Wireless Sensor Network and AI Application for Educational Technology Course

Abstract

Over the course of its long development, the modern educational technology curriculum has undergone several changes and amassed a lot of information. Theoretically speaking, the state places a strong priority on the use of IT in schools. Students majoring in education should take educational technology courses so that they can learn the characteristics and application techniques of core current information-based teaching media and incorporate them into their own lesson plans and classroom activities. This will help them meet the information needs of today's classrooms as they evolve with the advent of educational modernization and availability of educational information. Thus, this research employs a wireless sensor network (WSN) to gather and send data on ed tech classes and then employs AI to assess those classes' quality and guide real-time changes to how they are taught and complete the following tasks: (1) The development status of educational technology courses and WSN at home and abroad is introduced. (2) The application of WSN in teaching is introduced, the basic principle of GRU neural network and related optimization algorithms is expounded, and the quality evaluation system of educational technology courses is constructed. (3) The IPSO-Adam-GRU evaluation model improves the GRU neural network's hyperparameters with the help of the improved PSO approach and Adam gradient descent. The model is fed test data for evaluation, and the findings are compared to those from an expert's evaluation to determine how well the model performs. The results demonstrate that the model established for this article is superior to others since it provides a more accurate assessment.