• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

通过卷积神经网络和混合式学习提升舞蹈教育。

Enhancing dance education through convolutional neural networks and blended learning.

作者信息

Zhang Zhiping, Wang Wei

机构信息

College of Education, HanJiang Normal University, Shiyan, Hubei, China.

Dancing College, Sichuan Normal University, Chengdu, SiChuan, China.

出版信息

PeerJ Comput Sci. 2024 Oct 25;10:e2342. doi: 10.7717/peerj-cs.2342. eCollection 2024.

DOI:10.7717/peerj-cs.2342
PMID:39650395
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11622838/
Abstract

This article explores the evolving landscape of dance teaching, acknowledging the transformative impact of the internet and technology. With the emergence of online platforms, dance education is no longer confined to physical classrooms but can extend to virtual spaces, facilitating a more flexible and accessible learning experience. Blended learning, integrating traditional offline methods and online resources, offers a versatile approach that transcends geographical and temporal constraints. The article highlights the utilization of the dual-wing harmonium (DWH) multi-view metric learning (MVML) algorithm for facial emotion recognition, enhancing the assessment of students' emotional expression in dance performances. Moreover, the integration of motion capture technology with convolutional neural networks (CNNs) facilitates a precise analysis of students' dance movements, offering detailed feedback and recommendations for improvement. A holistic assessment of students' performance is attained by combining the evaluation of emotional expression with the analysis of dance movements. Experimental findings support the efficacy of this approach, demonstrating high recognition accuracy and offering valuable insights into the effectiveness of dance teaching. By embracing technological advancements, this method introduces novel ideas and methodologies for objective evaluation in dance education, paving the way for enhanced learning outcomes and pedagogical practices in the future.

摘要

本文探讨了舞蹈教学不断演变的格局,承认互联网和技术的变革性影响。随着在线平台的出现,舞蹈教育不再局限于实体教室,而是可以扩展到虚拟空间,提供更灵活、更便捷的学习体验。混合式学习将传统的线下方法与在线资源相结合,提供了一种超越地理和时间限制的通用方法。文章强调了利用双翼手风琴(DWH)多视图度量学习(MVML)算法进行面部表情识别,以增强对学生舞蹈表演中情感表达的评估。此外,动作捕捉技术与卷积神经网络(CNN)的集成有助于精确分析学生的舞蹈动作,提供详细的反馈和改进建议。通过将情感表达评估与舞蹈动作分析相结合,可以实现对学生表现的全面评估。实验结果支持了这种方法的有效性,展示了高识别准确率,并为舞蹈教学的有效性提供了有价值的见解。通过采用技术进步,这种方法为舞蹈教育中的客观评估引入了新的理念和方法,为未来提高学习成果和教学实践铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/d274a85c91f3/peerj-cs-10-2342-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/25781503b118/peerj-cs-10-2342-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/fe3298a38905/peerj-cs-10-2342-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/00afb3cc3d63/peerj-cs-10-2342-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/e68637e4c7b7/peerj-cs-10-2342-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/d274a85c91f3/peerj-cs-10-2342-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/25781503b118/peerj-cs-10-2342-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/fe3298a38905/peerj-cs-10-2342-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/00afb3cc3d63/peerj-cs-10-2342-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/e68637e4c7b7/peerj-cs-10-2342-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07c5/11622838/d274a85c91f3/peerj-cs-10-2342-g005.jpg

相似文献

1
Enhancing dance education through convolutional neural networks and blended learning.通过卷积神经网络和混合式学习提升舞蹈教育。
PeerJ Comput Sci. 2024 Oct 25;10:e2342. doi: 10.7717/peerj-cs.2342. eCollection 2024.
2
The analysis of teaching quality evaluation for the college sports dance by convolutional neural network model and deep learning.基于卷积神经网络模型和深度学习的高校体育舞蹈教学质量评价分析
Heliyon. 2024 Aug 9;10(16):e36067. doi: 10.1016/j.heliyon.2024.e36067. eCollection 2024 Aug 30.
3
A convolutional neural network based online teaching method using edge-cloud computing platform.一种基于卷积神经网络并使用边缘云计算平台的在线教学方法。
J Cloud Comput (Heidelb). 2023;12(1):49. doi: 10.1186/s13677-023-00426-6. Epub 2023 Mar 28.
4
Design of Blended Teaching Model Based on Emotion Recognition and Language Learning.基于情感识别与语言学习的混合式教学模式设计
Front Psychol. 2022 Jul 29;13:917517. doi: 10.3389/fpsyg.2022.917517. eCollection 2022.
5
The Effectiveness of a Blended Learning-Based Life Design Course: Implications of Instruction and Application of Technology.基于混合式学习的生活设计课程的有效性:教学与技术应用的启示
SN Comput Sci. 2023;4(4):360. doi: 10.1007/s42979-023-01730-3. Epub 2023 Apr 27.
6
Automatic Arrangement of Sports Dance Movement Based on Deep Learning.基于深度学习的体育舞蹈动作自动编排。
Comput Intell Neurosci. 2022 Feb 10;2022:9722558. doi: 10.1155/2022/9722558. eCollection 2022.
7
Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review.整合人工智能以评估学习环境中的情绪:一项系统的文献综述。
Front Psychol. 2024 Jun 19;15:1387089. doi: 10.3389/fpsyg.2024.1387089. eCollection 2024.
8
Design and practice of blended teaching of internal medicine nursing based on O-AMAS effective teaching model.基于 O-AMAS 有效教学模式的内科护理学混合式教学的设计与实践。
BMC Med Educ. 2024 May 28;24(1):580. doi: 10.1186/s12909-024-05588-8.
9
The analysis of dance teaching system in deep residual network fusing gated recurrent unit based on artificial intelligence.基于人工智能的融合门控循环单元的深度残差网络中的舞蹈教学系统分析
Sci Rep. 2025 Jan 8;15(1):1305. doi: 10.1038/s41598-025-85407-2.
10
Online education isn't the best choice: evidence-based medical education in the post-epidemic era-a cross-sectional study.在线教育并非最佳选择:后疫情时代基于证据的医学教育——一项横断面研究。
BMC Med Educ. 2023 Oct 10;23(1):744. doi: 10.1186/s12909-023-04746-8.

本文引用的文献

1
End2End Occluded Face Recognition by Masking Corrupted Features.基于掩蔽损坏特征的端到端遮挡人脸识别。
IEEE Trans Pattern Anal Mach Intell. 2022 Oct;44(10):6939-6952. doi: 10.1109/TPAMI.2021.3098962. Epub 2022 Sep 14.
2
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects.卷积神经网络综述:分析、应用与展望
IEEE Trans Neural Netw Learn Syst. 2022 Dec;33(12):6999-7019. doi: 10.1109/TNNLS.2021.3084827. Epub 2022 Nov 30.
3
Salient Object Detection in the Deep Learning Era: An In-Depth Survey.
深度学习时代的显著目标检测:深入调查。
IEEE Trans Pattern Anal Mach Intell. 2022 Jun;44(6):3239-3259. doi: 10.1109/TPAMI.2021.3051099. Epub 2022 May 5.
4
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives.深度学习运动捕捉基础:原理、陷阱与展望。
Neuron. 2020 Oct 14;108(1):44-65. doi: 10.1016/j.neuron.2020.09.017.