期刊
2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)
卷 -, 期 -, 页码 936-939出版社
IEEE
DOI: 10.1109/ICMLA.2017.00-34
关键词
Unsupervised Learning; Clustering; K-means Algorithm; Learning Analytics
Massive Open Online Courses (MOOCs) have recently been highly popular among worldwide learners, while it is challenging to manage and interpret the large-scale discussion forum which is the dominant channel of online communication. K-Means clustering, one of the famous unsupervised learning algorithms, could help instructors identify influential users in MOOC forum, to better understand and improve online learning experience. However, traditional K-Means suffers from bias of outliers and risk of falling into local optimum. In this paper, OP-DCI, an optimized K-Means algorithm is proposed, using outlier post-labeling and distant centroid initialization. Outliers are not solely filtered out but extracted as distinct objects for post-labeling, and distant centroid initialization eliminates the risk of falling into local optimum. With OP-DCI, learners in MOOC forum are clustered efficiently with satisfactory interpretation, and instructors can subsequently design personalized learning strategies for different clusters.
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