Design of intelligent diagnosis system for teaching quality based on wireless sensor network and data mining

• Published in: EURASIP journal on wireless communications and networking Vol. 2021; no. 1; pp. 1 - 12
• Main Authors: Zhang, Yanping, Huang, Wanwei
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 21.01.2021; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Algorithms and Architectures for Industrial Wireless Sensor Networks; Artificial neural networks; Communications Engineering; Computer simulation; Convolutional neural network; Data mining; Diagnosis; Engineering; Fuzzy comprehensive evaluation; Information Systems Applications (incl.Internet); Mobile computing; Networks; Quality of education; Sensors; Signal,Image and Speech Processing; Teaching methods; Teaching quality diagnosis; Wireless networks; Wireless sensor network; Wireless sensor networks; Wireless sensor network; Teaching quality diagnosis; Convolutional neural network; Fuzzy comprehensive evaluation
• ISSN: 1687-1499; 1687-1472; 1687-1499
• DOI: 10.1186/s13638-021-01902-w

With the popularization of computers and various mobile intelligent terminals, intelligent teaching systems based on learners are becoming more and more popular among learners. The above phenomenon has greatly affected and changed the current teaching quality diagnosis methods and models. However, the author found through investigation that the current intelligent teaching quality diagnosis still has different degrees of deficiencies in the design and implementation. In response to the above problems, this paper proposes a teaching quality intelligent diagnosis model based on the combination of wireless sensor networks and fuzzy comprehensive evaluation algorithms. First of all, this article is based on the wireless sensor network to link various levels of intelligent teaching systems, and constructs the information transmission structure of the teaching intelligent diagnosis system. Secondly, this article uses fuzzy comprehensive evaluation and convolutional neural network algorithms to evaluate and excavate intelligent teaching information. Finally, the model successfully passed the simulation test and simulation application, which can provide intelligent diagnosis of teaching quality for modern intelligent teaching system.