An Analysis of Educational Cloud Platforms using Multi-agent Learning

Abstract

With the rapid increment of benefits in on-demand service, large network access, and availability, more and more industries move their focus into cloud platforms, and the field of education is no different. With the covid-19 pandemic situation, educational cloud platforms are getting more popularity and relevance among educational institutions such as open and distance universities and research institutes. This paper presents a multi-agent reinforcement learning-based approach for supporting better use of educational cloud platforms by trying to come up with a mechanism to recognize the best available option of educational cloud platforms for a specific user, identifying the adverse effects of using the selected options of platforms, if there's any and to come up with a mechanism to monitor plagiarism across platforms. In this paper, we explore multi-agent reinforcement learning techniques in finding adaptive solutions for this issue.