Title :
VisMOOC: Visualizing video clickstream data from massive open online courses
Author :
Conglei Shi ; Siwei Fu ; Qing Chen ; Huamin Qu
Author_Institution :
CSE Dept., Hong Kong Univ. of Sci. & Technol., Hong Kong, China
Abstract :
Massive Open Online Courses (MOOCs) are becoming increasingly popular and have attracted much research attention. Analyzing clickstreams on MOOC videos poses a special analytical challenge but provides a good opportunity for understanding how students interact with course videos, which in turn can help instructors and educational analysts gain insights into online learning behavior. In this poster, we develop a visual analytical system, VisMOOC, to help instructors analyze the clickstream data. VisMOOC consists of three main views: the List View to list all course videos for analysts to select the video they are interested in; the Content-based View to show how each type of click actions change along the video timeline, which enables the most viewed sections to be observed and the most interesting patterns to be discovered; The Dashboard View shows the information of the clickstream data in different aspects, including the course information, the geographic distribution, the video temporal information, the video popularity, and the animation. Furthermore, case studies made by the instructors demonstrate the usefulness of VisMOOC and helped them gaining deep insights into learning behavior for MOOCs.
Keywords :
behavioural sciences computing; computer aided instruction; computer animation; data analysis; data visualisation; educational courses; human computer interaction; video signal processing; VisMOOC; animation; clickstream data analysis; content-based view; course information; dashboard view; geographic distribution; list view; massive open online courses; online learning behavior; video clickstream data visualization; video popularity; video selection; video temporal information; video timeline; visual analytical system; Abstracts; Animation; Data visualization; Image color analysis; Presses; Streaming media; Visualization;
Conference_Titel :
Visual Analytics Science and Technology (VAST), 2014 IEEE Conference on
Conference_Location :
Paris
DOI :
10.1109/VAST.2014.7042528