Taking Informed Action on Student Activity in MOOCs
This work addresses the challenge of improving learning outcomes in MOOCs by allowing instructors to tailor actions to specific student subgroups, though it is incremental as it builds on existing clustering techniques.
The paper tackles the problem of instructors treating MOOC participants as a homogeneous group by proposing a method to divide students into meaningful subgroups based on fine-grained activity data, enabling targeted interventions.
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.