101,218 research outputs found
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Designing for change: mash-up personal learning environments
Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down.
The learning environment is an (if not 'theĂŻÂżÂœ) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed.
In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services.
Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a â we think â comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems
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Service quality measurement in the internet context: A proposed model
The survival of any organisation in a highly competitive environment depends on its ability to provide the best service quality to its existing customers as the quality of service is a key factor in the success of any organisation. It is well established that the measurement of service quality is an important procedure for the improvement of the success and performance of any organisation. Facts indicate that more attention is needed toward developing an industry-specific scale for measuring customer service quality within the still-developing sector of Internet-based self-service technologies. The main objectives of this research paper are two-fold; firstly, to review comprehensively previous and contemporary literature on service quality measurement and to discuss the key issues on the development of an industry-specific scale for measuring customer service quality in the specific context of Internet-based self-service technologies, secondly, to propose a conceptual model for service quality perceptions of Internet-based self-service technologies through identifying its key antecedents and consequences. The findings of this study will be significant for both scholars and practitioners in this area as it provides a deep understanding of the way customers evaluate services provided via self-service technologies
Component-aware Orchestration of Cloud-based Enterprise Applications, from TOSCA to Docker and Kubernetes
Enterprise IT is currently facing the challenge of coordinating the
management of complex, multi-component applications across heterogeneous cloud
platforms. Containers and container orchestrators provide a valuable solution
to deploy multi-component applications over cloud platforms, by coupling the
lifecycle of each application component to that of its hosting container. We
hereby propose a solution for going beyond such a coupling, based on the OASIS
standard TOSCA and on Docker. We indeed propose a novel approach for deploying
multi-component applications on top of existing container orchestrators, which
allows to manage each component independently from the container used to run
it. We also present prototype tools implementing our approach, and we show how
we effectively exploited them to carry out a concrete case study
Observation Centric Sensor Data Model
Management of sensor data requires metadata to understand the semantics of observations. While e-science researchers have high demands on metadata, they are selective in entering metadata. The claim in this paper is to focus on the essentials, i.e., the actual observations being described by location, time, owner, instrument, and measurement. The applicability of this approach is demonstrated in two very different case studies
Ontology-based collaborative framework for disaster recovery scenarios
This paper aims at designing of adaptive framework for supporting
collaborative work of different actors in public safety and disaster recovery
missions. In such scenarios, firemen and robots interact to each other to reach
a common goal; firemen team is equipped with smart devices and robots team is
supplied with communication technologies, and should carry on specific tasks.
Here, reliable connection is mandatory to ensure the interaction between
actors. But wireless access network and communication resources are vulnerable
in the event of a sudden unexpected change in the environment. Also, the
continuous change in the mission requirements such as inclusion/exclusion of
new actor, changing the actor's priority and the limitations of smart devices
need to be monitored. To perform dynamically in such case, the presented
framework is based on a generic multi-level modeling approach that ensures
adaptation handled by semantic modeling. Automated self-configuration is driven
by rule-based reconfiguration policies through ontology
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