Construction and optimization of a smart classroom system for emotional computing

HUANG Lijuan; CHENG Zhengyi; YANG Ziyan

doi:10.11959/j.issn.2096-3750.2024.00398

您当前的位置：

首页 >

文章列表页 >

Construction and optimization of a smart classroom system for emotional computing

Theory and Technology | 更新时间：2024-08-16

- Construction and optimization of a smart classroom system for emotional computing
- Chinese Journal on Internet of Things Vol. 8, Issue 2, Pages: 116-126(2024)
- 作者机构：
  
  1.四川天府新区麓湖小学,四川成都 610218
  2.电子科技大学,四川成都 611731
  3.东北师范大学,吉林长春 130117
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2024.00398
  CLC：
- Received：26 March 2024，
  
  Revised：2024-06-15，
  
  Published：10 June 2024
- 稿件说明：
移动端阅览
黄丽娟,程正一,杨紫岩.面向情感计算的智慧教室系统构建以及优化[J].物联网学报,2024,08(02):116-126.

HUANG Lijuan,CHENG Zhengyi,YANG Ziyan.Construction and optimization of a smart classroom system for emotional computing[J].Chinese Journal on Internet of Things,2024,08(02):116-126.
黄丽娟,程正一,杨紫岩.面向情感计算的智慧教室系统构建以及优化[J].物联网学报,2024,08(02):116-126. DOI： 10.11959/j.issn.2096-3750.2024.00398.

HUANG Lijuan,CHENG Zhengyi,YANG Ziyan.Construction and optimization of a smart classroom system for emotional computing[J].Chinese Journal on Internet of Things,2024,08(02):116-126. DOI： 10.11959/j.issn.2096-3750.2024.00398.

摘要

在新一代信息技术与智慧教育深度融合的时代背景下，智慧教室系统的研究与应用受到广泛的关注。然而，当前智慧教室系统的应用还处于起步阶段，其缺乏对学生的实时性反馈及实时正向干预功能。针对此问题，首先通过构建基于情感学习的智慧教室系统，引入边缘计算来提高课堂实时性和智能化程度，以实现学生学习期间情感状态的实时性反馈。其次，为提升智慧教室系统的性能，利用凸优化相关理论实现系统资源的优化分配。最后，通过验证，智慧教室系统的多资源联合优化方法可以有效降低设备数据采集及处理的最大时延，能极大地提高智慧教室系统的情感计算实时性能；系统不盲目追求平均时延最小化，有效避免出现单一用户因实时性差而体验不佳的情况。该系统和算法成果对未来智慧教室的建设具有借鉴和参考意义。

Abstract

In the context of the deep integration of new generation information technology and smart education

the research and application of smart classroom systems have received widespread attention. However

the current application of smart classroom systems is still in its early stages

lacking real-time feedback and real-time positive intervention functions for students. To solve this problem

a smart classroom system based on emotional learning was built

and edge computing was introduced to improve the real-time and intelligent level of the classroom

so as to achieve real-time feedback of students' emotional state during learning. Secondly

to improve the performance of the smart classroom system

convex optimization theory was utilized to optimize the allocation of system resources. Finally

through verification

the multiple resources joint optimization method of the smart classroom system can effectively reduce the maximum delay of device data collection and processing

greatly improve the real-time performance of emotional computing in the smart classroom system

and avoid blindly pursuing the minimum average delay

effectively avoiding the situation where a single user experienced poorly due to poor real-time performance. Overall

the system and algorithm achievements have reference and significance for the construction of future smart classrooms.

关键词

Keywords

references

王立新 , 田荔枝 . 高校智慧教学系统建设探索与实践 [J ] . 安阳师范学院学报 , 2023 ( 2 ): 152 - 155 .

WANG L X , TIAN L Z . Exploration and practice of building smart teaching system in colleges and universities [J ] . Journal of Anyang Normal University , 2023 ( 2 ): 152 - 155 .

程春梅 . 智慧校园建设总体架构模型及典型应用分析 [J ] . 数字技术与应用 , 2021 , 39 ( 2 ): 178 - 180 .

