Abstract—With the rapid development of social economy and
the Internet, the network education is becoming a way of
teaching which has a wide application range and covering larger
area. Virtual learning community (VLC) is a combination of
computer technology, psychology, pedagogy and other
multi-disciplinary research field and actually a new model of
network education. However, the teaching data of VLC are
often disorderly, fragmentary, mixed and its value is also not
easy to detect. The using of data mining technology will solve
this kind of problems and bring many unexpected benefits
support the teaching of the VLC. This paper reports on the
analysis of learning behavior of the VLC and how to extract the
feature vector of learning. The fuzzy c-means clustering
algorithm is applied to analyze the learning behavior and divide
the students of the VLC by the feature of them. Then some
targeted teaching guidance can be made for each group. This
kind of grouping strategy is to be found feasible and achieved
good effect by simulation experiment.
Index Terms—Fuzzy c-means clustering algorithm，learning
characteristics, virtual learning community(VLC).
Yan Cheng is with Tongji University. She is also with Jiangxi Normal
University, Nanchang, Jiangxi, China (e-mail: chyan88888@jxnu.edu.cn).
Jian Hua Xie and Zhi Ming Yang are with Jiangxi Normal University,
Nanchang, Jiangxi, China (e-mail: 971383331@qq.com,
709830862@qq.com).
Cite: Yan Cheng, Jian Hua Xie, and Zhi Ming Yang, "The Clustering Analysis Method of the Learning Characteristics Based on the Virtual Learning Community," International Journal of Information and Education Technology vol. 7, no. 1, pp. 66-70, 2017.