research-article

A Framework of Online Learning and Experiment System Based on Affective Computing

Authors:

Xiaomei TaoAuthors Info & Claims

EBIMCS '20: Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science

Pages 619 - 624

https://doi.org/10.1145/3453187.3453405

Published: 24 March 2021 Publication History

Abstract

With the development of online education, the problem of emotional deficiency has gradually emerged. In recent years, the research about emotional interaction in online education, and the application of Affective Computing in online education came into being. This paper introduces the impact of Affective Computing on online education, including the use of expressions, speech, eye movement, and physiological signals, as well as text and questionnaires and other modals, or multi-modal to infer students' emotional state and learning state in the learning process, and then to realize personalized learning support. The emerging technologies such as Artificial Intelligence, Big Data and Brain Computer Interface, can be applied to online education combined with Affective Computing technologies to improve the interaction between learners and the on-line learning system. This paper proposes a framework of online learning and experiment system based on Affective Computing supported by Artificial Intelligence, Big Data and Brain Computer Interface. The system can provide personalized education for students and help them to carry out experiments in a virtual reality environment. The framework can reduce the impact on students in the process of data collection, so that it can obtain more real data, and the framework uses emerging technologies to provide support and reference for emotion analysis and recognition, which will make the analysis and response more timely and emotion recognition more accurate.

References

[1]

CNNIC. 2020. The 45th China Statistical Report on Internet Development(April 28, 2020). http://www.cnnic.cn/hlwfzyj/hlwxzbg/hlwtjbg/202004/P020200428596599037028.pdf.

[2]

Agnieszka Landowska. 2014. Affective learning manifesto-10 years later. European Conference on e-Learning(Academic Conferences International Limited, 281).

[3]

Picard R W. 2000. Affective computing. MIT press.

[4]

Ze-nan L. 2019. Replace 80% of manual class evaluation! This Tsinghua team is using AI technology to improve classroom teaching(Jan. 15, 2019). https://www.sohu.com/a/289356348_129720.

[5]

GhasemAghaei R, Arya A, and Biddle R. 2016. A dashboard for affective E-learning: Data visualization for monitoring online learner emotions. EdMedia+ Innovate Learning. Association for the Advancement of Computing in Education(AACE, 1536-1543).

[6]

Yang D, Alsadoon A, and Prasad P W C, et al. 2018. An emotion recognition model based on facial recognition in virtual learning environment. Procedia Computer Science, 125, 2--10.

[7]

Xiaowei W, and Zhiliang W. 2006. E-Learning Assistant System Based on Artificial Psychology. Microcomputer information, 22, 014, 275--277.

[8]

Nan Z. 2016. Design and Research of Wearable Affective Computing Device for online Learning. Hang zhou: ZHEJIANG UNIVERSITY.

[9]

Chuan H. 2018. Case analysis of educational application based on wearable devices. Science and technology communication, 21, 90--91.

[10]

Rao-dong Y. 2015. Application of attention detection technology based on brain wave and computer vision in e-learning. Kunming: Kunming University of Science and Technology.

[11]

JingQiu Z. 2018. EVALUATION SYSTEM OF ATTENTIVENESS BASED ON MULTIPLE PHYSIOLOGICAL INFORMATION. Harbin: Harbin Institute of Technology.

[12]

Xi-Rong M, Lin L, and Jing S. 2005. The e-Learning System Model Based on Affective Computing. Computer science, 32, 8, 131--133.

[13]

Yao-feng X, Jin-peng Y, and Wei G, et al. 2018. Research on multi-modal emotion computing for online learning. China Educational Technology, 373, 02, 52--56, 89.

[14]

Duo S, Xuesong L. 2012. An e-learning system based on affective computing. Physics Procedia, 24, 1893--1898.

[15]

Wu L, Yanhui Z, and Yingzi F. 2007. Speech emotion recognition in e-learning system based on affective computing. Third International Conference on Natural Computation(IEEE, 809-813).

[16]

Lun X, An-qi Z, and Zhi-ling T, et al. 2020. An emotional state regulation system for online learning. Beijing: CN109255366B(July 17, 2020).

[17]

Xin-quan J, Lin-li W, and Xian-min Y. 2016. Construction of Online Learning Emotional Measurement Model based on Big Data. Modern educational technology, 26, 12, 5--11.

[18]

Xuefeng W, Yuan G, and Haijun Z, et al. 2018. Chinese Higher Education and Chinese K-12 Educational Technology Adoption Trend Analysis---Based on the Comparison between 2017 NMC Technology Outlook for Chinese Higher Education and 2016 NMC Technology Outlook for Chinese K-12 Education. China Educational Technology, 01, 72--76.

