Open Access

ARTICLE

A Multi-Feature Weighting Based K-Means Algorithm for MOOC Learner Classification

by Yuqing Yang¹, Dequn Zhou¹, Xiaojiang Yang¹

1 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China.
2 Office of International Cooperation and Exchanges, Nanjing University of Finance & Economics, Nanjing, 210046, China.
3 Jiangsu Guidgine Educational Evaluation Inc., Nanjing, 210046, China.
4 International Education Office of Centennial College, Toronto, P.O. Box 631, Canada.

* Corresponding Author: Dequn Zhou. Email: .

Computers, Materials & Continua 2019, 59(2), 625-633. https://doi.org/10.32604/cmc.2019.05246

Abstract

Massive open online courses (MOOC) have recently gained worldwide attention in the field of education. The manner of MOOC provides a new option for learning various kinds of knowledge. A mass of data miming algorithms have been proposed to analyze the learner’s characteristics and classify the learners into different groups. However, most current algorithms mainly focus on the final grade of the learners, which may result in an improper classification. To overcome the shortages of the existing algorithms, a novel multi-feature weighting based K-means (MFWK-means) algorithm is proposed in this paper. Correlations between the widely used feature grade and other features are first investigated, and then the learners are classified based on their grades and weighted features with the proposed MFWK-means algorithm. Experimental results with the Canvas Network Person-Course (CNPC) dataset demonstrate the effectiveness of our method. Moreover, a comparison between the new MFWK-means and the traditional K-means clustering algorithm is implemented to show the superiority of the proposed method.

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Keywords

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