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Informal Learning in Online Knowledge Communities: Predicting Community Response to Visitor Inquiries

机译：在线知识社区中的非正式学习：预测社区对访客查询的反应

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Informal learning in online knowledge communities (OKCs) comprises visitor inquiries on specific topics. Learning can occur only if the OKC adequately respond. This study aims to predict OKC response, using a social learning analytics approach based on computational linguistics and Bakhtin's theory of dialogism. Observing the blog topic (cooking vs. politics & economics) and the visitor inquiry format (off-topic vs. on-topic), a field experiment with a 2 × 2 factorial design was conducted on a sample of N = 68 blogger communities with a total of 25,303 members. For the entire sample, the community response was influenced only by the inquiry format. In a separate examination of experimental groups, only for one examined topic (cooking) this remained true, while for the other (politics & economics) the community response only depended on the previously established dialog quality. The findings suggest identification criteria for responsive communities, which can support OKC integration in learning environments.
机译：在线知识社区（OKC）中的非正式学习包括针对特定主题的访客查询。只有在OKC做出充分响应的情况下，才能进行学习。这项研究旨在使用基于计算语言学和巴赫金对话理论的社会学习分析方法来预测OKC的响应。观察博客主题（烹饪vs.政治与经济学）和访客询问格式（主题离题与主题问询），对N = 68个博客社区的样本进行了2×2析因设计的现场实验。共有25,303名成员。对于整个样本，社区回应仅受查询格式的影响。在对实验组的单独检查中，仅对一个检查的主题（烹饪）保持不变，而对于其他（政治和经济学），社区的响应仅取决于先前确定的对话质量。研究结果提出了响应社区的识别标准，该标准可以支持OKC在学习环境中的集成。

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