Visualizing Sequential Patterns in Large Datasets Using Levels of Abstraction

Student retention and success are important topics in all academic fields and institutions. Faculty members seek to understand which topics, theories, or skills defeat students or require strengthening to promote success. Programs seek to understand how to better sequence courses to ensure students are prepared for requisite future courses. Institutions seek to understand how to intervene to promote retention and improve graduation rates. Unfortunately, most statistics gathered by Institutional Research efforts are limited to failure rates, enrollment rates, and graduation rates and do not often explore individual student performance or enrollment patterns. While these are often further analyzed by various student demographic attributes such as race and gender, these statistical methods alone are insufficient to understand student performance over time and sequential patterns of enrollment or success and failure. This research presents a method using multiple levels of abstraction to visualize performance patterns over time.;To visualize student enrollment and performance patterns, several issues must be addressed including sequential versus concurrent enrollment, spatial layout of course events, and performance over time. Another challenge addressed by this work is that of presenting sequences within the context of the entire program. To address these challenges, multiple simultaneous visualizations are used to illustrate performance within context. The aggregated view represents the lowest level of abstraction: student enrollment and performance are aggregated into a graph structure, presenting patterns of movement throughout the program at the individual course level. The clustered view represents mined sequential patterns of enrollment and performance, illustrating common sequences. The directed view represents the highest level of abstraction and uses two visual elements, heat maps and a vector field, to illustrate overall performance in individual events and movement through the program. Results from multiple cohorts can then be superimposed on the same visualization to enable easy comparisons between patterns. Together, these abstractions provide a focus+context view of student performance, retaining outliers and emphasizing common patterns to illuminate dominant and unique patterns between cohorts of students.;This work makes substantial contributions in the fields of data mining, visualization, and education, addressing the challenges of visualizing sequential patterns. In data mining, new techniques are introduced to visualize mined patterns of sequential, concurrent, and cyclic items; enabling comparison of these patterns based on their subsequences, support, and item values. Within visualization, new techniques are introduced to simultaneously visualize patterns with sequential, concurrent, and cyclic events and performance values associated with these patterns. In education, new methods are introduced for visualizing students' grade-based performance over time, supporting a stronger understanding of enrollment and performance patterns throughout an academic program. Utility of these techniques is accomplished through the application to the University of Maryland, Baltimore County's computer science program with the goal of improving instruction and retention.