A Smart Visual Analysis Solution for MOOC Data

Massive Open Online Courses (MOOCs) have been the focus of online education in recent years. Compared to traditional courses, indirect communication and trans normal number of students in MOOCs bring great difficulties for instructors in knowing how much and how well students have learned. In this paper, a smart visual analysis solution for MOOC data is proposed. The solution contains four views, Demographics Distribution View, Activeness Calendar View, Progress Distribution View and Personal Footprints View. We also integrate data mining results into visualization to help instructors quickly build an insight. Our solution provides a direct, explicit, vivid and interactive way for instructors to know about students' learning progress in multiple scales, which was commented as very helpful in the case study.