Online Learning Network Teaching System Based on Facial Recognition Technology
- https://doi.org/10.2991/msmi-17.2017.65How to use a DOI?
- network teaching system; three-dimensional learning emotion model; learning emotion detection; emotion recognition
Network teaching is a kind of teaching method for teachers to carry out teaching on Internet through the computer and Internet technology. Students wouldn't be limited by geographical and time, learning at anytime and anywhere and sharing high-quality teaching resources on internet. Network teaching is a truly effective modern education method. However, compared with the traditional classroom teaching, on network teaching, teachers can't distinguish whether students are interested in teaching content, whether students is concentrating on learning, and then make corresponding adjustment to teaching strategies by observing students' facial expressions. In order to solve this problem, this paper designed and implemented an online learning emotion detection system based on facial expression recognition technology. This system acquires students' online learning images through the webcam, analyses the focus of students' learning and their interest in teaching content through the three-dimensional learning emotion model we have established. This can help teachers to improve the content of teaching and comprehensively evaluate students' learning attitude, so as to promote the development of network teaching.
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiangnan Zhao AU - Weidong Zhu AU - Bo Wang PY - 2017/06 DA - 2017/06 TI - Online Learning Network Teaching System Based on Facial Recognition Technology BT - Proceedings of the 2017 International Conference on Management Science and Management Innovation (MSMI 2017) PB - Atlantis Press SP - 289 EP - 293 SN - 2352-5428 UR - https://doi.org/10.2991/msmi-17.2017.65 DO - https://doi.org/10.2991/msmi-17.2017.65 ID - Zhao2017/06 ER -