关键词: 社会网络分析, 在线课堂, 推荐系统, 主题模型

Abstract: The rapid deployment of massive open online courses (MOOCs) has created a surge in the global connectivity among students for educational purposes. With contrast to large scale enrolled students, almost all the courses in MOOCs have a low completion rate. One of the important reasons for those who drop out the course is lack of suitable study buddies to communicate each other. This paper analyzes students’ posted messages and their roles and behaviors in MOOCs discussion forum, and builds students’ behavior feature model and relational networks between students. To estimate student’s relevancy with respected to topic words dictionary which is selected according to course content in advance, this paper proposes a new topic model with fixed topic dictionary, and finally recommends study buddies for student by inferred topic distribution. Experiments on crawled Coursera dataset demonstrate that the topic model can find out those students with more similarity on same topic words, and recommend study buddies. And this will be helpful for students to complete the study process.

Key words: social network analysis, online courses, recommendation system, topic model