DocumentCode :
3610734
Title :
Visual Analytics for MOOC Data
Author :
Qu, Huamin ; Chen, Qing
Author_Institution :
Hong Kong University of Science and Technology
Volume :
35
Issue :
6
fYear :
2015
Firstpage :
69
Lastpage :
75
Abstract :
With the rise of massive open online courses (MOOCs), tens of millions of learners can now enroll in more than 1,000 courses via MOOC platforms such as Coursera and edX. As a result, a huge amount of data has been collected. Compared with traditional education records, the data from MOOCs has much finer granularity and also contains new pieces of information. It is the first time in history that such comprehensive data related to learning behavior has become available for analysis. What roles can visual analytics play in this MOOC movement? The authors survey the current practice and argue that MOOCs provide an opportunity for visualization researchers and that visual analytics systems for MOOCs can benefit a range of end users such as course instructors, education researchers, students, university administrators, and MOOC providers.
Keywords :
Cryptography; Data mining; Data visualization; Distance education; Education courses; Online services; Visual analytics; MOOCs; clickstreams; computer graphics; visual analytics; visualization; web log data analysis;
fLanguage :
English
Journal_Title :
Computer Graphics and Applications, IEEE
Publisher :
ieee
ISSN :
0272-1716
Type :
jour
DOI :
10.1109/MCG.2015.137
Filename :
7331178
Link To Document :
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