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Validating Empirically a Rating Approach for Quantifying the Quality of Collaboration

机译:验证定量合作质量的评级方法

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Interdisciplinarity in the Computer Supported Collaborative Learning (CSCL) research field involves the application of several methodological approaches towards analysis that range from deep-level qualitative analyses of small interaction-rich episodes of collaboration, to quantitative measures of suitably categorized events of interaction used as indicators of the success of collaboration in some of its facets. This article adopts an alternative approach to CSCL analysis that aims at taking advantage of some desired properties of each of these diverse methodological trends, involving the use of a rating scheme for the assessment of collaboration quality. After defining a set of dimensions that cover the most important aspects of collaboration, it employs appropriately trained human raters basing their assessments on substantial aspects of collaboration that are not easily formalisable. The activities studied here regard 228 collaborating dyads, working synchronously on a problem-solving task. Based on this large dataset, relations between dimensions of collaboration quality are unraveled on empirical grounds, by elaborating ratings statistically using a multidimensional scaling technique.
机译:计算机支持的协作学习(CSCL)研究领域涉及应用几种方法论方法,从而从小互动的合作剧集的深度定性分析的范围,以适当分类的互动事件用作指标的定量测量一些方面的合作成功。本文采用替代方法来分析,旨在利用这些不同方法趋势中的每一个的一些所需特性,涉及使用评级方案进行协作质量的评估。在定义一组涵盖合作方面的一组尺寸后,它采用适当培训的人类评估者基于实质性的合作方面的评估,这些协作不可携带。这里研究的活动在于228个协作二元,同步地在解决问题的任务上工作。基于该大型数据集,通过使用多维缩放技术在统计上统计地阐述额定值,通过在统计上阐述额定值来解开协作质量维度之间的关系。

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