CHENG C M . Research and analysis on the overall typical application and architecture model of building smart campus [J ] . Digital Technology & Application , 2021 , 39 ( 2 ): 178 - 180 .

吴修国 , 李亚琪 , 汪思鹏 . 基于教育大数据的混合式精准教学模式构建与应用 [J ] . 计算机教育 , 2022 ( 11 ): 142 - 145,150 .

WU X G , LI Y Q , WANG S P . Construction and application of hybrid precision teaching model based on educational big data [J ] . Computer Education , 2022 ( 11 ): 142 - 145,150 .

盘东霞 , 莫健樱 . 我国智慧教育研究热点及趋势探讨: 基于知识图谱的可视化分析 [J ] . 电脑与电信 , 2021 ( 11 ): 99 - 104 .

PAN D X , MO J Y . Discussion on the research hotspots and trends of wisdom education in china—visual analysis based on knowledge map [J ] . Computer & Telecommunication , 2021 ( 11 ): 99 - 104 .

王琪 . SpringBoot在线学习系统的开发 [J ] . 互联网周刊 , 2023 ( 5 ): 60 - 62 .

WANG Q . Development of SpringBoot online learning system [J ] . China Internet Week , 2023 ( 5 ): 60 - 62 .

丁继红 . 多模态协作学习分析理论模型、实践逻辑和教育价值 [J ] . 远程教育杂志 , 2023 , 41 ( 2 ): 95 - 104 .

DING J H . The theoretical model, practice logic and educational value of multimodal collaborative learning analysis [J ] . Journal of Distance Education , 2023 , 41 ( 2 ): 95 - 104 .

胡存兵 , 张喜鲜 . 大数据助力大规模个性化教育实践探索 [J ] . 中国教育技术装备 , 2021 ( 6 ): 34 - 37 .

HU C B , ZHANG X X . Practice and exploration of large-scale personalized education assisted by big data [J ] . China Educational Technology & Equipment , 2021 ( 6 ): 34 - 37 .

吴吉义 , 李文娟 , 曹健 , 等 . 智能物联网AIoT研究综述 [J ] . 电信科学 , 2021 , 37 ( 8 ): 1 - 17 .

WU J Y , LI W J , CAO J , et al . AIoT: A taxonomy, review and future directions [J ] . Telecommunications Science , 2021 , 37 ( 8 ): 1 - 17 .

李智杰 . AIoT技术的发展趋势及面临问题探究 [J ] . 中国安防 , 2022 ( 6 ): 31 - 34 .

LI Z J . Exploring the development trends and challenges of AIoT Technology [J ] . China Security & Protection , 2022 ( 6 ): 31 - 34 .

顾小清 , 王超 . 打开技术创新课堂教学的新窗：刻画AIoT课堂应用场景 [J ] . 现代远程教育研究 , 2021 , 33 ( 2 ): 3 - 12 .

GU X Q , WANG C . New thinking for using technology to innovate classroom teaching: Portraying the application scenarios of AIoT in the classroom [J ] . Modern Distance Education Research , 2021 , 33 ( 2 ): 3 - 12 .

Y. H. Fan . Computational model analysis of affective cognitive reasoning in artificial intelligence [J ] . Journal of Shanghai Normal University (Philosophy and Social Sciences Edition) , 2020 , 49 ( 02 ): 94 - 103 .

马磊 , 吴慧 , 郭晓蓓 . 情感计算联合边缘计算在商业银行数字化转型中的应用探索 [J ] . 西南金融 , 2021 ( 9 ): 40 - 51 .

MA L , WU H , GUO X B . Application of affective computing and edge computing in the digital transformation of commercial banks [J ] . Southwest Finance , 2021 ( 9 ): 40 - 51 .

李洪修 , 丁玉萍 . 人工智能背景下情感教学的运行与实现 [J ] . 现代教育技术 , 2020 , 30 ( 9 ): 21 - 27 .

