An Adaptive Caching Strategy for m-Learning Based on SCORM Sequencing and Navigation

Chang, Hsuan Pu; Shih, Timothy K.; Li, Qing; Wang, Chun-Chia; Wang, Te-Hua; Chao, Louis R.

doi:10.1007/s11280-008-0044-2

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…[35]. Wireless networks-related adaptive solutions include various catching strategies to predict and download in advance learning materials, in order to provide learners with access to educational content when they lack network coverage [36], or to reduce the waiting time associated with low-speed wireless networks [37]. Adaptive solutions addressing the learning context and in particular the learner location, make use of technologies such as RFID for object tagging, GPS for outdoor positioning, or WiFi-based positioning for detecting learners' indoor location [38].…”

Section: A Overviewmentioning

confidence: 99%

Energy-Aware Mobile Learning:Opportunities and Challenges

Moldovan

Weibelzahl

Muntean

2014

IEEE Commun. Surv. Tutorials

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Abstract-As mobile devices are becoming more powerful and affordable they are increasingly used for mobile learning activities. By enabling learners' access to educational content anywhere and anytime, mobile learning has both the potential to provide online learners with new opportunities, and to reach less privileged categories of learners that lack access to traditional e-learning services. Among the many challenges with mobile learning, the battery-powered nature of mobile devices and in particular their limited battery life, stands out as one issue that can significantly limit learners' access to educational content while on the move. Adaptation and personalisation solutions have widely been considered for overcoming the differences between learners and between the characteristics of their mobile devices. However, while various energy saving solutions have been proposed in order to provide mobile users with extended device usage time, the areas of adaptive mobile learning and energy conservation in wireless communications failed to meet under the same umbrella. This paper bridges the two areas by presenting an overview of adaptive mobile learning systems as well as how these can be extended to make them energy-aware. Furthermore, the paper surveys various approaches for energy measurement, modelling and adaptation, three major aspects that have to be considered in order to deploy energy-aware mobile learning systems. Discussions on the applicability and limitations of these approaches for mobile learning are also provided.

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Section: A Overviewmentioning

confidence: 99%

Energy-Aware Mobile Learning:Opportunities and Challenges

Moldovan

Weibelzahl

Muntean

2014

IEEE Commun. Surv. Tutorials

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“…This can be achieved by using mobile and portable devices such as PDAs, cellular phones, UMPCs (Ultra Mobile PC), smart phones and TPCs (Table PCs) [16][17][18]. They are able to connect learners with learning systems and educational contents in order to present educational contents and to offer a place to exchange educational information between learners and instructors [19][20][21][22][23][24]. [25] analyzed m-Learning qualities according to the following four characteristics: first, in an m-Learning environment learners can learn anywhere regardless of their location.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Design and Development of m-Learning Service Based on 3G Cellular Phones

Chung¹,

Lee²

2012

Journal of Information Processing Systems

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Abstract-As the knowledge society matures, not only distant, but also off-line universities are trying to provide learners with on-line educational contents. Particularly, high effectiveness of mobile devices for e-Learning has been demonstrated by the university sector, which uses distant learning that is based on blended learning. In this paper, we analyzed previous m-Learning scenarios and future technology prospects. Based on the proposed m-Learning scenario, we designed cellular phonebased educational contents and service structure, implemented m-Learning system, and analyzed m-Learning service satisfaction. The design principles of the m-Learning service are 1) to provide learners with m-Learning environment with both cellular phones and desktop computers; 2) to serve announcements, discussion boards, Q&A boards, course materials, and exercises on cellular phones and desktop computers; and 3) to serve learning activities like the reviewing of full lectures, discussions, and writing term papers using desktop computers and cellular phones. The m-Learning service was developed on a cellular phone that supports H.264 codex in 3G communication technology. Some of the functions of the m-Learning design principles are implemented in a 3G cellular phone. The contents of lectures are provided in the forms of video, text, audio, and video with text. One-way educational contents are complemented by exercises (quizzes)

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“…PC, to retrieve the learning content. The usage behaviors, as part of learner profile, will be recorded in a general format described in [1]. We take the recorded information as a kind of usage experience.…”

Section: A Hardscorm In the Context Of Cyber-imentioning

confidence: 99%

Shift to Cyber-I: Reexamining Personalized Pervasive Learning

Yen

Huang

et al. 2010

2010 IEEE/ACM Int'l Conference on Green Computing and Communications &Amp; Int'l Conference on Cyber, Physical and Social Compu

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Recently, there have been lots of researches on how to provide personalized services to different users according to their personal characters, needs, situations, contexts and so on. Such personalized services are often based on users' profiles provided by the users in the beginning, and users' records or experiences in using corresponding systems in certain periods. However, different systems or terminals collect user information independently and cannot share the user's personal information collected by these different systems/terminals. As a result, each system can only utilize the limited information collected by the system itself to provide services or recommendations, and it thus cannot meet what users' needs in varied situations across the different systems/terminals. Therefore, it is still an open issue on how to gather, share and utilize various kinds of personal information to effective personalized services in right time, right place and right means to users. In this paper, a case study for personalized pervasive learning is discussed as one typical application of the concept of Cyber-I, an individual's counterpart on the cyberspace. The Cyber-I aims to provide a better environment for users to obtain what they may really need on the Web, and it can be considered as an innovated possibility of web usage scenario in the near future. Our case study using pervasive devices (iPad, cell phone, and laptop) for learning issues can be regarded as the best practice to support the proposed Cyber-I.

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An Adaptive Caching Strategy for m-Learning Based on SCORM Sequencing and Navigation

Cited by 6 publications

References 11 publications

Energy-Aware Mobile Learning:Opportunities and Challenges

Energy-Aware Mobile Learning:Opportunities and Challenges

Design and Development of m-Learning Service Based on 3G Cellular Phones

Shift to Cyber-I: Reexamining Personalized Pervasive Learning

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