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ERIC Number: ED560574
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 8
Abstractor: As Provided
Reference Count: 30
Influence Analysis by Heterogeneous Network in MOOC Forums: What Can We Discover?
Jiang, Zhuoxuan; Zhang, Yan; Liu, Chi; Li, Xiaoming
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
With the development of Massive Open Online Courses (MOOC) in recent years, discussion forums there have become one of the most important components for both students and instructors to widely exchange ideas. And actually MOOC forums play the role of social learning media for knowledge propagation. In order to further understand the emerging learning settings, we explore the social relationship there by modeling the forum as a heterogeneous network with theories of social network analysis. We discover a specific group of students, named representative students, who feature large engagement in discussions and large aggregation of the majority of the whole forum participation, except the large learning behavior or the best performance. Based on these discoveries, to answer representative students' threads preferentially could not only save time for instructors to choose target posts from all, but also could propagate the knowledge as widespread as possible. Furthermore if extra attention is paid to representative students in the sight of their behavior, performance and posts, instructors could readily get feedback of the teaching quality, realize the major concerns in forums, and then make measures to improve the teaching program. We also develop a real-time and effective visualization tool to help instructors achieve these. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: International Educational Data Mining Society
Identifiers - Location: China