Members’ Behavior in Virtual Learning Community: A Study Using Data Mining Approach

Xiaokang Li, Yu Nie, Min Chen, Xiaoqing Liu, Xiaolei Liu

Abstract


Purpose: With the development of information technology, online virtual learning community is on its way to become an important approach for people to construction and sharing of knowledge. Researches on virtual learning community are not only important to the establishment and management of virtual learning community itself, but are helpful for people’s quest for the future development of online learning. However, current researches related to the virtual learning community are in inadequacy, and especially the application of quantitative analysis method for research is rarely seen. Using quantitative analysis method of data mining to study members’ behavior in online learning communities. Method: In this article, the discussion data (posts) from five online English virtual learning communities in China are sampled and colleted. These data were processed according to a series of guidelines to obtain proper data documents, and these data documents were opened under Waikato Environment for Knowledge Analysis and then carried out preprocessing. Next, the module of association rule learning in Waikato Environment Knowledge Analysis were used to perform mining on these processed data, and obtained a series of potential behavior rules in these communities. The partial rules have been listed in the article with their meaning analyzed. Findings: The result shows that in this setting it is feasible to apply the association rule learning to virtual learning community. Value: It provides approaches and lays the foundation for future relevant studies.


Full Text: PDF DOI: 10.5539/cis.v7n4

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This work is licensed under a Creative Commons Attribution 3.0 License.

Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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