A Learning Analytics Model Based on Expression Recognition and Affective Computing: Review of Techniques and Survey of Acceptance
- 10.2991/978-94-6463-012-1_19How to use a DOI?
- Personalized Learning; Expression Recognition; Affective Computing; Deep Learning; Teaching Evaluation
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.
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Cite this article
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 -