239,651 research outputs found

    SLIS Student Research Journal, Vol. 6, Iss. 1

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    How to promote informal learning in the workplace? The need for incremental design methods

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    Informal Learning in the Workplace (ILW) is ensured by the everyday work activities in which workers are engaged. It accounts for over 75 per cent of learning in the workplace. Enterprise Social Media (ESM) are increasingly used as informal learning environments. According to the results of an implementation we have conducted in real context, we show that ESM are appropriate to promote ILW. Nevertheless, social aspects must be reconsidered to address users' needs regarding content and access, quality information indicators, moderation and control

    An Open System for Social Computation

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    Part of the power of social computation comes from using the collective intelligence of humans to tame the aggregate uncertainty of (otherwise) low veracity data obtained from human and automated sources. We have witnessed a surge in development of social computing systems but, ironically, there have been few attempts to generalise across this activity so that creation of the underlying mechanisms themselves can be made more social. We describe a method for achieving this by standardising patterns of social computation via lightweight formal specifications (we call these social artifacts) that can be connected to existing internet architectures via a single model of computation. Upon this framework we build a mechanism for extracting provenance meta-data across social computations

    An Open System for Social Computation

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    Part of the power of social computation comes from using the collective intelligence of humans to tame the aggregate uncertainty of (otherwise) low veracity data obtained from human and automated sources. We have witnessed a surge in development of social computing systems but, ironically, there have been few attempts to generalise across this activity so that creation of the underlying mechanisms themselves can be made more social. We describe a method for achieving this by standardising patterns of social computation via lightweight formal specifications (we call these social artifacts) that can be connected to existing internet architectures via a single model of computation. Upon this framework we build a mechanism for extracting provenance meta-data across social computations
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