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Validating Empirically a Rating Approach for Quantifying the Quality of Collaboration

机译:经验验证用于量化协作质量的评级方法

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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)研究领域中的跨学科性涉及几种分析方法的应用,这些方法包括对丰富的小型互动事件的深层定性分析,以及用作指标的适当分类的互动事件的定量度量合作在某些方面的成功。本文对CSCL分析采用了另一种方法,其目的是利用这些不同方法学趋势中的每一种的某些期望属性,包括使用评估方案来评估协作质量。在定义了涵盖协作最重要方面的一组维度之后,它使用了经过适当培训的人类评估者,将他们的评估基于协作的重要方面,这些方面不容易形式化。在这里研究的活动涉及228个合作的二元组,它们在解决问题的任务中同步工作。基于这个庞大的数据集,通过使用多维缩放技术统计地制定等级,可以根据经验阐明协作质量维度之间的关系。

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