Analysis of informationization teaching algorithms based on the internet of things

Abstract

This study intends to solve the problems brought on by regional differences in the distribution of educational resources, inadequate growth of schools, and various levels of informationization in university education. Because of the complicated functional framework that is now in place, university life is unsafe for both teachers and students. The issue is further complicated by challenges with maintaining several cards for one person, a lack of seamless software and educational platform integration, and multiple obstacles between data and users. Information inequality is exacerbated by inadequate learning resources, and development is hampered by the lack of efficient teacher-student feedback mechanisms. It can also be difficult to accurately manage a huge group of people. This study uses the web and artificial intelligence (AI) technology to create comprehensive, succinct, effective, and high-performing college instruction information technology in order to address these difficulties. Irrespective of the time or day, the system seeks to serve teachers and students while managing a sizable influx of visitors. Throughout the development process, the system is actively optimized and improved by the research. The experiment’s findings illustrate a robust interface function via practical assessment. Usability assessments show that the feedback is better than the previous system, with response times being reduced. Additionally, the updated system shows a typical reduction in overall electrical usage.