CCVis: Visual Analytics of Student Online Learning Behaviors Using Course Clickstream Data
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Abstract
As more and more college classrooms utilize online platforms to facilitate teaching and learning activities, analyzing student online behaviors becomes increasingly important for instructors to effectively monitor and manage student progress and performance. 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 possible relationships between student clickstream behaviors and their
course performance. We employ higher-order network and structural 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 evaluation. Finally, we discuss the limitation and extension of this work.