teaching system

Figure 3.

Network diagram of ideological and political exercises under the same knowledge point
Network diagram of ideological and political exercises under the same knowledge point

Figure 4.

Exercise network under knowledge point with sequence relation
Exercise network under knowledge point with sequence relation

Figure 5.

Learner subgraph
Learner subgraph

Figure 6.

Flowchart of a recommendation model based on reinforcement learning
Flowchart of a recommendation model based on reinforcement learning

Figure 7.

Structure of reinforcement learning recommendation model
Structure of reinforcement learning recommendation model

Figure 8.

Distribution of learners’ ability level
Distribution of learners’ ability level

Figure 9.

Learner optimal recommendation model
Learner optimal recommendation model

Figure 10.

Comparison of distribution of knowledge point correlation degree
Comparison of distribution of knowledge point correlation degree

Figure 11.

Comprehensive difficulty change in recommendation sequence
Comprehensive difficulty change in recommendation sequence

Figure 12.

Changes of the average reward value of different learning factors
Changes of the average reward value of different learning factors
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