The Predictive Analytics Reporting Framework

While it has long been an accepted truism that ‘big data’ holds promise to revolutionize higher education, translating the 1s and 0s collected at institutions into usable, predictive information about what makes students more or less likely to complete a course has proven difficult. A new effort pooling data from multiple institutions is showing promise and could become an important tool for educational leaders and policymakers as they work to increase degree completion.

The WICHE Cooperative for Educational Technologies (WCET) is in the midst of a multi-year project funded by the Bill & Melinda Gates foundation that aims to do just that. The Predictive Analytics Reporting (PAR) Framework is a collaborative multi-institutional data mining effort focused on predicting and reducing the risk that higher education students will not complete courses.
The project completed a proof of concept pilot project last year that demonstrated the feasibility of combining data across institutions and using it to identify both common and institutional factors that are risk factors for non-completion of online courses.
Although the effort has focused on students under the age of 26, some of the analysis, particularly surrounding transfer students, will certainly have implications for adult college completion efforts. In the coming months, the Adult College Completion Network will present findings of the project and keep Network members informed of progress and potential implications.
For more resources on PAR, see:


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