The Knowledge Life Cycle for e-learning

In this paper, we examine the semantic aspects of e-learning from both pedagogical and technological points of view. We suggest that if semantics are to fulfil their potential in the learning domain then a paradigm shift in perspective is necessary, from information-based content delivery to knowledge-based collaborative learning services. We propose a semantics driven Knowledge Life Cycle that characterises the key phases in managing semantics and knowledge, show how this can be applied to the learning domain and demonstrate the value of semantics via an example of knowledge reuse in learning assessment management

Evaluation Report of Prospero’s Island: an Immersive Approach to Literacy at Key Stage 3.

Prospero's Island is an immersive theatre project created by Punchdrunk Enrichment and sponsored by Learning Partner, London Borough of Hackney (Hackney Learning Trust). The project sought to inspire and motivate students’ engagement with the English curriculum, and to develop an immersive approach to teaching literacy that would improve students’ learning. Prospero’s Island took place in a secondary academy in Hackney, London over two school terms (autumn 2014-spring 2015). The project was embedded in existing schemes of work, and included the following elements: • An immersive theatre installation for Year 7-8 students (aged 11-13 years); this took the form of an interactive game based on The Tempest; over a two-week period groups of students participated in this experience for a morning or afternoon (autumn term); • A Teaching and Learning Day (TALD) and eight twilight CPD sessions on immersive learning techniques for school staff and teachers across London (autumn term); • A return to the installation for one lesson, led by English teachers (autumn term); • Follow-on work by teachers to develop immersive learning in English lessons (spring term); • An independent evaluation of the project (autumn and spring terms)

Learning Object Categories From Internet Image Searches

In this paper, we describe a simple approach to learning models of visual object categories from images gathered from Internet image search engines. The images for a given keyword are typically highly variable, with a large fraction being unrelated to the query term, and thus pose a challenging environment from which to learn. By training our models directly from Internet images, we remove the need to laboriously compile training data sets, required by most other recognition approaches-this opens up the possibility of learning object category models “on-the-fly.” We describe two simple approaches, derived from the probabilistic latent semantic analysis (pLSA) technique for text document analysis, that can be used to automatically learn object models from these data. We show two applications of the learned model: first, to rerank the images returned by the search engine, thus improving the quality of the search engine; and second, to recognize objects in other image data sets

Adding generic contextual capabilities to wearable computers

Context-awareness has an increasingly important role to play in the development of wearable computing systems. In order to better define this role we have identified four generic contextual capabilities: sensing, adaptation, resource discovery, and augmentation. A prototype application has been constructed to explore how some of these capabilities could be deployed in a wearable system designed to aid an ecologist's observations of giraffe in a Kenyan game reserve. However, despite the benefits of context-awareness demonstrated in this prototype, widespread innovation of these capabilities is currently stifled by the difficulty in obtaining the contextual data. To remedy this situation the Contextual Information Service (CIS) is introduced. Installed on the user's wearable computer, the CIS provides a common point of access for clients to obtain, manipulate and model contextual information independently of the underlying plethora of data formats and sensor interface mechanisms