Ubiquitous Edge Computing For AI And IT In Wireless-Driven Classroom Educational Frameworks

Abstract

Multimodal teaching utilizes various instructional strategies and tools, enhancing education comprehensively. This approach is adaptable, benefiting from diverse formats like video lectures, interactive elements, and quizzes, and ensuring adequate resources for every student type. Individual learners require a platform, such as a recorded broadcast or an online learning system, to access the material. This study focuses on the integration of wireless sensor networks, artificial intelligence, and multimodal educational theories. Beyond academic settings, multimodal data technology finds practical application, demonstrated through the implementation of a Random Offloading Process. This process is evaluated against traditional ANN classification methods. The findings indicate that the newly proposed method significantly improves educational outcomes, boasting an impressive accuracy rate of 99.69%.