Visual analysis of multi-subject association patterns in high-dimensional time-varying student performance data

Exploring the association patterns of student performance in depth can help administrators and teachers optimize the curriculum structure and teaching plans more specifically to improve teaching effectiveness in a college undergraduate major. However, these high-dimensional time-varying student performance data involve multiple associated subjects, such as student, course, and teacher, which exhibit complex interrelationships in academic semesters, knowledge categories, and student groups. This makes it challenging to conduct a comprehensive analysis of association patterns. To this end, we construct a visual analysis framework, called MAPVis, to support multi-method and multi-level interactive exploration of the association patterns in student performance. MAPVis consists of two stages: in the first stage, we extract students’ learning patterns and further introduce mutual information to explore the distribution of learning patterns; in the second stage, various learning patterns and subject attributes are integrated based on a hierarchical apriori algorithm to achieve a multi-subject interactive exploration of the association patterns among students, courses, and teachers. Finally, we conduct a case study using real student performance data to verify the applicability and effectiveness of MAPVis.