Tracking students' data has become a must for new online learning platforms. With thousands of students enrolling in MOOCs, a huge amount of data is generated. As a teacher, you might face issues with respect to managing their requests, following up their comments, or even tracing their most engaged activities in the course.
Researchers from Tsinghua University from Beijing China proposed a smart visual analysis solution for large MOOC data. The main objective of their research is to help instructors tracking the progress of their students. The proposed solution was designed to include four main views for the instructors:
- Demographics view
- Activeness calendar view
- Progress distribution view
- Personal Footprints view
The researchers designed the smart visual approach based on data collected from two MOOCs from xuetangX platform. They tracked over 4 million events, 15k students and 32 weeks of lecturing materials.
The Visual Analytics
As previously said, the authors proposed four views to the instructors. See the figure below before going through the article.
Demographics View (top left)
it was preferred to show pie graphs. the instructor is able to see the gender percentage of his/her class, the age category, and educational level background.
Activeness Calendar (top right)
Tooltip technology has been used to ease the interactive experience. the calendar demonstrates heatmap so the instructors know what is the most active days of their course.
Progress Distribution (bottom left)
In Progress Distribution View, users are allowed to see how many students are active in every past week and analyze the distribution of learning progress up to now.
Personal Footprints (bottom right)
In Progress Distribution View, a click on the circle will trigger Personal Footprints View to demonstrate this student's learning progress.
Li, X., Men, C., Zhang, F., & Du, Z. (2017, November). A Smart Visual Analysis Solution for MOOC Data. In Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence & Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), 2017 IEEE 15th Intl(pp. 101-106). IEEE.