LI H X , DING Y P . The operation and realization of affective teaching under the background of artificial intelligence [J ] . Modern Educational Technology , 2020 , 30 ( 9 ): 21 - 27 .

徐振国 , 张冠文 , 孟祥增 , 等 . 基于深度学习的学习者情感识别与应用 [J ] . 电化教育研究 , 2019 , 40 ( 2 ): 87 - 94 .

XU Z G , ZHANG G W , MENG X Z , et al . Learners’ emotion recognition and its application based on deep learning [J ] . e-Education Research , 2019 , 40 ( 2 ): 87 - 94 .

王冬青 , 韩后 , 邱美玲 , 等 . 基于情境感知的智慧课堂动态生成性数据采集方法与模型 [J ] . 电化教育研究 , 2018 , 39 ( 5 ): 26 - 32 .

WANG D Q , HAN H , QIU M L , et al . Dynamic generative data acquisition methods and model based on context awareness in smart classroom [J ] . e-Education Research , 2018 , 39 ( 5 ): 26 - 32 .

赵玲朗 , 范佳荣 , 唐烨伟 , 等 . 智慧学习资源进化框架、模型研究: 基于多目标优化视角 [J ] . 电化教育研究 , 2020 , 41 ( 12 ): 59 - 64 .

ZHAO L L , FAN J R , Tang Y W , et al . Research on evolutionary framework and model of intelligent learning resources: multi-objective optimization perspective [J ] . e-Education Research , 2020 , 41 ( 12 ): 59 - 64 .

朱珂 , 张思妍 , 刘濛雨 . 基于情感计算的虚拟教师模型设计与应用优势 [J ] . 现代教育技术 , 2020 , 30 ( 6 ): 78 - 85 .

ZHU K , ZHANG S Y , LIU M Y . The design and application advantages of virtual teachers model based on affective computing [J ] . Modern Educational Technology , 2020 , 30 ( 6 ): 78 - 85 .

LIU Y Q , PENG M G , SHOU G C , et al . Toward edge intelligence: multiaccess edge computing for 5G and Internet of Things [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 8 ): 6722 - 6747 .

王晓俊 . 多输入融合水下图像增强与目标识别 [D ] . 舟山 : 浙江海洋大学 , 2022 .

WANG X J . Multi-input fusion underwater image enhancement and target recognition [D ] . Zhoushan : Zhejiang Ocean University , 2022 .

王哲 . 边缘计算发展现状与趋势展望 [J ] . 自动化博览 , 2021 ( 2 ):　 22 - 29 .

WANG Z . Development status and trend of edge computing [J ] . Automation Panorama 1 , 2021 ( 2 ): 22 - 29 .

马兆星 . 边缘计算环境下面向依赖性任务的分割策略研究和联合优化设计 [D ] . 安徽 : 合肥工业大学 , 2022 .

MA Z X . Research on segmentation strategy and joint optimization design for dependent tasks in edge computing environment [D ] . Anhui : Hefei University of Technology , 2022 .

施巍松 , 张星洲 , 王一帆 , 等 . 边缘计算: 现状与展望 [J ] . 计算机研究与发展 , 2019 , 56 ( 1 ): 69 - 89 .

SHI W S , ZHANG X Z , WANG Y F , et al . Edge computing: state-of-the-art and future directions [J ] . Journal of Computer Research and Development , 2019 , 56 ( 1 ): 69 - 89 .

周伟 , 杜静 , 汪燕 , 等 . 面向智慧教育的学习环境计算框架 [J ] . 现代远程教育研究 , 2022 , 34 ( 5 ): 91 - 100 .

ZHOU W , DU J , WANG Y , et al . The learning environment computing framework for smart education [J ] . Modern Distance Education Research , 2022 , 34 ( 5 ): 91 - 100 .

王薇 , 王小奇 , 李颖 , 等 . 5G+边缘计算技术赋能的智慧校园应用探究 [J ] . 中国新通信 , 2023 , 25 ( 21 ): 89 - 92 .

