A Context-Driven Content Adaptation Planner for Improving Mobile Internet Accessibility

Yang, Stephen J. H.; Zhang, J.; Huang, Angus F. M.; Tsai, Jeffrey J. P.; Yu, Pengfei

doi:10.1109/icws.2008.31

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…This predefined score tree with limited content versions may not meet the user's needs and may constrain the flexibility and extensibility of the system. Yang et al (2008) took the learner's environmental context into account, proposing a context-driven content adaptation planner. This planner can determine a proper version of content created by static or dynamic adaptation, based on the predefined context profile to meet a learner's context status (e.g., brighten the background light in the outdoor environment).…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, the dynamic adaptation approach can apply content structure analysis (Buyukkokten et al 2001(Buyukkokten et al , 2002González-Castaño et al 2002;Chen et al 2003;Yin and Lee 2004;Ramaswamy et al 2005;Laakko and Hiltunen 2005;Yang et al 2007b;He et al 2007;Kim et al 2008;Hsiao et al 2008) to dynamically transcode, rearrange the layout, and distillate the content to improve the delivery latency and meet the devices' capabilities. Alternatively, it can apply context-based adaptation (Lum and Lau 2003;Mohomed et al 2004Mohomed et al , 2006aMohomed et al ,b, 2007Lee et al 2006;Yang et al 2008) to consider both the environmental context and the preference of the requesting user to offer more precisely adapted content.…”

Section: Introductionmentioning

confidence: 98%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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

View full text Add to dashboard Cite

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“…This is especially important for the case of mobile [13] clients, where the operating environment of the client can change over time. The research in this paper does not directly address context-awareness and adaptivity because we only consider the client's platform and not temporally sensitive information such as location.…”

Section: Context-aware and Adaptive Servicesmentioning

confidence: 99%

Web Service Mashup Middleware with Partitioning of XML Pipelines

Wohlstadter

Cannon

2009

2009 IEEE International Conference on Web Services

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Traditionally, the composition of Web services to create mashups has been achieved by using an application server as a mediator between a client browser and services. To avoid this bottleneck, mashups are sometimes implemented so that Web service composition takes place directly from the end user's browser. Creating such implementations is difficult because developers must manage the separation of software into various distributable pieces, in different languages, and coordinate their communication. In this paper we describe a middleware for managing Web service mashups in a disciplined, and flexible way. We build upon the established abstraction of XML pipelines, but describe a new approach for selectively partitioning pipeline components between a browser client and application server. We provide a performance evaluation for a common mashup application scenario.

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