Identifying Students' Progress and Mobility Patterns in Higher Education Through Open-Source Visualization

• Published in: 2022 IEEE Integrated STEM Education Conference (ISEC) pp. 154 - 161
• Main Authors: Oran, Ali, Martin, Andrew, Klymkowsky, Michael, Stubbs, Robert
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.03.2022
• Subjects: Data mining; Data visualization; Education; Educational Data Mining; Higher Education; Learning Analytics; Open source software; Open-source Data Visualization; Visualization
• DOI: 10.1109/ISEC54952.2022.10025315

For ensuring students' continuous achievement of academic excellence, higher education institutions commonly engage in periodic and critical revision of its academic programs. Depending on the goals and the resources of the institution, these revisions can focus only on an analysis of retention-graduation rates of different entry cohorts over the years, or survey results measuring students' level of satisfaction in their programs. They can also be more comprehensive, requiring an analysis of the content, scope, and alignment of a program's curricula, for improving academic excellence. The revisions require the academic units to collaborate with university's data experts, commonly the Institutional Research Office, to gather the needed information. The information should be highly informative yet easily interpretable, so that the review committee can quickly notice areas of improvement and take actions afterwards. In this study, we discuss the development and practical use of a visual that was developed with these key points in mind. The visuals, referred by us as "Students' Progress Visuals are based on the Sankey diagram and provide information on students' progress and mobility patterns in an academic unit over time in an easily understandable format. They were developed using open-source software, and recently began to be used by several departments of our research intensive higher-ed institution for academic units' review processes. Our discussion includes questions these visuals can address in Higher-Ed, other relevant studies, the data requirements for their development, comparisons with other reporting methods, and how they were used in actual practice with actual case studies.