SEAtS big data predictive analytics collect, categorise and score data collected from physical and digital touch-points overlaid with data from timetables, student information and other campus systems.
SEAtS predictive analytics collect, categorize and score data collected from physical and digital touch-points. This data is interwoven with data from timetables, socio-demographic and historic attainment, student information and data harvested from other campus systems. The result is a personalised view of engagement, retention and achievement for every student. Historic retention and engagement and attainment outcomes are used to train machine learning models to identify patterns applicable to the student population as a whole.
These models are applied to predict outcomes and potential intervention opportunities for the entire student population. The result is a unique perspective on the probability of success. A view based on engagement, retention and achievement, the primary drivers of successful student outcomes.
The SEAtS Data Repository supports your data analytics requirements by enabling you to process both structured and unstructured data from your campus and beyond and combine them with student profile and activity data to identify emerging trends and patterns.
SEAtS Predictive Analytics power the SEAtS Student Retention and Student Success solutions.
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