  This paper presents a novel approach to understand specific student behavior
in MOOCs. Instructors currently perceive participants only as one homogeneous
group. In order to improve learning outcomes, they encourage students to get
active in the discussion forum and remind them of approaching deadlines. While
these actions are most likely helpful, their actual impact is often not
measured. Additionally, it is uncertain whether such generic approaches
sometimes cause the opposite effect, as some participants are bothered with
irrelevant information. On the basis of fine granular events emitted by our
learning platform, we derive metrics and enable teachers to employ clustering,
in order to divide the vast field of participants into meaningful subgroups to
be addressed individually.
