ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
3D representations for software visualization
Proceedings of the 2003 ACM symposium on Software visualization
TreeJuxtaposer: scalable tree comparison using Focus+Context with guaranteed visibility
ACM SIGGRAPH 2003 Papers
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
The Craft of Information Visualization: Readings and Reflections
The Craft of Information Visualization: Readings and Reflections
Information Visualization: Perception for Design
Information Visualization: Perception for Design
Execution patterns in object-oriented visualization
COOTS'98 Proceedings of the 4th conference on USENIX Conference on Object-Oriented Technologies and Systems - Volume 4
| Hi-index | 0.00 |
Student retention is an important topic in Computer Science departments across the country. Keeping strong students and helping struggling students perform better are two fundamental components of improving retention. Isolating the cause(s) of students leaving the major is an important area of research. We endeavor to explore this problem using a visualization tool to probe student data within the beginning course sequence in Computer Science. We would like to see what patterns exist amongst students, focusing on success, failure, and repetition patterns throughout the first three courses. Identifying these patterns can help isolate some of the causes of decreased retention within the department, allowing us to address individual projects, courses, or exams that may be causing students exceptional difficulty or loss of interest. Due to the large amount of data and the variety of students' paths through their courses, it is essential that a visualization be developed to represent the data. Using graph layouts, parallel coordinates, color-mapping, and interactive selection, users can explore and query the data. Users can discover patterns within the data by selecting subgroups of students and examining the event sequences to find patterns of success, failure, and repetition amongst those students. Departments can use this information to isolate profiles of students for retention, remediation, and recruitment efforts as well as identify areas of the curriculum or instruction that can be improved.