Research on multimodal online learning status evaluation based on video camera

A person's state is reflected in many aspects, such as emotions and body movements. Online teaching makes it difficult for teachers to accurately understand the learning status of students due to the separation of space between teachers and students. This paper extracts images from video cameras, from which identifies the learner's emotion, head posture and fatigue, and evaluates the learner's learning state by synthesizing the three-sided information. The seven emotions were divided into three categories: negative, positive and natural. Head posture is defined by Euler angles, and fatigue is determined by blinking frequency. Hierarchical decision-making method is used in the model for information fusion. The learning state assessment method proposed in this paper integrates the performance of both internal and external aspects of psychology and behavior, and has high reliability. Real-time understanding of students' learning status can help improve the effectiveness of teaching.