Toward a New Framework of Recommender Memory Based System for MOOCs

El Alami Taha, El Kadiri Kamal Eddine, Chrayah Mohamed


MOOCs is the new wave of remote learning that has revolutionized it since its apparition, offering the possibility to teach a very big group of student, at the same time, in the same course, within all disciplines and without even gathering them in the same geographic location, or at the same time; Allowing the sharing of all type of media and document and providing tools to assessing student performance. To benefit from all this advantages, big universities are investing in MOOCs platforms to valorize their approach, which makes MOOC available in a multitude of languages and variety of disciplines. Elite universities have open their doors to student around the world without requesting tuition or claiming a college degree, however even with the major effort reaching to maximize students visits and hooking visitors to the platform, using recommending systems propose content likely to please learners, the dropout rate still very high and the number of users completing a course remains very low compared to those who have quit. In this paper we propose an architecture aiming to maximize users visits by exploiting users big data and combining it with data available from social networks.


big data, content base filtering, distant learning, moocs,

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).