Online Education Optimization Based on Edge Computing under the COVID-19 Pandemic

The COVID-19 pandemic has strongly affected education in China, even if education departments and corresponding schools took a series of measures to manage online education of the school's new semester in China, including maneuver, learning platform allocation, and teacher training. In this paper, edge computing is used to optimize online education, and a task offloading algorithm is designed to minimize the computing delay of terminal tasks. Through preparation, practice, and reflection of this online education, this study aims to comprehensively demonstrate the learning condition of online education in China and present the real adjustment impact based on the problems encountered during the process. Although several schools gradually reopened to students in 3 months, several improvements are warranted in various ways. This study proposes the construction of education infrastructure, the adjustment of teaching organization, and the learning methods of teachers and students, providing a clear guiding significance for the development and enhancement of online education in the future.

Automated summary

This study optimized online education using edge computing and a task offloading algorithm to minimize computing delay. Through preparation, practice, and reflection, it comprehensively demonstrated the learning condition of online education in China and proposed improvements in education infrastructure, teaching organization, and learning methods for teachers and students.