[19]

Xin-fang L, Jing L, and Ming-zhuo L. 2016. Application Analysis and Deep Infusion Strategies of Emerging Technologies in Higher Education------Based on Content Analysis of the Horizon Report(2004-2016) in New Media Consortium. Research on Higher Education in Heilongjiang Province, 12, 24--27.

[20]

Wen-ji F, and Lei Y. 2020. Analysis on the Development Trend and Application of Higher Education Technologies------based on the Research and Analysis of Horizon Report from 2015 to 2020. Modern educational technology, 30, 04, 25--31.

[21]

Hu Bin, Cai Hanshu, and Zhao Qinglin, et al. 2017. A biological information feedback system based on virtual reality. Gansu: CN106377252A(Feb. 08, 2017).

[22]

Chen M, Ma Y, Song J, and Hu B, et al. 2016. Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring. Mobile Networks & Applications, 21, 5, 1--21.

Digital Library

[23]

Ping Z, Kai N, and Hui T, et al. 2019. Technology prospect of 6G mobile communications. Journal of Communications, 40, 01, 141--148.

Cited By

Vyakaranam ARamayah BMaul T(2024)Preliminary Study: Speech Emotion Recognition in Online Teaching From the Perspective of Educators Especially Late Deafened2024 2nd International Conference on Software Engineering and Information Technology (ICoSEIT)10.1109/ICoSEIT60086.2024.10497503(216-221)Online publication date: 28-Feb-2024
https://doi.org/10.1109/ICoSEIT60086.2024.10497503
Vyakaranam AMaul TRamayah B(2024)A review on speech emotion recognition for late deafened educators in online educationInternational Journal of Speech Technology10.1007/s10772-023-10064-727:1(29-52)Online publication date: 24-Jan-2024
https://dl.acm.org/doi/10.1007/s10772-023-10064-7
Rathi SDeshpande Y(2023)Embedding Affect Awareness in e-Learning: A Systematic Outline of the LiteratureAI, IoT, Big Data and Cloud Computing for Industry 4.010.1007/978-3-031-29713-7_3(39-63)Online publication date: 1-Aug-2023
https://doi.org/10.1007/978-3-031-29713-7_3

Index Terms

A Framework of Online Learning and Experiment System Based on Affective Computing

Recommendations

An affective computing approach to develop the game-based adaptive learning material for the elementary students
HCCE '12: Proceedings of the 2012 Joint International Conference on Human-Centered Computer Environments

E-learning has been the mainstream in the field of education. Nowadays, many children use the game-based learning materials for leaning. However, the difficulty and frustration of the materials usually decrease learners' pleasure and interests. Learners'...
Emotional Design Tutoring System Based on Multimodal Affective Computing Techniques

In a traditional class, the role of the teacher is to teach and that of the students is to learn. However, the constant and rapid technological advancements have transformed education in numerous ways. For instance, in addition to traditional, face to ...
Impact of inquiry interventions on students in e-learning and classroom environments using affective computing framework
Abstract
Effective teaching strategies improve the students’ learning rate within academic learning time. Inquiry-based instruction is one of the effective teaching strategies used in the classrooms. But these teaching strategies are not adapted in other ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

EBIMCS '20: Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science

December 2020

718 pages

ISBN:9781450389099

DOI:10.1145/3453187

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Guilin: Guilin University of Technology, Guilin, China
International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

EBIMCS 2020

EBIMCS 2020: 2020 3rd International Conference on E-Business, Information Management and Computer Science

December 5 - 6, 2020

Wuhan, China

Acceptance Rates

EBIMCS '20 Paper Acceptance Rate 112 of 566 submissions, 20%;

Overall Acceptance Rate 143 of 708 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
106
Total Downloads

Downloads (Last 12 months)19
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Vyakaranam ARamayah BMaul T(2024)Preliminary Study: Speech Emotion Recognition in Online Teaching From the Perspective of Educators Especially Late Deafened2024 2nd International Conference on Software Engineering and Information Technology (ICoSEIT)10.1109/ICoSEIT60086.2024.10497503(216-221)Online publication date: 28-Feb-2024
https://doi.org/10.1109/ICoSEIT60086.2024.10497503
Vyakaranam AMaul TRamayah B(2024)A review on speech emotion recognition for late deafened educators in online educationInternational Journal of Speech Technology10.1007/s10772-023-10064-727:1(29-52)Online publication date: 24-Jan-2024
https://dl.acm.org/doi/10.1007/s10772-023-10064-7
Rathi SDeshpande Y(2023)Embedding Affect Awareness in e-Learning: A Systematic Outline of the LiteratureAI, IoT, Big Data and Cloud Computing for Industry 4.010.1007/978-3-031-29713-7_3(39-63)Online publication date: 1-Aug-2023
https://doi.org/10.1007/978-3-031-29713-7_3

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten