Socio-semantic Network Analysis of Knowledge-Creation Discourse on a Real-Time Scale
Socio-semantic Network Analysis of Knowledge-Creation Discourse on a Real-Time Scale
复制标题
实时知识创造话语的社会语义网络分析
DOI：
10.1007/978-3-030-67788-6_12
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Oshima Jun
中科院分区：
文献类型：
--
作者：
Ohsaki Ayano;Oshima Jun
本研究探讨了时间分析方法与数据上收集的实时规模来调查学习知识创造。在协作学习过程中，协作地建立想法对学习者来说是重要的。因此，合作学习策略必须考虑学习者语篇的时间性。因此，本研究采用实时收集的数据和两种分析方法：社会语义网络分析（SSNA）和深度对话话语分析相结合。特别地，作者在这项研究中使用了SSNA结合移动节窗口方法和网络生命周期。本研究的目的是检验分析具有时间戳信息的数据的可能性。为了实现这一目标，作者通过使用以下步骤分析相同的数据集进行了比较研究。首先，作者将从协作学习中收集的数据可视化。其次，作者对有序数据和具有时间戳信息的数据的分析结果进行了比较。第三，利用SSNA结合移动节窗法和网络生命周期对带有时间戳信息的数据进行分析，发现关键点，并对话语数据进行了深入分析。本研究的第一个发现是，所提出的分析方法可以有效地代表的过程中的想法改进。第二个发现是，使用时间戳信息的分析是有效的评估每个组之间的相似性和差异。这项研究表明了时间分析和分析实时收集的数据的有效性。
This study discusses the temporal analysis method with data on collected on a real-time scale to investigate learning as knowledge-creation. During collaborative learning, collaboratively building ideas is important for learners. Thus, collaborative learning strategies must consider learners’ discourses temporally. Therefore, this study used data collected in real-time and two analysis methods: the combination of socio-semantic network analysis (SSNA) and in-depth dialogical discourse analysis. Especially, the authors used the SSNA combined with the moving stanza window method and the network lifetime in this study. The goal of this study was to examine the possibility of analyzing data with timestamp information. For this goal, the authors conducted a comparative study by analyzing the same dataset using the following steps. First, the authors visualized the data gathered from collaborative learning. Second, the authors conducted a comparison between the analyzed results of the ordered data and of data with timestamp information. Third, the authors detected the pivotal points from the results of analyzing data with timestamp information using the SSNA combined with the moving stanza window method and the network lifetime, and the discourse data was analyzed in great depth. The first finding of this study is that the proposed analysis method can effectively represent the process of ideas improvement. The second finding is that analysis using timestamp information is effective for assessing the similarities and differences between each group. This study suggests the effectiveness of temporal analysis and analyzing data gathered in real-time.