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