2,157 research outputs found

    Learning architectures and negotiation of meaning in European trade unions

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    As networked learning becomes familiar at all levels and in all sectors of education, cross-fertilisation of innovative methods can usefully inform the lifelong learning agenda. Development of the pedagogical architectures and social processes, which afford learning, is a major challenge for educators as they strive to address the varied needs of a wide range of learners. One area in which this challenge is taken very seriously is that of trade unions, where recent large-scale projects have aimed to address many of these issues at a European level. This paper describes one such project, which targeted not only online courses, but also the wider political potential of virtual communities of practice. By analysing findings in relation to Wengers learning architecture, the paper investigates further the relationships between communities of practice and communities of learners in the trade union context. The findings suggest that a focus on these relationships rather than on the technologies that support them should inform future developments

    The STIN in the Tale: A Socio-technical Interaction Perspective on Networked Learning

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    In this paper, we go beyond what have been described as 'mechanistic' accounts of e-learning to explore the complexity of relationships between people and technology as encountered in cases of networked learning. We introduce from the social informatics literature the concept of sociotechnical interaction networks which focus on the interplay between participants, technology, learning artefacts and practices. We apply this concept to case material drawn from transnational trade union education to identify and to analyse three aspects of networked learning: the local sociotechnical networks of learners; the construction of an overarching, global sociotechnical network for learning; and the evolution of such networks over time. Finally we identify issues for further research highlighted by these models

    Fish or Fowl: A Wizard of Oz Evaluation of Dialogue Strategies in the Restaurant Domain

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    Recent work on evaluation of spoken dialogue systems suggests that the information presentation phase of complex dialogues is often the primary contributor to dialogue duration. This indicates that better algorithms are needed for the presentation of complex information in speech. Currently however we lack data about the tasks and dialogue strategies on which to base such algorithms. In this paper, we describe a Wizard of Oz tool and a study which applies user models based on multi-attribute decision theory to the problem of generating tailored and concise system responses for a spoken dialogue system. The resulting Wizard corpus will be distributed by the LDC as part of our work on the ISLE project

    Fitting Linear Mixed-Effects Models using lme4

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    Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.Comment: 51 pages, including R code, and an appendi
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