WANG W , WANG X Q , LI Y , et al . Research on smart campus application enabled by 5G+edge computing technology [J ] . China New Telecommunications , 2023 , 25 ( 21 ): 89 - 92 .

宋亚楼 . 边缘计算在校园环境中的缓存优化与任务卸载研究 [D ] . 北京 : 北京交通大学 , 2022 .

SONG Y L . Research on cache and task offloading optimization of edge computing in campus environment [D ] . Beijing : Beijing Jiaotong University , 2022 .

ZHANG Z Y , ZHOU H , LI D W . Joint optimization of multi-user computing offloading and service caching in mobile edge computing [C ] // Proceedings of the 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) . Piscataway : IEEE Press , 2021 : 1 - 2 .

LI X Z . 5G converged network resource allocation strategy based on reinforcement learning in edge cloud computing environment [J ] . Computational Intelligence and Neuroscience , 2022 , 2022 : 6174708 .

郑玉玮 , 毕婧华 , 陆畅 . 多媒体学习中的情绪设计: 理论基础和设计方法 [J ] . 现代教育科学 , 2019 ( 2 ): 134 - 140, 151 .

ZHENG Y W , BI J H , LU C . Emotional design in multimedia learning: the theoretical basis and design methods [J ] . Modern Education Science , 2019 ( 2 ): 134 - 140, 151 .

WANG H Y , LI B , WU S , et al . Rethinking the learning paradigm for dynamic facial expression recognition [C ] // Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2023 : 17958 - 17968 .

LIU F , WANG H Y , SHEN S Y , et al . OPO-FCM: a computational affection based OCC-PAD-OCEAN federation cognitive modeling approach [J ] . IEEE Transactions on Computational Social Systems , 2023 , 10 ( 4 ): 1813 - 1825 .

SHEN S Y , ZHOU A M . Temporal shift module with pretrained representations for speech emotion recognition [J ] . Intelligent Computing , 2024 , 3 : 0073 .

SHEN S Y , GAO Y , LIU F , et al . Emotion neural transducer for fine-grained speech emotion recognition [C ] // Proceedings of the ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Piscataway : IEEE Press , 2024 : 10111 - 10115 .

HUANG L J , WANG N Y , YANG Z Y , et al . Emotional computing at the Edge to Support Effective IoE Applications in Future Classroom [C ] // Proceedings of the 2022 International Conference on Advanced Learning Technologies (ICALT) . Piscataway : IEEE Press , 2022 : 400 - 402 .

刘智勇 . 5G超密集网络中面向移动边缘计算资源分配策略研究 [D ] . 北京 : 北京信息科技大学 , 2021 .

LIU Z Y . Research on Resource Allocation Strategy for Mobile Edge Computing in 5G Ultra-dense Network [D ] . Beijing : Beijing Information Science & Technology University , 2021 .

CHEN M , HAO Y X . Task offloading for mobile edge computing in software defined ultra-dense network [J ] . IEEE Journal on Selected Areas in Communications , 2018 , 36 ( 3 ): 587 - 597 .

YANG Z Y , MEI H B , WANG W Y , et al . Joint resource allocation for emotional 5G IoT systems using deep reinforcement learning [J ] . International Journal of Machine Learning and Cybernetics , 2021 , 12 ( 12 ): 3517 - 3528 .

Views

265

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Joint multi-dimensional resource optimization for model compression in wireless federated learning

Dynamic QoS-aware intelligent edge computing resource management algorithm for body area networks

Collaborative offloading computing scheme based on energy harvesting technology

Edge server deployment decision based on improved NSGA-Ⅱ in the Internet of vehicles edge computing scenario

Research on agricultural IoT pest and disease image recognition algorithm based on STM32

Related Author

ZHU Guangzhao

ZHU Xiaorong

XU Ding

MU Siqi

WEN Shuo

LU Yang

AI Bo

WANG Jun

Related Institution

School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications

School of Electronics and Information Engineering, Beijing Jiaotong University

School of Computer and Information Technology, Beijing Jiaotong University

School of Sports Engineering, Beijing Sport University

Nanjing University of Posts and Telecommunications

⁰