With the growing aggregation of student data, predictive analytics and “at-risk” analysis are slowly beginning to supplant more traditional identification methods as the primary focus of institutions aiming to prevent student attrition.

With the growing aggregation of student data, predictive analytics and “at-risk” analysis are slowly beginning to supplant more traditional identification methods as the primary focus of institutions aiming to prevent student attrition. Despite the advantages offered through advanced data analysis, the profession has been hesitant to fully adopt the practice in crafting strategy and allocating resources. Using the text and at least one peer-reviewed article, post an original thread in support of, or in opposition to, the use of predictive analytics and at-risk scoring as a primary consideration in crafting a student support strategy.