Details

Autor(en) / Beteiligte

• Zhang, Xiaojing
• Che, Hangjun
• Hangjun Che

Titel

Research on the Quality Improvement of Ideological and Political Teaching in the Internet of Things Environment

Ist Teil von

• Wireless communications and mobile computing, 2022-10, Vol.2022, p.1-10

Ort / Verlag

Oxford: Hindawi

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• The research of Internet of Things (IOT) network and deep learning has gradually become a hot research topic for academic researchers. As a typical form of IoT networks and deep learning, it is of great significance to study the IOT and ideological and political education. In view of the drawbacks of the ideological and political course, such as boring teaching content and rigid teaching methods, strengthening teaching construction with the help of intelligent teaching assistant system has become the focus of attention in the field of education. The continuous improvement of IOT technology has brought certain changes to people’s society and students’ lifestyle. At present, the teaching aids and systems on the market focus on one aspect of the teaching process, not all aspects of the teaching process. According to the problem that the ideological and political course is a required course but its teaching efficiency is not high, this paper will combine the basic characteristics of the Internet of Things and deep learning to design the ideological and political teaching system, improve the drawbacks of the traditional system with an intelligent system, which will involve the method and calculation method of the edge matching algorithm, so as to improve the efficiency of ideological and political teaching and student learning. In addition, the image feature algorithm (ORB algorithm) is used to process images, and the improved surf feature processing algorithm is combined to explore how to improve the teaching quality of the course with the help of intelligent algorithms. The studies in this paper provide an important guidance to the application of both IoT networks and deep learning.