Multimedia augmented m-learning: Issues, trends and open challenges

Yousafzai, Abdullah and Chang, Victor and Gani, Abdullah and Noor, Rafidah Md (2016) Multimedia augmented m-learning: Issues, trends and open challenges. International Journal of Information Management, 36 (5). pp. 784-792. ISSN 0268-4012, DOI https://doi.org/10.1016/j.ijinfomgt.2016.05.010.

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Official URL: http://dx.doi.org/10.1016/j.ijinfomgt.2016.05.010

Abstract

The advancement in mobile technology and the introduction of cloud computing systems enable the use of educational materials on mobile devices for a location- and time-agnostic learning process. These educational materials are delivered in the form of data and compute-intensive multimedia-enabled learning objects. Given these constraints, the desired objective of mobile learning (m-learning) may not be achieved. Accordingly, a number of m-learning systems are being developed by the industry and academia to transform society into a pervasive educational institute. However, no guideline on the technical issues concerning the m-learning environment is available. In this study, we present a taxonomy of such technical issues that can impede the life cycle of multimedia-enabled m-learning applications. The taxonomy is devised based on the issues related to mobile device heterogeneity, network performance, content heterogeneity, content delivery, and user expectation. These issues are discussed, along with their causes and measures, to achieve solutions. Furthermore, we identify several trending areas through which the adaptability and acceptability of multimedia-enabled m-learning platforms can be increased. Finally, we discuss open challenges, such as low complexity encoding, data dependency, measurement and modeling, interoperability, and security as future research directions.

Item Type: Article
Funders: Bright Spark Program and High Impact Research Grant, University of Malaya: BSP/APP/1635/2013 and UM.C/625/1/HIR/MOE/FCSIT/03
Uncontrolled Keywords: Mobile learning; Cloud learning; Multimedia-enabled learning; Personalized learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 30 May 2018 01:36
Last Modified: 12 Oct 2018 01:53
URI: http://eprints.um.edu.my/id/eprint/18756

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