Analysis of MOOC Learning Rhythms

With the increasing popularity of Massive Open Online Course (MOOC), a large amount of data has been collected by the MOOC platforms about the users and their interactions with the platforms. Many studies analyze the data to understand the online learning behavior of the students in order to improve the courses and services. In this paper, we propose the concept of learning rhythms. We divide the students into three groups corresponding to the level of engagement with the course. We capture the learning behavior on different learning units by observing the delay time and the study time of the students, and use them to infer the eagerness and intensity applied to studying the materials. We use the frequent tree mining technique to extract frequent patterns. The most frequently occurred subtrees are identified as typical learning rhythms. To evaluate our method, we analyze the data provided by XuetangX, an online learning platform in China, and study the learning rhythms using one of its most popular courses. © 2018 IEEE.