CCVis: Visual analytics of student online learning behaviors using course clickstream data

Abstract

As more and more college classrooms utilize online plat- forms to facilitate teaching and learning activities, analyzing stu- dent online behaviors becomes increasingly important for instruc- tors to effectively monitor and manage student progress and per- formance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possi- ble relationships between student clickstream behaviors and their course performance. We employ higher-order network and struc- tural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evalua- tion. Finally, we discuss the limitation and extension of this work.