A semantic web services-based infrastructure for context-adaptive process support

Current technologies aimed at supporting processes whether it is a business or learning process - primarily follow a metadata- and data-centric paradigm. Whereas process metadata is usually based on a specific standard specification - such as the Business Process Modeling Notation (BPMN) or the IMS Learning Design Standard - the allocation of resources is done manually at design-time, and the used data is often specific to one process context only. These facts limit the reusability of process models across different standards and contexts. To overcome these issues, we introduce an innovative Semantic Web Service-based framework aimed at changing the current paradigm to a context-adaptive service-oriented approach. Following the idea of layered semantic abstractions, our approach supports the development of abstract semantic process model - reusable across different contexts and standards - that enables a dynamic adaptation to specific actor needs and objectives. To illustrate the application of our framework and establish its feasibility, we describe a prototypical application in the E-Learning domain

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

SOA services in higher education

Service Oriented Architecture (SOA) is a recent architectural framework for distributed software system development in which software components are packaged as Services. It has become increasingly popular in academia and in industry, but has been principally used in the business domain. However, in higher education, SOA has rarely been applied or investigated. In this paper, we propose the idea of applying SOA technologies in the education domain, to increase both interoperability and flexibility within the e-learning environment. We expect that both students and teachers in higher educational institutions can benefit from this approach. We also describe a number of possible SOA services, along with a high level service roadmap to support a university's learning and teaching activities

Panning for gold: designing pedagogically-inspired learning nuggets

Tools to support teachers and learning technologists in the creation of effective learning designs are currently in their infancy. This paper describes a metadata model, devised to assist in the conception and design of new learning activities, that has been developed, used and evaluated over a period of three years. The online tool that embodies this model was not originally intended to produce runtime executable code such as IMS-LD, but rather focussed on assisting teachers in the thought processes involved in selecting appropriate methods, tools, student activities and assessments to suit the required learning objectives. Subsequently, we have modified the RELOAD editor such that the output from our tool can be translated into IMS-LD. The contribution of this paper is the comparison of our data model with that of IMS-LD, and the analysis of how each can inform the other

Creating awareness of kinaesthetic learning using the Experience API: current practices, emerging challenges, possible solutions

We describe our use of the Experience API in preparing blue-collar workers for three frequently arising work contexts, including, for example, the requirement to perform maintenance tasks exactly as specified, consistently, quickly, and without error. We provide some theoretical underpinning for modifying and updating the API to remain useful in near-future training scenarios, such as having a shorter time allowed for kinaesthetic learning experiences than in traditional apprenticeships or training. We propose ways to involve a wide range of stakeholders in appraising the API and ensuring that any enhancements to it, or add-ons, are useful, feasible and compatible with current TEL practices and tools, such as learning-design modelling languages

Is countershading camouflage robust to lighting change due to weather?

Countershading is a pattern of coloration thought to have evolved in order to implement camouflage. By adopting a pattern of coloration that makes the surface facing towards the sun darker and the surface facing away from the sun lighter, the overall amount of light reflected off an animal can be made more uniformly bright. Countershading could hence contribute to visual camouflage by increasing background matching or reducing cues to shape. However, the usefulness of countershading is constrained by a particular pattern delivering ‘optimal’ camouflage only for very specific lighting conditions. In this study, we test the robustness of countershading camouflage to lighting change due to weather, using human participants as a ‘generic’ predator. In a simulated three-dimensional environment, we constructed an array of simple leaf-shaped items and a single ellipsoidal target ‘prey’. We set these items in two light environments: strongly directional ‘sunny’ and more diffuse ‘cloudy’. The target object was given the optimal pattern of countershading for one of these two environment types or displayed a uniform pattern. By measuring detection time and accuracy, we explored whether and how target detection depended on the match between the pattern of coloration on the target object and scene lighting. Detection times were longest when the countershading was appropriate to the illumination; incorrectly camouflaged targets were detected with a similar pattern of speed and accuracy to uniformly coloured targets. We conclude that structural changes in light environment, such as caused by differences in weather, do change the effectiveness of countershading camouflage

An artefact repository to support distributed software engineering

The Open Source Component Artefact Repository (OSCAR) system is a component of the GENESIS platform designed to non-invasively inter-operate with work-flow management systems, development tools and existing repository systems to support a distributed software engineering team working collaboratively. Every artefact possesses a collection of associated meta-data, both standard and domain-specific presented as an XML document. Within OSCAR, artefacts are made aware of changes to related artefacts using notifications, allowing them to modify their own meta-data actively in contrast to other software repositories where users must perform all and any modifications, however trivial. This recording of events, including user interactions provides a complete picture of an artefact's life from creation to (eventual) retirement with the intention of supporting collaboration both amongst the members of the software engineering team and agents acting on their behalf

Towards a competency model for adaptive assessment to support lifelong learning

Adaptive assessment provides efficient and personalised routes to establishing the proficiencies of learners. We can envisage a future in which learners are able to maintain and expose their competency profile to multiple services, throughout their life, which will use the competency information in the model to personalise assessment. Current competency standards tend to over simplify the representation of competency and the knowledge domain. This paper presents a competency model for evaluating learned capability by considering achieved competencies to support adaptive assessment for lifelong learning. This model provides a multidimensional view of competencies and provides for interoperability between systems as the learner progresses through life. The proposed competency model is being developed and implemented in the JISC-funded Placement Learning and Assessment Toolkit (mPLAT) project at the University of Southampton. This project which takes a Service-Oriented approach will contribute to the JISC community by adding mobile assessment tools to the E-framework