Conference paper 2021

Communication and Security Issues in Online Learning during the COVID-19 Pandemic

2021 IEEE 9th International Conference on Information, Communication and Networks, ICICN 2021
Conference · pp. 538-544
Abstract

This era is characterized by the rapid evolution of technology and its application in all sectors of day to day life. At the same time, the outbreak of the COVID-19 pandemic has highlighted the distance education as vital, compulsory and worldwide, for Educational Institutions. The attainments of technology are set at the service of education and the problem of security and privacy in online learning is emerged. This paper focuses on the development of a secure online learning system, which utilizes edge computing and privacy mechanisms, such as trust evaluation from direct and indirect observations. The proposed system provides to the Educational Institutions the advantage of conducting online courses, by ensuring privacy in communication for students, professors and the organization. © 2021 IEEE.

Keywords

Author Keywords

COVID-19 privacy communication issues online learning system

Index Keywords

E-learning Learning systems Online learning COVID-19 privacy Educational institutions Online learning systems ITS applications Evolution of technology Technology application Communication issue Security issues
Author Affiliations
Department of Applied Informatics, University of Macedonia, Thessaloniki, Central Macedonia, Greece
Aristotle University of Thessaloniki, Thessaloniki, Central Macedonia, Greece
Department of Electrical & Computer Engineering, University of Western Macedonia, Kozani, Western Macedonia, Greece
Department of Computer Science, Nagoya Institute of Technology, Nagoya, Aichi, Japan
Funding & Acknowledgements
No funding information
References 10 References
1 Current Ecosystem of Learning Management Systems in Higher Education Student Faculty and IT Perspectives, (2014)
2 Moubayed, Abdallah, E-Learning: Challenges and Research Opportunities Using Machine Learning Data Analytics, IEEE Access, 6, pp. 39117-39138, (2018)
3 Booth, Brandon M., Toward active and unobtrusive engagement assessment of distance learners, 2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017, 2018-January, pp. 470-476, (2017)
4 Murshed, Mahbub, Engagement detection in e-learning environments using convolutional neural networks, Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019, pp. 80-86, (2019)
5 Otoo-Arthur, David, A scalable heterogeneous big data framework for e-learning systems, 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2020 - Proceedings, (2020)
6 Otoo-Arthur, David, A systematic review on big data analytics frameworks for higher education - Tools and algorithms, ACM International Conference Proceeding Series, pp. 79-87, (2019)
7 Tech Rep, (2018)
8 Artificial Intelligence Review, (2019)
9 Kolar, Petar, Experiences with Online Education during the COVID-19 Pandemic-Stricken Semester, Proceedings Elmar - International Symposium Electronics in Marine, 2020-September, pp. 97-100, (2020)
10 Mohammad, Rasheed, Understanding education difficulty during covid-19 lockdown: Reports on Malaysian university students' experience, IEEE Access, 8, pp. 186939-186950, (2020)
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