April 14, 2015 to April 17, 2015
Conglei Shi , The Hong Kong University of Science and Technology, China
Siwei Fu , The Hong Kong University of Science and Technology, China
Qing Chen , The Hong Kong University of Science and Technology, China
Huamin Qu , The Hong Kong University of Science and Technology, China
Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. With thousands of students watching course videos, enormous amounts of clickstream data are produced and recorded by the MOOCs platforms for each course. Such large-scale data provide a great opportunity for instructors and educational analysts to gain insight into online learning behaviors on an unprecedented scale. Nevertheless, the growing scale and unique characteristics of the data also pose a special challenge for effective data analysis. In this paper, we introduce VisMOOC, a visual analytic system to help analyze user learning behaviors by using video clickstream data from MOOC platforms. We work closely with the instructors of two Coursera courses to understand the data and collect task analysis requirements. A complete user-centered design process is further employed to design and develop VisMOOC. It includes three main linked views: the List View to show an overview of the clickstream differences among course videos, the Content-based View to show temporal variations in the total number of each type of click action along the video timeline, the Dashboard View to show various statistical information such as demographic information and temporal information. We conduct two case studies with the instructors to demonstrate the usefulness of VisMOOC and discuss new findings on learning behaviors.
Streaming media, Data visualization, Education, Image color analysis, Interviews, Visual analytics,
Conglei Shi, Siwei Fu, Qing Chen, Huamin Qu, "VisMOOC: Visualizing video clickstream data from Massive Open Online Courses", PACIFICVIS, 2015, 2015 IEEE Pacific Visualization Symposium (PacificVis), 2015 IEEE Pacific Visualization Symposium (PacificVis) 2015, pp. 159-166, doi:10.1109/PACIFICVIS.2015.7156373