Integration and Sharing Model of College English Software Teaching Resources Based on the Internet of Things

In today’s organized world, resource sharing actively participates in increasing the teaching and learning experience by addressing gaps in material availability and educational approaches. College English teaching resources contain textbooks, multimedia content, lesson plans, and online tools to improve language achievement and communication skills. By integrating Internet of Things (IoT) technology into training, a more dynamic and resource-efficient learning environment has been produced, which has transformed conventional teaching techniques. However, conventional resource-sharing approaches often overcome limitations, such as unequal distribution, outdated content, and lack of real-time updates. The research presented an integration and sharing model for college English software teaching resources, leveraging IoT technology to address challenges, such as fragmented resources and ineffective utilization. The Manta Ray Foraging-driven Deep Long Short-Term Memory (MRF-DLSTM) was utilized to estimate details in the exchange of instructional materials for English majors. Teaching resources, including e-books, multimedia content, quizzes, and interactive lessons, are collected from several sources, with IoT devices gathering and assigning data. The data were preprocessed using data cleaning, removing any identical data, and normalizing the established data. To extract the characteristics, Principal Component Analysis (PCA) is utilized and can decrease the number of dimensions from the preprocessed data. The suggested MRF-DLSTM model performs better than current techniques with an RMSE of 0.18, MAE of 0.30, MAPE of 0.25, and a 95% accuracy rate. According to experimental findings, the suggested model greatly enhances the sharing and integration of materials for teaching English using college software, enhancing their availability and promoting the development of modern, technology-driven English education.