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Title: E-Learning environment based intelligent profiling system for enhancing user adaptation
Authors: Kaur, Ramneet
Gupta, Deepali
Madhukar, Mani
Singh, Aman
Abdelhaq, Maha
Alsaqour, Raed
Breñosa, Jose
Goyal, Nitin
Keywords: education; e-learning; customization; learner profile; MOOCs; user-profiling
Issue Date: 2022
Publisher: Electronics
Abstract: Online learning systems have expanded significantly over the last couple of years. Massive Open Online Courses (MOOCs) have become a major trend on the internet. During the COVID-19 pandemic, the count of learner enrolment has increased in various MOOC platforms like Coursera, Udemy, Swayam, Udacity, FutureLearn, NPTEL, Khan Academy, EdX, SWAYAM, etc. These platforms offer multiple courses, and it is difficult for online learners to choose a suitable course as per their requirements. In order to improve this e-learning education environment and to reduce the drop-out ratio, online learners will need a system in which all the platform’s offered courses are compared and recommended, according to the needs of the learner. So, there is a need to create a learner’s profile to analyze so many platforms in order to fulfill the educational needs of the learners. To develop a profile of a learner or user, three input parameters are considered: personal details, educational details, and knowledge level. Along with these parameters, learners can also create their user profiles by uploading their CVs or LinkedIn. In this paper, the major innovation is to implement a user interface-based intelligent profiling system for enhancing user adaptation in which feedback will be received from a user and courses will be recommended according to user/learners’ preferences.
Appears in Collections:School of Engineering & Technology

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