78,202 research outputs found
Ubiquitous computing and knowledge management
MOBIlearn is a large European research project to develop a mobile learning system to facilitate formal, non formal and informal learning. The project has two primary objectives: • Develop a methodology for creating mobile learning scenarios and producing learning objects to implement them. • Develop the technology to deliver the learning objects to users via mobile computing devices. This paper will concentrate the MOBIlearn health care domain. One of this applications main objectives is managing and sharing of tacit knowledge. Using the system participants discuss case studies
and alternative approaches to specific problems are evaluated and documented. This is then used and extended in future case studies. In a mobile learning environment, individual health workers can use the system to either advanced their skills, or in a ‘live’ incident, use it for reference and indeed call for backup.</p
Metadata and ontologies for organizing students’ memories and learning: standards and convergence models for context awareness
Este artÃculo trata de las ontologÃas que sirven para la comprensión en contexto y la Gestión de la Información Personal (PIM)y su aplicabilidad al proyecto Memex Metadata(M2). M2 es un proyecto de investigación de la Universidad de Carolina del Norte en Chapel Hill para mejorar la memoria digital de los alumnos utilizando tablet PC, la tecnologÃa SenseCam de Microsoft y otras tecnologÃas móviles(p.ej. un dispositivo de GPS) para capturar el contexto del aprendizaje. Este artÃculo presenta el proyecto M2, dicute el concepto de los portafolios digitales en las actuales tendencias educativas, relacionándolos con las tecnologÃas emergentes, revisa las ontologÃas relevantes y su relación con el proyecto CAF (Context Awareness Framework), y concluye identificando las lÃneas de investigación futuras.This paper focuses on ontologies supporting context awareness and Personal Information Management (PIM) and their
applicability in Memex Metadata (M2) project. M2 is a research project of the University of North Carolina at Chapel Hill to
improve student digital memories using the tablet PC, Microsoft’s SenseCam technology, and other mobile technologies (e.g.,
a GPS device) to capture context. The M2 project offers new opportunities studying students’ learning with digital
technologies. This paper introduces the M2 project; discusses E-portfolios and current educational trends related to pervasive
computing; reviews relevant ontologies and their relationship to the projects’ CAF (context awareness framework), and
concludes by identifying future research directions
A self-regulated learning approach : a mobile context-aware and adaptive learning schedule (mCALS) tool
Self-regulated students are able to create and maximize opportunities they have for studying or learning. We combine this learning approach with our Mobile Context-aware and Adaptive Learning Schedule (mCALS) tool which will create and enhance opportunities for students to study or learn in different locations. The learning schedule is used for two purposes, a) to help students organize their work and facilitate time management, and b) for capturing the users’ activities which can be retrieved and translated as learning contexts later by our tool. These contexts are then used as a basis for selecting appropriate learning materials for the students. Using a learning schedule to capture and retrieve contexts is a novel approach in the context-awareness mobile learning field. In this paper, we present the conceptual model and preliminary architecture of our mCALS tool, as well as our research questions and methodology for evaluating it. The learning materials we intend to use for our tool will be Java for novice programmers. We decided that this would be appropriate because large amounts of time and motivation are necessary to learn an object-oriented programming language such as Java, and we are currently seeking ways to facilitate this for novice programmers
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
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