RAP - a basic context awareness model

The content granularity and context information are two important factors to the efficiency and reusability of learning objects. The context information is necessary to facilitate the discovery and reuse of learning objects stored in global and/or local repositories. However, traditional learning objects are generally not conceived to incorporate with enough context information. Users have to do some extension of the description item set to fit their special use. In this paper, in order to deal with the issue mentioned above, we firstly introduce a context-rich paradigm, the related service driven tagging strategy, and a context model of learning objects. We further explain how to use the context information to realize the adaptive granularity of the content object. Finally, we show a simple concept model for online authoring systems that support the evolution from resource objects to learning objects.

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“…With the reference to the SCORM's CAM [15], we give the learning object description model, shown in Fig. 6.…”

Section: A the Concept Model Of Learning Object And Resource Objectmentioning

confidence: 99%

Putting adaptive granularity and rich context into learning objects

Man

Jin

2010

2010 9th International Conference on Information Technology Based Higher Education and Training (ITHET)

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“…The main drawback of the above presented approaches is the lack of semantic information encapsulated in the context model representation which makes the process of inferring new context related knowledge extremely difficult. In the current work we try to overcome these deficiencies by using our RAP context model [9] to represent the context information. The RAP context model ontology based representation is used by the context aware applications to infer new context related information using reasoning and learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…The CMAA agent calculates the context variation generated by changes of resource properties' values ∆ RPV as presented in (9). If val t+1 -val t =0 then the property value hasn't changed from t to t+1 and that property is ignored when the variation is calculated.…”

Section: The Closed Environment Context Variationmentioning

confidence: 99%

“…To represent a closed environment context information in a programmatic manner we have used the RAP context model [9]. In RAP, the context is defined as a triple: C = <R, A, P >, where R is the set of context resources that generates and/or processes context information, A is the set of actors which interact with context resources in order to satisfy their needs and P is the set of real world context related policies.…”

Section: The Context Model Layermentioning

confidence: 99%

“…CMAA identifies this variation and generates (i) a new context-actor instance and (ii) a new specific context model instance. In order to evaluate the context variation generated by actors' mobility we use the isotropic context space concept, defined in [9]. A context space is isotropic if and only if the set of real world context resources is invariant to the actors' movement.…”

Section: The Closed Environment Context Variationmentioning

confidence: 99%

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Middleware for Smart Environments Management

Anghel

Cioara

Salomie

et al. 2010

INT J COMPUT COMMUN

Self Cite

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This paper introduces a self-configuring middleware that manages the processes of context information acquisition and representation from smart closed environments, targeting the development of context aware applications. The environment context information is modeled using three sets: context resources, context actors and context policies. The context model artifacts are generated and administrated at run time by a management infrastructure based on intelligent software agents. The self-configuring property is enforced by monitoring the closed environment in order to detect variations or conditions for which the context model artifacts must be updated. The middleware was tested and validated within the premises of our Distributed Systems Research Laboratory smart environment.

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Shi

Han

et al. 2009

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RAP - a basic context awareness model

Cited by 12 publications

References 7 publications

Putting adaptive granularity and rich context into learning objects

Putting adaptive granularity and rich context into learning objects

Middleware for Smart Environments Management

A User Behavior Perception Model Based on Markov Process

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