Analytics

This article is part two of a two-part series to help your institution bounce back from the disruption of COVID-19. 

The COVID-19 virus has completely changed life in America after just a few short weeks. Every day schedules, our shopping and eating habits, and even our interactions with friends and family have been disrupted.

As the VP of Enrollment or Director of Financial Aid, you may gain a better idea of the size and caliber of your incoming student class as the enrollment season progresses.

A hands-on approach to predictive analytics provides both the data and support for solutions.

Identifying individual students who are at-risk of dropping out is often a subjective exercise in higher education.

Predictive Analytics in a World of Choices 

As higher education continues to leverage student data and make predictions as to whether students will enroll, be retained, and graduate, the inevitable question that comes up is … why do students behave the way they do?

They invent, design, code, create, and collaborate.

SightLine has discovered through focused research that, on average, twice as many students leave their degrees during summer break than during winter break. Is this exodus a result of the temporal difference between the two vacations?

It’s well known that colleges struggle to retain a portion of students until graduation. Researchers and college professionals postulate a variety of reasons why students leave a four-year degree program.