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Personalized Links Recommendation Based on Learning Analytics in MOOCs

Authors:
Ahmed Mohamed Fahmy Yousef

Keywords: Massive Open Online Courses; MOOCs; Viedo-Based Learning; Learning analytics; Recommendation Systems

Abstract:
Increasingly, Massive Open Online Courses (MOOCs) are widely used and have become a key instrument in Technology Enhanced Learning (TEL) models in the last few years. However, the key challenge with this kind of larger scales platforms is how to provide course participants with a quality learning materials that promote effective learning based on their needs. Indeed, this requires careful planning, monitoring and evaluation of all learning activities. Recently, learning analytics and Recommender systems are widely used in MOOCs to overcome this challenge in providing personalization and accessibility learning materials for course participants. The purpose of the current study was to determine the usability and effectiveness of a personalized links recommendation tool based on learning analytics in MOOCs. This personalized links recommendation tool was undertaken the power of crowd sourcing to provide course participants with an high quality learning material from externals recourses, e.g., Open Education Resources (OER). The present study makes several noteworthy contributions such as researching the mapping of learning data, an open personalized - links recommendation architecture, and a user-friendly and dynamic interface to deliver the recommendations.

Pages: 115 to 119

Copyright: Copyright (c) IARIA, 2017

Publication date: March 19, 2017

Published in: conference

ISSN: 2308-4367

ISBN: 978-1-61208-541-8

Location: Nice, France

Dates: from March 19, 2017 to March 23, 2017