Socio-semantic Network Analysis of Knowledge-Creation Discourse on a Real-Time Scale

Abstract

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.