Deep-Learning-Guided Student Intelligent Classroom Management System - Xiaobing Niu (Applied Bionics and Biomechanics)
Aiming at the limitations of old-fashioned instructing knowledge and the insufficiency of existing dynamically managed classrooms, a deep-learning-guided Unity3D-based intelligent classroom management system and the corresponding instrument support were proposed. We first build a virtual scene and import Unity3D motors, in order to improve practical fake action proofs through C# script and prefix system. Subsequently, we attempt to solve the permission arrangement proposition in multiperson entity scenes, and accordingly, we complete the cognitive assistance module using authorization strategy. Our system can provide different students with tailored permissions, foresee text, video, and some flexible functions. Our system can be divided into multiple Spring Cloud frameworks. We further leverage the Redis to optimize the system architecture. The system can be conveniently applied in chemistry instructing with clear virtual auditions under the government direct supervision. It can effectively address authority issues in real scenarios while enhancing the learning efficiency and increasing accessibility. A set of intelligent classroom behavior system based on deep learning that supported by cunning learning methods are proposed accordingly. It can complete the classroom perception ministry. It can optimally conduct status monitoring as well as classroom assignment and discussion services through deep learning vision techniques such as face perception and facial expression analysis. Extensive experimental results have shown the competitive performance of our method.