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Developing Online Learning Resources: Big Data, Social Networks, and Cloud Computing to Support Pervasive Knowledge
ARTICLE

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Education and Information Technologies Volume 21, Number 6, ISSN 1360-2357

Abstract

Utilizing online learning resources (OLR) from multi channels in learning activities promise extended benefits from traditional based learning-centred to a collaborative based learning-centred that emphasises pervasive learning anywhere and anytime. While compiling big data, cloud computing, and semantic web into OLR offer a broader spectrum of pervasive knowledge acquisition to enrich users' experience in learning. In conventional learning practices, a student is perceived as a recipient of information and knowledge. However, nowadays students are empowered to involve in learning processes that play an active role in creating, extracting, and improving OLR collaborative learning platform and knowledge sharing as well as distributing. Researchers have employed contents analysis for reviewing literatures in peer-reviewed journals and interviews with the teachers who utilize OLR. In fact, researchers propose pervasive knowledge can address the need of integrating technologies like cloud computing, big data, Web 2.0, and Semantic Web. Pervasive knowledge redefines value added, variety, volume, and velocity of OLR, which is flexible in terms of resources adoption, knowledge acquisition, and technological implementation.

Citation

Anshari, M., Alas, Y. & Guan, L.S. (2016). Developing Online Learning Resources: Big Data, Social Networks, and Cloud Computing to Support Pervasive Knowledge. Education and Information Technologies, 21(6), 1663-1677. Retrieved December 15, 2018 from .

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Cited By

  1. Big Data Emerging Technology: Insights into Innovative Environment for Online Learning Resources

    Miftachul Huda; Andino Maseleno, Universiti Brunei Darussalam; Pardimin Atmotiyoso, Maragustam Siregar, Roslee Ahmad, Kamarul Jasmi & Nasrul Muhamad

    International Journal of Emerging Technologies in Learning (iJET) Vol. 13, No. 1 (Jan 22, 2018) pp. 23–36

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