The Predictive Analytics Reporting (PAR) Framework is a longitudinal data-mining project. It was created by and for educators with an interest in exploring the value of using big data–style analysis techniques to extend our understanding of student loss and momentum. PAR Framework partner institutions contributed de-identified student records to create a single federated data set, which was then analyzed to look for patterns that predict risks to student progress. Early results show great promise for helping institutions respond to retention-specific risks to student success. This session will offer a panel presentation and discussion of results from the PAR Framework proof-of-concept phase of development, featuring the unique perspectives of PAR Framework Founding Partners.
