文献类型：期刊文献

英文题名：Design of English Teaching Sharing System Combining Internet of Things and Memory Mechanism

作者：Guo, Jie[1]

通讯作者：Guo, J[1]

机构：[1]Xinxiang Univ, Sch Foreign Languages, Xinxiang 453000, Henan, Peoples R China

第一机构：新乡学院外国语学院

通讯机构：[1]corresponding author), Xinxiang Univ, Sch Foreign Languages, Xinxiang 453000, Henan, Peoples R China.|[1107117]新乡学院外国语学院;[11071]新乡学院;

年份：2022

卷号：2022

外文期刊名：WIRELESS COMMUNICATIONS & MOBILE COMPUTING

收录：;EI(收录号：20222912363936);Scopus(收录号：2-s2.0-85133963412);WOS:【SCI-EXPANDED(收录号:WOS:000826052800004)】；

语种：英文

外文关键词：Data fusion - Design - Engineering education - Information analysis - Information management - Informatization - Internet of Everything - Internet of things - Teaching

摘要：In the era of Internet of Everything, English teaching has evolved continuously undergoing radical changes. Informatization and modern technologies are continuously promoting the reform of education, and the application of these technological tools makes information sharing a reality. Timely information sharing contributes to the development of English teaching as a part of curriculum requirements. As a consequence of this rapid progress, the theoretical and practical research on IoT-based English teaching sharing systems is also increasing. In this paper, we initiate an exhaustive research of the popular methodologies and then adopt LSTM network management platform data to address the shortcomings of the current teaching system has memory mechanism issues. At the outset, the standard English curriculum is discussed to establish the background, and then, data fusion among various types of information is considered. This is done by completely utilizing independent and scattered heterogeneous data in the information platform without changing or minimizing the information available in the shared system. Then, the information fusion is thoroughly analyzed resulting in the proposal of a multisource and multigranularity information fusion method based on a neural network. The method implements a study considering multisource heterogeneous data, which is followed by carrying out of functional design and process design. The paper also conducts a study on CNN-based multisource information association and designs information association algorithms to customize the dataset. Finally, the study of multigranularity feature fusion based on CNN and LSTM is implemented. The design relevant to granularity calculation algorithm helps in the customization of the dataset. The multigranularity feature fusion is conducted integrating CNN and LSTM. The experimental results show that the proposed method effectively integrates the scattered teaching information in the system, which is conducive to improving the utilization rate of teaching resources and simultaneously promoting the development of teaching information.