Affective And Behavioral Computing For Personalized E-Learning

With the development and widespread use of motion-sensing technology, users can interact with computer through body movement physically and break through the the 2D limitation. This paper analyzes the hybrid of external motion and internal emotion, then discusses Affective and Behavioral Computing (ABC) model and methods for personalized E-Learning.In particular, the main contents focused by this thesis are as follows:1. This thesis provides Affective and Behavioral Computing, and discusses the personalized E-learning style based on cognitive experience, then analyzes the creative art experience for children which can promote the all-around intellectual development of user by physical movement with the help of virtual perceptual avatar.2. This thesis analyzes the Affective and Behavioral Computing interaction model,the Affective and Behavioral Computing avatar, and discusses the key technology of motion recognition and related algorithms.3. This thesis proposes an interaction painting by Shu-Kong Facsimile based on motion sensors. Experiment results show that our approach can inspire the interests of the children on learning ink painting more effectively than the traditional pen-based interface, teach through lively activities and make a deeper impression.