Online mode development of Korean art learning in the post-epidemic era based on artificial intelligence and deep learning

被引:0
|
作者
Kaiyi Deng
Guanen Wang
机构
[1] The Catholic University of Korea,Performing Arts and Culture
[2] Sichuan Normal University,Academy of Global Governance and Area Studies
来源
关键词
Korean art learning; Online platform; Personalized learning; Intelligent learning model; Recommendation of art learning resources;
D O I
暂无
中图分类号
学科分类号
摘要
This study aims to explore the establishment of an online platform for Korean art learning in the post-epidemic era and propose a personalized intelligent art learning model based on artificial intelligence (AI) and neural network technology. Firstly, an online platform for Korean art learning and communication is established, which offers a flexible and convenient learning environment for learners. Secondly, a personalized intelligent art learning model based on AI is proposed. It combines hidden representation and its characteristics through learners' personalized characteristics and historical learning records to provide resources suitable for learners. Finally, the recommendation method of art learning resources based on a neural network is adopted. The relationship between resources is inferred using a neural network model, and accurate recommendation is made according to learners' personality characteristics. In addition, this study also explores the innovative application of deep learning (DL) and super-automated AI methods on the online Korean art learning platform. The innovation and novelty of this study lie in the combination of DL and super-automated AI methods to achieve innovation and progress on the online Korean art learning platform. The application of the DL algorithm can comprehensively analyze learners' personality characteristics and learning needs and provide personalized learning resources and suggestions based on this information. At the same time, introducing super-automated AI methods will make the recommendation of learning resources more accurate and efficient, offering learners a better learning experience and results. The experimental results show that the average score of the experimental group is 87.9202 before and 91.1305 after the experiment through the intelligent art learning system, and the learners have made remarkable progress in art learning. Learners' learning styles have been effectively recognized and satisfied, the satisfaction score of the learning environment is high (4.3 ~ 4.9 points), and their problem-solving ability has been significantly improved. It shows that the intelligent art learning system established here is vital and effective for Korean art learning in the post-epidemic era.
引用
收藏
页码:8505 / 8528
页数:23
相关论文
共 50 条
  • [41] Development of an Artificial Intelligence for Small Bowel Capsule Endoscopy Using Deep Learning
    Hosoe, Naoki
    Hayashi, Yukie
    Kamiya, Kenji Limpias
    Sujino, Tomohisa
    Takabayhashi, Kaoru
    Sakurai, Hinako
    Okuzawa, Anna
    Ogata, Haruhiko
    Kanai, Takanori
    [J]. AMERICAN JOURNAL OF GASTROENTEROLOGY, 2021, 116 : S1415 - S1416
  • [42] Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era
    Jing, Yankang
    Bian, Yuemin
    Hu, Ziheng
    Wang, Lirong
    Xie, Xiang-Qun Sean
    [J]. AAPS JOURNAL, 2018, 20 (03):
  • [43] Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era
    Yankang Jing
    Yuemin Bian
    Ziheng Hu
    Lirong Wang
    Xiang-Qun Sean Xie
    [J]. The AAPS Journal, 20
  • [44] Artificial Muscle Intelligence System With Deep Learning for Post-Stroke Assistance and Rehabilitation
    Jacob, Sunil
    Menon, Varun G.
    Al-Turjman, Fadi
    Vinoj, P. G.
    Mostarda, Leonardo
    [J]. IEEE ACCESS, 2019, 7 : 133463 - 133473
  • [45] Portrait of College Students' Online Learning Behavior Based on Artificial Intelligence Technology
    Wang, Hongjian
    Song, Yanbin
    [J]. IEEE ACCESS, 2024, 12 : 6318 - 6328
  • [46] Personalized Online Learning Resource Recommendation Based on Artificial Intelligence and Educational Psychology
    Wei, Xin
    Sun, Shiyun
    Wu, Dan
    Zhou, Liang
    [J]. FRONTIERS IN PSYCHOLOGY, 2021, 12
  • [47] A Design for Experimental Program of Artificial Intelligence and Machine Vision Based on Online Learning
    Gao, Sihan
    Chen, Wanshun
    Ma, Shuxiang
    [J]. E-LEARNING, E-EDUCATION, AND ONLINE TRAINING, ELEOT 2019, 2019, 299 : 177 - 181
  • [48] The relationship between Chinese university students' learning preparation and learning achievement within the EFL blended teaching context in COVID-19 post-epidemic era: The mediating effect of learning methods
    Hua, Meng
    Wang, Lin
    [J]. PLOS ONE, 2023, 18 (01):
  • [49] RETRACTED: Privacy Protection Dilemma and Improved Algorithm Construction Based on Deep Learning in the Era of Artificial Intelligence (Retracted Article)
    Tang, Chenming
    [J]. SECURITY AND COMMUNICATION NETWORKS, 2022, 2022
  • [50] Intelligent e-learning design for art courses based on adaptive learning algorithms and artificial intelligence
    Zheng, Wang
    [J]. ENTERTAINMENT COMPUTING, 2024, 50