Visual Progression Analysis of Student Records Data

Abstract

University curriculum, both on a campus level and on a per-major level, are affected in a complex way by many decisions of many administrators and faculty over time. As universities across the United States share an urgency to significantly improve student success and success retention, there is a pressing need to better understand how the student population is progressing through the curriculum, and how to provide better supporting infrastructure and refine the curriculum for the purpose of improving student outcomes. This work has developed a visual knowledge discovery system called eCamp that pulls together a variety of populationscale data products, including student grades, major descriptions, and graduation records. These datasets were previously disconnected and only available to and maintained by independent campus offices. The framework models and analyzes the multi-level relationships hidden within these data products, and visualizes the student flow patterns through individual majors as well as through a hierarchy of majors. These results support analytical tasks involving student outcomes, student retention, and curriculum design. It is shown how eCamp has revealed student progression information that was previously unavailable.

University Information System’s Impact on Academic Performance: A Comprehensive Logistic Regression Analysis with Principal Component Analysis and Performance Metrics

“…eCamp, a visual knowledge discovery system, adds a novel dimension to student progression analysis. Raji et al [16] introduce a system that reveals previously unavailable information, aiding in student outcomes, retention efforts, and curriculum design. The visual representation of student records data proves instrumental in offering a comprehensive understanding of student progress.…”

CoursePathVis: Course path visualization using flexible grouping and funnel-augmented Sankey diagram

“…While there are recent works that focus on visual analytics of student progression in the higher education setting [14,15], the proposed CoursePathVis aims to understand how students progress through the curriculum and identify common patterns and outliers for stakeholders to make better-informed decisions or recommendations. Our work is closely related to course program data visualization [23,21]. Instead of using fine-grained trajectories followed by clustering and composition [23] or a coarsegrained Sankey-like radial graph showing student progression in a university-wide setting [21], we employ the funnel-augmented Sankey diagram to investigate course paths taken by students within a department.…”

“…Our work is closely related to course program data visualization [23,21]. Instead of using fine-grained trajectories followed by clustering and composition [23] or a coarsegrained Sankey-like radial graph showing student progression in a university-wide setting [21], we employ the funnel-augmented Sankey diagram to investigate course paths taken by students within a department.…”

Failure Analysis in University and Computer Science Contexts With Data Mining

“…[ Baradwaj and Pal 2011] investigated students' failure, a relevant task in EDM, using a decision tree to evaluate students' performance, grouping them according to their results. [Raji et al 2017] addressed the problem by proposing a visual analysis system, using over 16 years of collected data, to describe the students' progression. The systems allowed them to question some school policies.…”