CLOUD COMPUTING AND BIG DATA AS CONVERGENT TECHNOLOGIES FOR MOBILE E-LEARNING

Big data science is a powerful, pervasive force in knowledge management today, particularly for addressing the complex challenge of E-Learning. Cloud Computing comes in to provide access to entirely new education capabilities through sharing resources and services and managing and assigning resource...

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Bibliographic Details
Published in:eLearning and Software for Education 2014, Vol.10 (1), p.113-120
Main Authors: MILITARU, Traian-Lucian, Vulpe, Alexandru, Todoran, Gyorgy, Suciu, George
Format: Article
Language:English
Subjects:
Social Sciences
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Summary:Big data science is a powerful, pervasive force in knowledge management today, particularly for addressing the complex challenge of E-Learning. Cloud Computing comes in to provide access to entirely new education capabilities through sharing resources and services and managing and assigning resources effectively. Done right, the application of scientific principles to the creation of a true E-Learning optimization strategy can lead to significant improvements for both instructors and students. The paper is a general overview on how to provide a convergent platform that integrates cloud computing and big data for mobile E-Learning. Furthermore, we present how we use a cloud search based application to seek for weak signals in big data by analyzing multimedia data (text, voice, picture, video) and mining online social networks. Our research explains why education can no longer thrive without a science-based E-Learning platform, defines and illustrates the right science-based approach, and calls out the key features and functionalities of leading science-based E- Learning optimization systems. The paper fills a gap in the big data literature by providing an overview of big data tools running on cloud platforms, which could be applied for E-Learning strategies. In particular, given a cloud platform, we propose to leverage trivial and non-trivial connections between different curricula information and data from online social networks, in order to find patterns that are likely to provide innovative solutions to existing E-Learning problems. The aggregation of such weak signals will provide evidence of connections between student related behaviour faster and better than trivial mining of data. As a consequence, the software has a significant potential for matching E-Learning strategies and education challenges that are related in non-obvious ways.
ISSN:2066-026X
2066-8821