A Learning Analytics Model Based on Expression Recognition and Affective Computing: Review of Techniques and Survey of Acceptance

Chengliang Wang; Jian Dai; Yu Chen; Xing Zhang; Liujie Xu

doi:10.2991/978-94-6463-012-1_19

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A Learning Analytics Model Based on Expression Recognition and Affective Computing: Review of Techniques and Survey of Acceptance

Authors

Chengliang Wang¹, Jian Dai¹, Yu Chen¹, Xing Zhang¹, Liujie Xu¹^{, *}

¹College of Educational Science and Technology, Zhejiang University of Technology, Liuxia Street, Hangzhou, China

^*Corresponding author. Email: xulj2004@126.com

Corresponding Author

Liujie Xu

Available Online 9 December 2022.

DOI: 10.2991/978-94-6463-012-1_19 How to use a DOI?
Keywords: Personalized Learning; Expression Recognition; Affective Computing; Deep Learning; Teaching Evaluation
Abstract: The development of technology informatization and intelligence makes personalized adaptive learning possible, especially the rapid development of artificial intelligence provides practical technical support for intelligent teaching. This paper reviews the current research status of expression recognition and affective computing in the education field, and analyzes the technical basis required to realize affective computing in the online learning environment from facial expression recognition, image pre-processing and feature extraction, and explores the technical feasibility of the learning analysis model based on affective computing is discussed. Through a questionnaire survey, we investigated learners’ acceptance of expression recognition and affective computing applied to the field of education and teaching, and found that most learners were willing to use AI-related technologies to collect information when learning in order to improve the efficiency of learning, and some learners were even willing to share this information with teachers.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2022 International Conference on Educational Innovation and Multimedia Technology (EIMT 2022)
Series: Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date: 9 December 2022
ISBN: 978-94-6463-012-1
ISSN: 2667-128X
DOI: 10.2991/978-94-6463-012-1_19 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Chengliang Wang
AU  - Jian Dai
AU  - Yu Chen
AU  - Xing Zhang
AU  - Liujie Xu
PY  - 2022
DA  - 2022/12/09
TI  - A Learning Analytics Model Based on Expression Recognition and Affective Computing: Review of Techniques and Survey of Acceptance
BT  - Proceedings of the 2022 International Conference on Educational Innovation and Multimedia Technology (EIMT 2022)
PB  - Atlantis Press
SP  - 169
EP  - 178
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-012-1_19
DO  - 10.2991/978-94-6463-012-1_19
ID  - Wang2022
ER  -

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