Title: QOE-based recommender system in the applications of e-learning resources

Authors: G. Senthil Kumar; C. Lakshmi

Addresses: Department of Software Engineering, SRMIST, Katankulathur-603203, TN, India ' Department of Software Engineering, SRMIST, Katankulathur-603203, TN, India

Abstract: Web services are more prevalent in the development of educational resources like, massive open online courses (MOOCs) and e-learning. In the current scenario of e-learning platforms, selecting the right course according to the user requirements is a tiresome and time-consuming process because there are numerous courses are available on the web. In such situations, to judge the course user can rely on the quality of service (QoS) descriptions given by the service provider but the QoS descriptions are not always reliable. Hence it leads to dissatisfaction to the users if the requirements are not fulfilled. In this paper, an alternative approach called quality of experience (QoE) adapted as a metric for arbitrating the courses. The main objective of this paper is to build an efficient e-learning recommender system that guides the user to opt a course according to his requirements, i.e., availability, cost and reputation.

Keywords: e-learning; learning management systems; LMS; quality of service; QoS; quality of experience; QoE; web services.

DOI: 10.1504/IJAIP.2023.132381

International Journal of Advanced Intelligence Paradigms, 2023 Vol.25 No.3/4, pp.398 - 409

Received: 12 Mar 2018
Accepted: 27 Dec 2018
Published online: 19 Jul 2023 *

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