33 research outputs found

    Seeking togetherness: moving toward a comparative evaluation framework in an interdisciplinary DIY networking project

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    There is renewed interest in community networks as a mechanism for local neighbourhoods to find their voice and maintain local ownership of knowledge. In a post-Snowden, big data, age of austerity there is both widespread questioning of what happens to public generated data shared over ‘free’ services such as Facebook, and also a renewed focus on self-provisioning where there are gaps in digital service provision. In this paper we introduce an EU funded collaborative project (‘MAZI’) that is exploring how Do-It-Yourself approaches to building community networks might foster social cohesion, knowledge sharing and sustainable living through four pilots across Europe. A key challenge is to develop a shared evaluation approach that will allow us to make sense of what we are learning across highly diverse local situations and disciplinary approaches. In this paper we describe our initial approaches and the challenges we face

    Challenges in context-aware mobile language learning: the MASELTOV approach

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    Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment

    Artificial Intelligence in Education

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    Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies

    Ownership & influence

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    Available from British Library Document Supply Centre- DSC:q95/19807 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

    Deeper share ownership

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    SIGLEAvailable from British Library Document Supply Centre- DSC:8310.951(SMF-P--12) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

    Closing the communications gap Disclosure and institutional shareholders

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    SIGLEAvailable from British Library Document Supply Centre-DSC:98/06330 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
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