A project that aims to identify common factors for why college students transfer, drop out, or fail to complete courses has released full definitions for the more than 60 data fields collected from its 16 institutional partners—a move that could help other schools improve student retention.
For the first time in the Predictive Analytics Reporting (PAR) Framework project’s history, it has publicly released full data definitions for the institutional, transcript, and student-level data in the PAR database. This is the first time the data fields and definitions used in PAR Framework modeling and analysis have been available beyond the project’s institutional partners.
PAR data definitions have been published using a Creative Commons license to encourage their distribution among the higher-education research community. Moving forward, PAR will continue to refine its data set to align, where appropriate, with the recently released Common Education Data Standards (CEDS) version 3 and other pertinent higher-ed data sets, the project says.
The PAR Framework is managed by WCET and unites a cohort of universities in a data mining project to identify effective practices for improving student retention in higher education. Currently, the project’s member universities are focusing on removing barriers to student success in online and blended-learning programs.
“PAR offers educational stakeholders a unique, multi-institutional lens for examining dimensions of student success from both unified and contextual perspectives,” the WCET press release explains.
“Common data definitions are at the core of the work we are doing with PAR,” said Beth Davis, PAR Framework project director. “Our goal in working with these varied institutional partners was to define common variables to ensure that comparisons and [data] aggregation are valid, reliable, and repeatable.”
Davis believes these simplified data definitions will enable the PAR Framework project to streamline research, achieve conclusive answers, and foster a better understanding of how to improve student retention.
(Next page: How the data definitions can be used to boost retention)
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