###### Abstract  
Developing a smart classroom can make the modern classroom more efficient and intelligent. Much research has been conducted pertaining to smart classrooms for hearing-impaired college students. However, there have been few significant breakthroughs in mobilizing students' learning efficiency as measured by information transmission, communication, and interaction in class. This research collects data through nonparticipatory observation and in-depth interviews and analyzes available data on classroom interaction needs of these students. We found that diversified explanations, recordable interactive contents, and interaction between teachers and students could improve the learning effects in the classroom. We also propose a tracking-processing method based on gesture recognition and representation and present a design for a processing system based on AT89C52 microcontroller and Kinect. In this way, sign language can be translated into text and all students can receive the information and participate in the interaction, which greatly improves students' autonomy and enthusiasm of learning. This design enables deaf students to fully use classroom learning resources, reduces learning time costs, and improves learning efficiency. It can assist teachers in teaching and tutoring students to enhance their experience. h 202  
these classrooms are important but sometimes inaccessible places to study, raise questions, and have discussions. Although multimedia facilities can help students understand and communicate better with teachers and improve learning efficiency, traditional classrooms and facilities are neither distinctively 'personalized' nor well-targeted for them [4; 5; 6]. As current classrooms and facilities maintain no significant difference from those of normal-hearing students, the existing classrooms and facilities cannot match their internal learning needs. As a result, they have been trapped in a predicament with low efficiency where they have trouble in understanding what they have learned and become frustrated in learning. Sign language is the dominant language for hearing-impaired college students. The development of sign-language recognition technology has a direct impact on their learning efficiency. Sign-language recognition technology mainly includes sensor-based recognition technology and vision-based recognition technology. Sensor-based sign-language recognition technologies are typically data gloves, which collect data by an optical fiber sensor on the dorsum of the fingers. The change of the fingers causes the rotation of the optical fiber, with alternating lights generated. The analog signal transmits the information of the finger change to the processor [7; 8; 9]. The vision-based sign-language gesture-recognition method consists of acquiring digital video-stream information for sign-language gestures using a camera, and then processing the information in a video frame by using a computer digital image-processing technology to acquire characteristics and complete gesture recognition. For example, [PERSON] [10] proposed a sign-language recognition method based on curvature scale space (CSS) and hidden Markov model (HMM), and [PERSON] [11] proposed a new method to recognize gestures with static gesture language letters. These technologies are of great help for single-sign language recognition. However, there are still problems in the application in the classroom, and advanced recognition technology has not been introduced into the classroom. To sum up, there have been many studies to help these students solve communication problems. However, these studies mainly focus on the intelligence of technology, which is not combined with the classroom situation and real needs of students. Therefore, the learning effect cannot be well-improved. Based on nonparticipatory observation and in-depth discussions, the paper adopts qualitative analysis to find the problems among them in the classroom interaction, especially in communication.