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Research on Adaptive Teaching Strategy of Smart Aesthetic Education Teaching Platform Based on Reinforcement Learning

Yifan Zhang

Yifan Zhang

Art Education Center, Zhengzhou University of Light IndustryZhengzhou, China
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Zhang, Yifan
  
23. Sept. 2025
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Online veröffentlicht: 23. Sept. 2025
Eingereicht: 07. Jan. 2025
Akzeptiert: 19. Apr. 2025
DOI: https://doi.org/10.2478/amns-2025-0992
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© 2025 Songyu Wu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Sorting out the process framework
Sorting out the process framework

Figure 2.

Steps of knowledge graph storage
Steps of knowledge graph storage

Figure 3.

DDQN training process
DDQN training process

Figure 4.

State composition
State composition

Figure 5.

State transition diagram
State transition diagram

Figure 6.

Precision comparison on two data sets
Precision comparison on two data sets

Figure 7.

Comparison of Recall on two datasets
Comparison of Recall on two datasets

Figure 8.

Comparison of knowledge points of middle and low level students
Comparison of knowledge points of middle and low level students

Figure 9.

Comparison of knowledge points of high-level students
Comparison of knowledge points of high-level students

Comparison experimental results of DDQN on the MOOCCube dataset

Index Model HR@10 HR@5 HR@3 HR@1 NDCG@10 MRR
FM 0.0536 0.0303 0.0189 0.0082 0.0241 0.0378
KGCN 0.4351 0.3378 0.2767 0.1197 0.1822 0.1920
GRU4Rec 0.7862 0.7439 0.6903 0.5209 0.6605 0.5832
KERL 0.9285 0.8802 0.8577 0.7721 0.8440 0.8299
DDQN4Rec 0.9309 0.8921 0.8602 0.7805 0.8512 0.8351
KERL4Rec 0.9412 0.9088 0.8789 0.7932 0.8793 0.8573

User satisfaction test results

Group Practicability Applicability Accuracy Total points
Control group 2.85±0.26 2.33±0.27 2.74±0.28 7.80±0.46
Experimental group 3.55±0.33 4.08±0.42 4.16±0.18 13.75±0.59
Z 1.300 4.886 4.267 0.783
P 0.022 <0.001 <0.001 0.043

Comparison experimental results of DDQN on the MOOPer dataset

Index Model HR@10 HR@5 HR@3 HR@1 NDCG@10 MRR
FM 0.4829 0.3822 0.3219 0.2516 0.3728 0.3264
KGCN 0.7823 0.6718 0.5567 0.3629 0.2662 0.3105
GRU4Rec 0.7612 0.7629 0.7873 0.7053 0.7451 0.7322
KERL 0.9638 0.9511 0.9340 0.9073 0.9371 0.9205
DDQN4Rec 0.9711 0.9605 0.9411 0.9105 0.9417 0.9215
KERL4Rec 0.9892 0.9784 0.9685 0.9235 0.9688 0.9398
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