Multimodal deep learning for art behavior analysis and personalized teaching path generation
Abstract In the current era of continuous innovation in educational philosophy, the importance of art education for students ‘creativity, aesthetic ability, and all-round development has become increasingly prominent, with personalized education becoming a key trend. This study focuses on art educat...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
|
| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00480-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849226077000433664 |
|---|---|
| author | Yikun Li Jie Shi |
| author_facet | Yikun Li Jie Shi |
| author_sort | Yikun Li |
| collection | DOAJ |
| description | Abstract In the current era of continuous innovation in educational philosophy, the importance of art education for students ‘creativity, aesthetic ability, and all-round development has become increasingly prominent, with personalized education becoming a key trend. This study focuses on art education, aiming to address the lack of personalization in traditional teaching methods by proposing innovative solutions using multimodal deep learning technology. The research constructs a Multimodal Feature Fusion Network (MFFN) and an Evolutionary Path Generation Algorithm (EPG), utilizing Kinect sensors, digital tablets, and other devices to collect multi-modal data including visual, tactile, behavioral, and environmental information, establishing a large-scale art learning dataset. Experimental results show that the MFFN model performs excellently in technique recognition, emotion judgment, and attention detection tasks, achieving an accuracy rate of 93.7% in technique recognition. Personalized teaching paths generated based on EPG can dynamically adjust according to learners’ real-time capability profiles and learning progress, improving learning efficiency by 42.6%. Additionally, the study constructs a system architecture covering data layers, algorithm layers, and application layers, enabling real-time dynamic adjustment and visual traceability of teaching paths. This research establishes a multimodal analysis paradigm for art behavior, promoting the digitalization and personalization of art education, with significant application value in areas such as art training institutions, special education, and the digital transformation of art education. |
| format | Article |
| id | doaj-art-8d5e8aaf0eca4e9a9cd5aca66bdf6bb3 |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-8d5e8aaf0eca4e9a9cd5aca66bdf6bb32025-08-24T11:39:57ZengSpringerDiscover Artificial Intelligence2731-08092025-08-015111810.1007/s44163-025-00480-wMultimodal deep learning for art behavior analysis and personalized teaching path generationYikun Li0Jie Shi1School of Arts, Weinan Normal UniversitySchool of Arts, Weinan Normal UniversityAbstract In the current era of continuous innovation in educational philosophy, the importance of art education for students ‘creativity, aesthetic ability, and all-round development has become increasingly prominent, with personalized education becoming a key trend. This study focuses on art education, aiming to address the lack of personalization in traditional teaching methods by proposing innovative solutions using multimodal deep learning technology. The research constructs a Multimodal Feature Fusion Network (MFFN) and an Evolutionary Path Generation Algorithm (EPG), utilizing Kinect sensors, digital tablets, and other devices to collect multi-modal data including visual, tactile, behavioral, and environmental information, establishing a large-scale art learning dataset. Experimental results show that the MFFN model performs excellently in technique recognition, emotion judgment, and attention detection tasks, achieving an accuracy rate of 93.7% in technique recognition. Personalized teaching paths generated based on EPG can dynamically adjust according to learners’ real-time capability profiles and learning progress, improving learning efficiency by 42.6%. Additionally, the study constructs a system architecture covering data layers, algorithm layers, and application layers, enabling real-time dynamic adjustment and visual traceability of teaching paths. This research establishes a multimodal analysis paradigm for art behavior, promoting the digitalization and personalization of art education, with significant application value in areas such as art training institutions, special education, and the digital transformation of art education.https://doi.org/10.1007/s44163-025-00480-wMultimodal deep learningArt learning behavior analysisPersonalized teaching pathEvolutionary algorithmMultimodal data fusion |
| spellingShingle | Yikun Li Jie Shi Multimodal deep learning for art behavior analysis and personalized teaching path generation Discover Artificial Intelligence Multimodal deep learning Art learning behavior analysis Personalized teaching path Evolutionary algorithm Multimodal data fusion |
| title | Multimodal deep learning for art behavior analysis and personalized teaching path generation |
| title_full | Multimodal deep learning for art behavior analysis and personalized teaching path generation |
| title_fullStr | Multimodal deep learning for art behavior analysis and personalized teaching path generation |
| title_full_unstemmed | Multimodal deep learning for art behavior analysis and personalized teaching path generation |
| title_short | Multimodal deep learning for art behavior analysis and personalized teaching path generation |
| title_sort | multimodal deep learning for art behavior analysis and personalized teaching path generation |
| topic | Multimodal deep learning Art learning behavior analysis Personalized teaching path Evolutionary algorithm Multimodal data fusion |
| url | https://doi.org/10.1007/s44163-025-00480-w |
| work_keys_str_mv | AT yikunli multimodaldeeplearningforartbehavioranalysisandpersonalizedteachingpathgeneration AT jieshi multimodaldeeplearningforartbehavioranalysisandpersonalizedteachingpathgeneration |