A New Model of Multiple Intelligence for Teaching English Informatics in the IoT Scenario

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 11
• Main Author: Ning, Yang
• Format: Journal Article
• Language: English
• Published: New York Hindawi 21.06.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Blended learning; Classrooms; Colleges & universities; Computation offloading; Computer networks; Computer simulation; Decision making; Distance learning; Distributed processing; Education; Educational technology; English teachers; Informatics; Information technology; Intelligence; Internet; Internet of Things; Latency; Mental task performance; Middleware; Modules; Multiple intelligences; Online instruction; Simulation; Skills; Students; Study and teaching; Teaching; Technological change; Time series; Virtual environments; Virtual reality; United Kingdom
• DOI: 10.1155/2022/5642284
• ISSN: 1687-5265; 1687-5273

This paper presents an in-depth study on the new mode of intelligent multidistance teaching of English with the help of virtual scenes of the Internet of Things. The virtual simulation technology is integrated into the traditional IoT teaching, and the professional education of IoT application technology is tapped; from the analysis of the current situation of IoT skills teaching and the feasibility of carrying out virtual simulation teaching, the “four-driven” design principle is proposed, and the teaching design is combined with the virtual simulation technology teaching, and the case design of the skill-based virtual simulation technology teaching of experience, demonstration, interaction, and assessment in the virtual environment is given. This paper presents the case design of virtual simulation teaching in a virtual environment with experience, demonstration, interaction, and assessment, and the multidimensional effect evaluation of IoT skills teaching and researches the application of virtual simulation to IoT skills teaching through the above four aspects. In this paper, a framework for distributed collaborative computing is built using an asynchronous message queue MQ, which enables multiple nodes to serve a task through task splitting. The DeepCluster module can effectively cluster the time series by deep representation learning and obtain the typical variation of time series patterns. In the task offloading module of the framework, a task offloading decision algorithm based on a value-constrained multi 0–1 backpacking model is designed to minimize task processing latency with an optimal offloading solution. The system test results show that the proposed distributed computing framework and offloading decision algorithm can significantly reduce the processing latency of large tasks.