Visual analytics techniques are widely utilized to facilitate the exploration of online educational data.
To help researchers better understand the necessity and the efficiency of these techniques in online
education, we systematically review related works of the past decade to provide a comprehensive
view of the use of visualization in online education problems. We establish a taxonomy based on
the analysis goal and classify the existing visual analytics techniques into four categories: learning
behaviour analysis, learning content analysis, analysis of interactions among students, and prediction
and recommendation. The use of visual analytics techniques is summarized in each category to show
their benefits in different analysis tasks. At last, we discuss the future research opportunities and
challenges in the utilization of visual analytics techniques for online education.