@inproceedings{bcde4107c6484a6cbb18c84fba1e3ed1,
title = "OP-DCI: A riskless K-means clustering for influential user identification in MOOC forum",
abstract = "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.",
keywords = "Clustering, K Means Algorithm, Learning Analytics, Unsupervised Learning",
author = "Xiangyu Hou and Lei, \{Chi Un\} and Kwok, \{Yu Kwong\}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 ; Conference date: 18-12-2017 Through 21-12-2017",
year = "2017",
doi = "10.1109/ICMLA.2017.00-34",
language = "English",
series = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
pages = "936--939",
editor = "Xuewen Chen and Bo Luo and Feng Luo and Vasile Palade and Wani, \{M. Arif\}",
booktitle = "Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017",
}