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

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Huiling Wang ◽  
Jiasheng Wang

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.

2021 ◽  
Vol 9 ◽  
Author(s):  
Basim Alsaywid ◽  
Miltiadis D. Lytras ◽  
Maha Abuzenada ◽  
Hara Lytra ◽  
Lama Sultan ◽  
...  

Background: Under the urgent circumstances of the COVID-19 pandemic, higher education institutions of an international scale have resorted to online education methods, exclusive or not. Among those, medical institutions are under double pressure, fighting the pandemic's effects and, at the same time providing efficient clinical training to their residents. The main aim of the study is to evaluate the preparedness of the educational institutions for the e-learning platform transition for the delivery of medical training and also to evaluate the overall satisfaction level of the participants with their e-learning experience.Methods: This is an observational cross-sectional study design. The survey's sample included 300 medical students and residents of multiple training levels and specialties, coming from more than 15 different cities of Saudi Arabia. Filling the questionnaire required specific inclusion criteria and all obtained data were secured by the Saudi Commission of Health specialty. The main objective was to evaluate the quality of e-learning methods provided by medical universities. For the collection of the data, Survey Monkey software was used and the analysis was conducted with SPSS.Results: The study found that the frequency of digital education use increased by ~61% during the coronavirus crisis, while almost 9 out of 10 residents have used some e-learning platform. It was reported that before the pandemic, participants' online training was deemed to be rather ineffective, given the rate of 3.65 out of 10. However, despite the increase in e-learning use after COVID-19, many obstacles arose duringcthe adaptation process. According to our survey: lectures and training sessions were not conducted as per the curriculum (56.33%); both students and instructors' academic behavior and attitude changed (48.33%); engagement, satisfaction, and motivation in class were rated low (5.93, 6.33, and 6.54 out of 10 accordingly), compared to the desired ones. Still, participants accredited e-learning as a potential mandatory tool (77.67%) and pinpointed the qualifications that in their opinion will maximize educational impact.Conclusion: The study concluded that innovative restructuring of online education should be based on defined critical success factors (technical support, content enhancement, pedagogy etc.) and if possible, set priority levels, so that a more permanent e-learning practice is achievable. Also our study confirmed that students were overall satisfied with the e-learning support of the training method.


Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.


2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wen Li ◽  
Robyn Gillies ◽  
Mingyu He ◽  
Changhao Wu ◽  
Shenjun Liu ◽  
...  

Abstract Background The COVID-19 pandemic posed a huge challenge to the education systems worldwide, forcing many countries to provisionally close educational institutions and deliver courses fully online. The aim of this study was to explore the quality of the online education in China for international medical and nursing students from low- and middle-income countries (LMICs) as well as the factors that influenced their satisfaction with online education during the COVID-19 pandemic. Methods Questionnaires were developed and administered to 316 international medical and nursing students and 120 teachers at a university in China. The Chi-square test was used to detect the influence of participants’ personal characteristics on their satisfaction with online education. The Kruskal–Wallis rank-sum test was employed to identify the negative and positive factors influencing the online education satisfaction. A binary logistic regression model was performed for multiple-factor analysis to determine the association of the different categories of influential factors—crisis-, learner-, instructor-, and course-related categories, with the online education satisfaction. Results Overall, 230 students (response rate 72.8%) and 95 teachers (response rate 79.2%) completed the survey. It was found that 36.5% of students and 61.1% of teachers were satisfied with the online education. Teachers’ professional title, students’ year of study, continent of origin and location of current residence significantly influenced the online education satisfaction. The most influential barrier for students was the severity of the COVID-19 situation and for teachers it was the sense of distance. The most influential facilitating factor for students was a well-accomplished course assignment and for teachers it was the successful administration of the online courses. Conclusions Several key factors have been identified that affected the attitudes of international health science students from LMICs and their teachers towards online education in China during the COVID-19 pandemic. To improve the online education outcome, medical schools are advised to promote the facilitating factors and cope with the barriers, by providing support for students and teaching faculties to deal with the anxiety caused by the pandemic, caring for the state of mind of in-China students away from home, maintaining the engagement of out-China students studying from afar and enhancing collaborations with overseas institutions to create practice opportunities at students’ local places.


Author(s):  
Naouri Abdenacer ◽  
Hangxing Wu ◽  
Nouri Nabil Abdelkader ◽  
Sahraoui Dhelim ◽  
Huansheng Ning

Sign in / Sign up

Export Citation Format

Share Document