PRODUCT SUITE

A student contemplating leaving his or her school begins to show certain academic and other behaviors over a period of time giving you sometimes a brief window of opportunity to intervene. As the student's behavior becomes progressively marked it is imperative that your intervention be not only precise but also timely. Hence accurate and early detection of at-risk students-or those students who have a greater likelihood of dropping out of a course or program-is critical. Intuition or conclusions drawn based on accumulated experience have a relevant but rather limited role to play in identifying potential dropouts. Critical early alerts of at-risk students can be created through a systematic analysis of the great amounts of data already available in your school based on predetermined rules.

ProRetention™ is designed for the imperative task of identifying students to whom you need to reach out, so you can focus on your mission to help them and thus retain them. ProRetention™ has a Predictive Analytical Modeling component that provides you a comprehensive, flexible, and convenient tool that will be custom-tailored to your needs and processes. It's our job as a system provider to help you be successful in your mission to retain and help all your students graduate.

Our Predictive Analytical Modeling helps institutions in addressing key issues of student retention by generating reports based on data and statistical analyses that represent a significant improvement over intuition. We compile relevant data by using statistical techniques and predictive modeling to help faculty and advisors determine which students may face academic difficulty, allowing timely intervention to help them succeed.

What does Predictive Analytical Modeling offer?

• Use of collective student information, both historical and transactional
• Near-accurate probability determination of a student dropping out
• Capability to analyze from multiple data sources such as your SIS or LMS, among others

What do you get?

• Prediction of patterns in advance
• Dynamic definition of rules for risk triggers