In Search of Conversational Grain Size: Modeling Semantic Structure Using Moving Stanza Windows
In Search of Conversational Grain Size: Modeling Semantic Structure Using Moving Stanza Windows
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寻找会话粒度：使用移动节窗口建模语义结构
DOI：
10.18608/jla.2017.43.7
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
D. Shaffer
中科院分区：
文献类型：
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作者：
Amanda Siebert;Golnaz Arastoopour;Wesley Collier;Z. Swiecki;A. Ruis;D. Shaffer
基于学生话语的学习分析不仅需要考虑话语的内容，还需要考虑学生在谈话中建立联系的方式。这需要对话语数据进行分割，以确定何时连接可能是有意义的。在本文中，我们提出了一种方法来分割数据建模的连接在话语中使用认知网络分析的目的。具体来说，我们使用认知网络分析模型连接在学生话语中使用的时间分割方法改编自最近的工作在学习科学。我们比较了这项研究的结果，一个纯粹的基于会话的分割方法，以检查的启示，时间分割建模连接的话语。
Analyses of learning based on student discourse need to account not only for the content of the utterances but also for the ways in which students make connections across turns of talk. This requires segmentation of discourse data to define when connections are likely to be meaningful. In this paper, we present an approach to segmenting data for the purposes of modeling connections in discourse using epistemic network analysis. Specifically, we use epistemic network analysis to model connections in student discourse using a temporal segmentation method adapted from recent work in the learning sciences. We compare the results of this study to a purely conversation-based segmentation method to examine the affordances of temporal segmentation for modeling connections in discourse.