Formal Specification of CA-UCON model using CCA

A Context-Aware Usage CONtrol (CAUCON) model is an extension of the traditional UCON model which enables adaptation to environmental changes in the aim of preserving continuity of usage in a pervasive computing system. When the authorisations and obligations requirements are met by the subject and the object, and the conditions requirements fail due to changes in the environment or the system context, CAUCON model triggers specific actions to adapt to the new situation. Besides the data protection, CA-UCON model so enhances the quality of services, striving to keep explicit interactions with the user at a minimum. This paper proposes a formal specification of the CA-UCON model in the Calculus of Context-aware Ambients (CCA in short). This enables formal analysis of the CA-UCON model using the execution environment of CCA. For illustration, some properties of the CA-UCON model are validated for a ubiquitous learning system

A task-driven design model for collaborative AmI systems

Proceedings of the CAISE*06 Workshop on Ubiquitous Mobile Information and Collaboration Systems UMICS '06. Luxemburg, June 5-9, 2006.The proceedings of this workshop also appeared in printed version In T. Latour and M. Petit (eds), Proceedings of Workshops and Doctoral Consortium, The 18th International Conference on Advanced Information Systems Engineering - Trusted Information Systems (CAiSE'06), June 5-9, 2006, Presses Universitaires de Namur, 2006, ISBN 2-87037-525.Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Ambient intelligence (AmI) is a promising paradigm for humancentred interaction based on mobile and context-aware computing, natural interfaces and collaborative work. AMENITIES (a conceptual and methodological framework based on task-based models) has been specially devised for collaborative systems and is the starting point for a new design proposal for application to AmI systems. This paper proposes a task-based model for designing collaborative AmI systems, which attempts to gather the computational representation of the concepts involved (tasks, laws, etc.) and the relationships between them in order to develop a complete functional environment in relation with the features of AmI systems (collaborative, context-aware, dynamic, proactive, etc.). The research has been applied to an e-learning environment and is implemented using a blackboard model.This research is partially supported by a Spanish R&D Project TIN2004-03140, Ubiquitous Collaborative Adaptive Training (U-CAT)

Location-Based Learning Management System for Adaptive Mobile Learning

E-learning and distance learning are all forms of learning that take place outside of a traditional learning environment and can be alternatives for learners who are not able to study in a traditional environment for various reasons. With advancement in technologies and increased use of smart phone, mobile learning has gained popularity as another form of learning and has enabled learners to learn anywhere and anytime. Ubiquitous learning takes mobile learning to another level by providing contents that are context and location aware. There is therefore the need to provide mobile devices with the right learning contents for the right users. The right learning contents should be adaptive to the learner’s location, as well as learning style and device etc. To be able to implement the learning, learning management systems play the important role in creating, managing, and delivering the learning contents. In this paper, a location-based Learning Management System for adaptive and personalized mobile learning is presented. The systems makes use of 5R Adaptation Framework for Location based Mobile learning, the location-based dynamic grouping algorithm, and concepts of the IMS Learning Design model to produce a location-based adaptive mobile learning setting

A Model of Personalized Context Aware E-learning Based on Psychological Experience

The use of context-aware approach in e-learning system has brought a new passion for users as an alternative to learning. It can provide personalized and adaptive learning patterns that can tailor to the needs, the circumstances and the behavior of users. Along with the continued development pervasive and ubiquitous computing, there are several studies related to this model. However, the existing models developed still focus on a wide variety of contexts, such as explicit contexts and context related to the physical environment of learning. Additionally, the developments of the current models by involving psychological condition of the learner taken into account are still limited. In fact, this condition can influence the learning engagement of learners. This research proposed a model of context aware e-learning that personalizing e-learning according to psychological state of the learners. The psychological experience is based on the theory of flow consisting of anxiety, boredom, and optimal condition measured naturally when users are interacting with e-learning. Furthermore, it becomes one of the strengths of this research. Psychological experiences are measured after the learner interacting with the e-learning. The data are obtained from learner behavior saved in the server log