使用多模态深度学习的学生参与评估。

IF 2.6 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
PLoS ONE Pub Date : 2025-06-10 eCollection Date: 2025-01-01 DOI:10.1371/journal.pone.0325377
Lijuan Yan, Xiaotao Wu, Yi Wang
{"title":"使用多模态深度学习的学生参与评估。","authors":"Lijuan Yan, Xiaotao Wu, Yi Wang","doi":"10.1371/journal.pone.0325377","DOIUrl":null,"url":null,"abstract":"<p><p>Student engagement assessment plays an important role in enhancing students' positive performance and optimizing teaching methods. In this paper, a multimodal deep learning framework is proposed for student engagement assessment. Based on this framework, we propose a method for engagement assessment that utilizes data from three modalities: video, text, and logs. This method implements the extraction of engagement indicators, the fusion of asynchronous data, the use of deep learning models to evaluate engagement levels, and the use of gradient magnitude mapping to further distinguish subtle differences between engagement levels. In subsequent empirical studies, we explore the applicability of several popular deep CNN models in this method and validate the reliability of the engagement quantification results using statistical methods. The analysis results demonstrate that the framework, which combines multimodal asynchronous data fusion and deep learning models to assess engagement, is both effective and practical.</p>","PeriodicalId":20189,"journal":{"name":"PLoS ONE","volume":"20 6","pages":"e0325377"},"PeriodicalIF":2.6000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12151416/pdf/","citationCount":"0","resultStr":"{\"title\":\"Student engagement assessment using multimodal deep learning.\",\"authors\":\"Lijuan Yan, Xiaotao Wu, Yi Wang\",\"doi\":\"10.1371/journal.pone.0325377\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Student engagement assessment plays an important role in enhancing students' positive performance and optimizing teaching methods. In this paper, a multimodal deep learning framework is proposed for student engagement assessment. Based on this framework, we propose a method for engagement assessment that utilizes data from three modalities: video, text, and logs. This method implements the extraction of engagement indicators, the fusion of asynchronous data, the use of deep learning models to evaluate engagement levels, and the use of gradient magnitude mapping to further distinguish subtle differences between engagement levels. In subsequent empirical studies, we explore the applicability of several popular deep CNN models in this method and validate the reliability of the engagement quantification results using statistical methods. The analysis results demonstrate that the framework, which combines multimodal asynchronous data fusion and deep learning models to assess engagement, is both effective and practical.</p>\",\"PeriodicalId\":20189,\"journal\":{\"name\":\"PLoS ONE\",\"volume\":\"20 6\",\"pages\":\"e0325377\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12151416/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PLoS ONE\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1371/journal.pone.0325377\",\"RegionNum\":3,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS ONE","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1371/journal.pone.0325377","RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

学生参与评价对提高学生的积极表现和优化教学方法具有重要作用。本文提出了一个用于学生参与度评估的多模态深度学习框架。基于此框架,我们提出了一种利用视频、文本和日志三种模式的数据进行敬业度评估的方法。该方法实现了敬业度指标的提取,异步数据的融合,利用深度学习模型评估敬业度,并利用梯度幅度映射进一步区分敬业度之间的细微差异。在后续的实证研究中,我们探索了几种流行的深度CNN模型在该方法中的适用性,并使用统计方法验证了敬业度量化结果的可靠性。分析结果表明,该框架结合了多模态异步数据融合和深度学习模型来评估员工敬业度,既有效又实用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Student engagement assessment using multimodal deep learning.

Student engagement assessment using multimodal deep learning.

Student engagement assessment using multimodal deep learning.

Student engagement assessment using multimodal deep learning.

Student engagement assessment plays an important role in enhancing students' positive performance and optimizing teaching methods. In this paper, a multimodal deep learning framework is proposed for student engagement assessment. Based on this framework, we propose a method for engagement assessment that utilizes data from three modalities: video, text, and logs. This method implements the extraction of engagement indicators, the fusion of asynchronous data, the use of deep learning models to evaluate engagement levels, and the use of gradient magnitude mapping to further distinguish subtle differences between engagement levels. In subsequent empirical studies, we explore the applicability of several popular deep CNN models in this method and validate the reliability of the engagement quantification results using statistical methods. The analysis results demonstrate that the framework, which combines multimodal asynchronous data fusion and deep learning models to assess engagement, is both effective and practical.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
PLoS ONE
PLoS ONE 生物-生物学
CiteScore
6.20
自引率
5.40%
发文量
14242
审稿时长
3.7 months
期刊介绍: PLOS ONE is an international, peer-reviewed, open-access, online publication. PLOS ONE welcomes reports on primary research from any scientific discipline. It provides: * Open-access—freely accessible online, authors retain copyright * Fast publication times * Peer review by expert, practicing researchers * Post-publication tools to indicate quality and impact * Community-based dialogue on articles * Worldwide media coverage
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信