文章基本信息

标题：Cluster Analysis in Online Learning Communities: A Text Mining Approach
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作者：Evren Eryilmaz ; Brian Thoms ; Zafor Ahmed 等
期刊名称：Communications of the Association for Information Systems
印刷版ISSN：1529-3181
出版年度：2022
卷号：51
页码：1-22
语种：English
出版社：Association for Information Systems
摘要：This paper presents a theory-informed blueprint for mining unstructured text data using mixed- and multi-methods to improve understanding of collaboration in asynchronous online discussions (AOD). Grounded in a community of inquiry theoretical framework to systematically combine established research techniques, we investigated how AOD topics and individual reflections on those topics affect formation of clusters or groups in a community. The data for the investigation came from 54 participants and 470 messages. Data analysis combined the analytical efficiency and scalability of topic modeling, social network analysis, and cluster analysis with qualitative content analysis. The cluster analysis found three clusters and that members of the intermediate cluster (i.e., middle of three clusters) played a pivotal role in this community by expressing uncertainty statements, which facilitated a collective sense-making process to resolve misunderstandings. Furthermore, we found that participants’ selected discussion topics and how they discussed those topics influenced cluster formations. Theoretical, practical, and methodological implications are discussed in depth.
关键词：Community of Inquiry;Computer-Mediated Communication;Cluster Analysis;Qualitative Analysis;SocialNetwork Analysis;Text Mining;Topic Modeling