On the Issue of Reusability and Adaptability in M-learning Systems

Pettersson, Oskar; Gil, Didac

doi:10.1109/wmute.2010.48

Cited by 9 publications

(3 citation statements)

References 7 publications

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“…In recent years, mobile learning is a field which combines mobile computing and electronic learning (e-learning) [3], have provided more interactive and personalized instruction based on the learner's context [4] and learner cognitive profile [5]. Content adaptation [6] provides the most suitable applications according to students' computing context [7], referred to devices, network, location, and time, which affect students' mobile access of learning content [3]. In particular, network layer Quality of Service (QoS) parameters, that have huge influence on the bandwidth allocation in the process of multimedia content delivery [8] are lower start up delay and reduced end-to-end delay.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Delivery of Multimedia Content in Mobile Environment

Karadimce¹,

Malekian²,

Abdullah³

2013

Appl. Math. Inf. Sci.

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Process of delivering multimedia learning objects such as text, video, images, audio, and animation requires real-time streaming of multimedia content from the M-learning systems to the mobile users. With enhancing the integration methods of the mobile devices into the learning systems, we have possibility to allow easy storing, recording and delivering of multimedia contents to the students in real-time. In this paper, we will present an intelligent mobile learning (M-learning) system for delivery of multimedia content depending of the context-aware conditions. This paper considers the significance of high QoS requirements for applications that are essential to achieve higher continuity of real-time multimedia contents. In this research, we have considered the best queuing packet delivery algorithm to provide appropriate multimedia contents to improve required bandwidth for user's communications.

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Section: Introductionmentioning

confidence: 99%

Intelligent Delivery of Multimedia Content in Mobile Environment

Karadimce¹,

Malekian²,

Abdullah³

2013

Appl. Math. Inf. Sci.

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“…This educational approach has become more and more popular because it allows learners to conveniently study anytime, anywhere; no longer are students restricted to the confines of a classroom. Due to the emergence of heterogeneous mobile devices and wireless technologies, such as cell/smartphones, Personal Digital Assistants (PDAs), netbooks, and other similar mechanisms, the requirements for delivering and displaying learning content on mobile devices are increasing rapidly (Tretiakov and Kinshuk 2004;Yang et al 2007a;Chang et al 2008;Queirós and Pinto 2009;Pettersson and Gil 2010). While the types of mobile devices and the available e-learning subject matter proliferate, a large gap still exists between traditional desktop and mobile-based systems.…”

Section: Introductionmentioning

confidence: 99%

“…Most existing research and systems either separately or partially considered the features of mobile devices, network conditions, and learner preferences (Zhao et al 2008;Nimmagadda et al 2010). Therefore, a pressing issue is how to consider not only the mobile devices' capabilities, but also the available network conditions and learners' diverse preferences in order to efficiently provide properly personalized learning content in mobile learning environments (Tong et al 2006;Mylonas et al 2007;Basaeed et al 2007;Zhao et al 2008;Franzoni et al 2008;Muntean 2008;Pettersson and Gil 2010;Nimmagadda et al 2010).…”

Section: Introductionmentioning

confidence: 99%

A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

Tseng

Lin

et al. 2011

User Model User-Adap Inter

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With the heterogeneous proliferation of mobile devices, the delivery of learning materials on such devices becomes subject to more and more requirements. Personalized learning content adaptation, therefore, becomes increasingly important to meet the diverse needs imposed by devices, users, usage contexts, and infrastructure. Historical server logs offer a wealth of information on hardware capabilities, learners' preferences, and network conditions, which can be utilized to respond to a new user request with the personalized learning content created from a previous similar request. In this paper, we propose a Personalized Learning Content Adaptation Mechanism (PLCAM), which applies data mining techniques, including clustering and decision tree approaches, to efficiently manage a large number of historical learners' requests. The proposed method will intelligently and directly deliver proper personalized learning content with higher fidelity from the Sharable Content Object Reference Model (SCORM)-compliant Learning Object Repository (LOR) by means of the proposed adaptation decision and content synthesis processes. Furthermore, the experimental results indicate that it is efficient and is expected to prove beneficial to learners.

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Adaptive Multimedia Delivery in M-Learning Systems Using Profiling

Karadimce

Davčev

2014

ICT Innovations 2013

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On the Issue of Reusability and Adaptability in M-learning Systems

Cited by 9 publications

References 7 publications

Intelligent Delivery of Multimedia Content in Mobile Environment

Intelligent Delivery of Multimedia Content in Mobile Environment

A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

Adaptive Multimedia Delivery in M-Learning Systems Using Profiling

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