The advent of the social web brought with it challenges and opportunities for research on learning and knowledge construction. Using the online-encyclopedia Wikipedia as an example, we discuss several methods that can be applied to analyze the dynamic nature of knowledge-related processes in mass collaboration environments. These methods can help in the analysis of the interactions between the two levels that are relevant in computer-supported collaborative learning (CSCL) research: The individual level of learners and the collective level of the group or community. In line with constructivist theories of learning, we argue that the development of knowledge on both levels is triggered by productive friction, that is, the prolific resolution of socio-cognitive conflicts. By describing three prototypical methods that have been used in previous Wikipedia research, we review how these techniques can be used to examine the dynamics on both levels and analyze how these dynamics can be predicted by the amount of productive friction. We illustrate how these studies make use of text classifiers, social network analysis, and cluster analysis in order to operationalize the theoretical concepts. We conclude by discussing implications for the analysis of dynamic knowledge processes from a learning sciences perspective.

中文翻译：

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使用大数据技术测量大规模协作在线环境中的生产摩擦

社交网络的出现带来了学习和知识建设研究的挑战和机遇。以在线百科全书Wikipedia为例，我们讨论了可用于分析大规模协作环境中与知识相关的过程的动态性质的几种方法。这些方法可以帮助分析与计算机支持的协作学习（CSCL）研究相关的两个层次之间的相互作用：学习者的个人层次和小组或社区的集体层次。与建构主义的学习理论相一致，我们认为在两个层次上知识的发展都是由生产摩擦引起的，即社会认知冲突的大量解决。通过描述在先前的Wikipedia研究中使用的三种原型方法，我们回顾了如何使用这些技术在两个层面上检查动力学并分析如何通过生产摩擦量来预测这些动力学。我们将说明这些研究如何利用文本分类器，社交网络分析和聚类分析来实现理论概念。最后，我们从学习科学的角度讨论了对动态知识过程分析的意义。和聚类分析，以便将理论概念付诸实践。最后，我们从学习科学的角度讨论了对动态知识过程分析的意义。和聚类分析，以便将理论概念付诸实践。最后，我们从学习科学的角度讨论了对动态知识过程分析的意义。