探索同学交往中的情绪状态对学生学习成绩的影响

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Nasrin Dehbozorgi;Mourya Teja Kunuku
{"title":"探索同学交往中的情绪状态对学生学习成绩的影响","authors":"Nasrin Dehbozorgi;Mourya Teja Kunuku","doi":"10.1109/TE.2023.3335171","DOIUrl":null,"url":null,"abstract":"Contribution: An AI model for speech emotion recognition (SER) in the educational domain to analyze the correlation between students’ emotions, discussed topics in teams, and academic performance.Background: Research suggests that positive emotions are associated with better academic performance. On the other hand, negative emotions have a detrimental impact on academic achievement. This highlights the importance of taking into account the emotional states of the students to promote a supportive learning environment and improve their motivation and engagement. This line of research allows the development of tools that allow educators to address students’ emotional needs and provide timely support and interventions. Intended Outcome: This work analyzes students’ conversations and their expressed emotions as they work on class activities in teams and investigates if their conversations are course-related or not by applying topic extraction to the conversations. Furthermore, a comprehensive analysis is conducted to identify the correlation between emotions expressed by students and the discussed topics with their performance in the course in terms of their grades. Application Design: The student’s performance is formatively evaluated, taking into account a combination of their scores in various components. The core of the developed model comprises a speech transcriber module, an emotion analysis module, and a topic extraction module. The outputs of all these modules are processed to identify the correlations. Findings: The findings show a strong positive correlation between the expressed emotions of “relief” and “satisfaction” with students’ grades and a strong negative correlation between “frustration” and grades. Data also shows a strong positive correlation between course-related topics discussed in teams and grades and a strong negative correlation between noncourse-related topics and grades.","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring the Influence of Emotional States in Peer Interactions on Students’ Academic Performance\",\"authors\":\"Nasrin Dehbozorgi;Mourya Teja Kunuku\",\"doi\":\"10.1109/TE.2023.3335171\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Contribution: An AI model for speech emotion recognition (SER) in the educational domain to analyze the correlation between students’ emotions, discussed topics in teams, and academic performance.Background: Research suggests that positive emotions are associated with better academic performance. On the other hand, negative emotions have a detrimental impact on academic achievement. This highlights the importance of taking into account the emotional states of the students to promote a supportive learning environment and improve their motivation and engagement. This line of research allows the development of tools that allow educators to address students’ emotional needs and provide timely support and interventions. Intended Outcome: This work analyzes students’ conversations and their expressed emotions as they work on class activities in teams and investigates if their conversations are course-related or not by applying topic extraction to the conversations. Furthermore, a comprehensive analysis is conducted to identify the correlation between emotions expressed by students and the discussed topics with their performance in the course in terms of their grades. Application Design: The student’s performance is formatively evaluated, taking into account a combination of their scores in various components. The core of the developed model comprises a speech transcriber module, an emotion analysis module, and a topic extraction module. The outputs of all these modules are processed to identify the correlations. Findings: The findings show a strong positive correlation between the expressed emotions of “relief” and “satisfaction” with students’ grades and a strong negative correlation between “frustration” and grades. Data also shows a strong positive correlation between course-related topics discussed in teams and grades and a strong negative correlation between noncourse-related topics and grades.\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10367874/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10367874/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0

摘要

贡献:教育领域的语音情感识别(SER)人工智能模型,用于分析学生的情绪、团队中讨论的话题和学习成绩之间的相关性:研究表明,积极情绪与更好的学习成绩有关。背景:研究表明,积极情绪与学习成绩的提高有关,而消极情绪则对学习成绩产生不利影响。这凸显了考虑学生的情绪状态对促进有利的学习环境、提高他们的学习动力和参与度的重要性。这一研究方向有助于开发工具,使教育工作者能够满足学生的情感需求,并提供及时的支持和干预。预期成果:本作品分析了学生以小组为单位开展课堂活动时的对话及其表达的情绪,并通过对对话进行主题提取,调查他们的对话是否与课程相关。此外,还将进行综合分析,以确定学生表达的情绪和讨论的话题与他们在课程中的成绩表现之间的相关性。应用设计:对学生的成绩进行形成性评价时,会综合考虑他们在各个部分的得分。所开发模型的核心包括语音转录模块、情感分析模块和话题提取模块。对所有这些模块的输出进行处理,以确定相关性。研究结果研究结果表明,学生表达的 "轻松 "和 "满意 "情绪与成绩之间存在很强的正相关性,而 "沮丧 "情绪与成绩之间存在很强的负相关性。数据还显示,团队中讨论的与课程相关的话题与成绩之间存在很强的正相关性,而与课程无关的话题与成绩之间存在很强的负相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Influence of Emotional States in Peer Interactions on Students’ Academic Performance
Contribution: An AI model for speech emotion recognition (SER) in the educational domain to analyze the correlation between students’ emotions, discussed topics in teams, and academic performance.Background: Research suggests that positive emotions are associated with better academic performance. On the other hand, negative emotions have a detrimental impact on academic achievement. This highlights the importance of taking into account the emotional states of the students to promote a supportive learning environment and improve their motivation and engagement. This line of research allows the development of tools that allow educators to address students’ emotional needs and provide timely support and interventions. Intended Outcome: This work analyzes students’ conversations and their expressed emotions as they work on class activities in teams and investigates if their conversations are course-related or not by applying topic extraction to the conversations. Furthermore, a comprehensive analysis is conducted to identify the correlation between emotions expressed by students and the discussed topics with their performance in the course in terms of their grades. Application Design: The student’s performance is formatively evaluated, taking into account a combination of their scores in various components. The core of the developed model comprises a speech transcriber module, an emotion analysis module, and a topic extraction module. The outputs of all these modules are processed to identify the correlations. Findings: The findings show a strong positive correlation between the expressed emotions of “relief” and “satisfaction” with students’ grades and a strong negative correlation between “frustration” and grades. Data also shows a strong positive correlation between course-related topics discussed in teams and grades and a strong negative correlation between noncourse-related topics and grades.
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
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
×
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:481959085
Book学术官方微信