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

Abstract

The critical revision of the content, scope and alignment of curricula is essential for improving students success at Higher-Ed institutions. The effort is never trivial, as students are accepted from multiple sources (e.g. high schools, community colleges, other institutions) with different academic preparation, socioeconomic background, and motivation. In addition, students follow diverse paths either according to their interests, or according to the necessities, such as academic or financial requirements. Some graduate from their entry majors in 4 years, some need more time, some transfer to different disciplines, and some leave their universities. Accordingly, providing Higher-Ed decision makers with an accurate summary of these diverse student characteristics is a necessity to help them make better data-informed decisions for improving students success. In this regard, any data mining methodology that can convey valuable patterns from student data sets in clear and informative fashion will be valuable. In this study, we discuss the development and use of such a visual tool based on the Sankey Diagram. It presents students progress and mobility patterns in an easily understandable format, was developed using open source software, and was used by several departments of a research intensive Higher-Ed institution of more than thirty-thousand students during their academic review process. This paper provides a general discussion about how these visuals could be used in Higher-Ed institutions by discussing problems that can be addressed, detailing the dataneeds, the development methods, comparisons with other reporting methods, and how they were used in actual practice.

Analysing Students’ Mobility at Higher Education Institutions: Case of Ukrainian University During the War

“…Examples of such systems include Mobility Management Systems, Student Management Systems, Student Connectivity Module (Materka et al, 2012) and Erasmus's integrated system for all participating universities -Erasmus+ Without Paper (Fedorchuk et al, 2022). In addition, some education data mining tools that proved to be valuable for other management issues, can also be appropriate to identify patterns and predict students' mobility (e.g., Sankey Diagram (Oran et al, 2019), logistic model and network approach (Columbu et al, 2021)).…”

Pathway patterns mobility study of first time vs. transfer students in computer science and information technology programs at a public university

“…Sankey diagrams (or student flow diagrams) are one way of analyzing student cohorts and have shown to be effective in addressing aspects of institutional effectiveness (Morse, 2014). Various studies have used these visualizations to understand student flow and address institutional challenges (Basavaraj et al , 2018; Basavaraj and Garibay, 2019; Oran et al , 2019). Such visualizations were created in this study to understand student degree mobility in their programs and their transitions between programs within the institution.…”