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Selection and Composition of Personalization Parameters in Cloud

Authors: Sameh Ghallabi; Fathi Essalmi; Mohamed Jemni; Kinshuk;

Selection and Composition of Personalization Parameters in Cloud

Abstract

Integrating e-learning personalization systems in cloud computing environment is a relatively recent trend in technology enhanced learning. Cloud computing is a new model that allows users to access applications through Internet. It is characterized by several advantages such as reducing the cost of development, high availability of information, scalability and so on. In literature, several works have taken into consideration these advantages for applying this model in learning systems. However, very little research is available that focuses on selecting and assembling the best personalization parameters according to the teachers' preferences. This paper proposes a solution based on the cloud for the composition of personalization parameters with minimal cost. Through this solution, teachers can easily access these parameters from anywhere and anytime.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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