Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms
This work addresses the challenge of utilizing detailed learning behavior data to improve educational materials and insights for K-12 students, representing an incremental advancement in visual analytics for e-learning.
The authors tackled the problem of analyzing student learning behaviors on K-12 mathematics e-learning platforms by developing a visual analytics system that supports correlation analysis and visualization of mouse-movement logs, enabling better design of learning resources and interpretation of problem-solving styles.
With increasing popularity in online learning, a surge of E-learning platforms have emerged to facilitate education opportunities for k-12 (from kindergarten to 12th grade) students and with this, a wealth of information on their learning logs are getting recorded. However, it remains unclear how to make use of these detailed learning behavior data to improve the design of learning materials and gain deeper insight into students' thinking and learning styles. In this work, we propose a visual analytics system to analyze student learning behaviors on a K-12 mathematics E-learning platform. It supports both correlation analysis between different attributes and a detailed visualization of user mouse-movement logs. Our case studies on a real dataset show that our system can better guide the design of learning resources (e.g., math questions) and facilitate quick interpretation of students' problem-solving and learning styles.