As the fascination with innovation continues to catalyze change in contemporary post-secondary education, the field of innovation science is beginning to emerge, so that the relationships between and among the endeavors of Invention, Innovation, and Implementation are better understood. This article explores the use of data analytics as an innovation trigger for supporting student success. Very few organizations have approached improving student success using an open strategy that involves data scientists and the many implementers of student success working across America’s college campuses. In an effort to expand student success research, the Predictive Analytics Reporting (PAR) Framework created common data definitions and organizing principles to support collaborative student success research among like-minded universities. By starting with common data, the members of the PAR collaborative have the ability to share, compare, and disseminate results, insights, and strategies for student success. The approach is yielding interesting research on success factors within student segments and learning modalities. The ability to share the results paves the road to adoption at other institutions or within systems.
Vignare, Karen; Wagner, Ellen; and Swan, Karen
"The Value of Common Definitions in Student Success Research: Setting the Stage for Adoption and Scale,"
Internet Learning: Vol. 6
, Article 3.
Available at: https://digitalcommons.apus.edu/internetlearning/vol6/iss1/3
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