Skip to main content

Big data can help schools improve retention rates

When it comes to for-profit companies, the benefits of data quality are unmistakable. By compiling accurate information on potential customers, corporations can find better ways to market their products and target advertising toward specific demographics who will respond to it best. Big data has plenty of nonprofit uses as well, most notably in the education system, where schools can gather information that will help them boost retention rates.

Students drop out of college for a variety of reasons. Some decide that they're desperate to enter the workforce as soon as possible, while others can't afford the expense of attending a private institution. Still others simply lose interest in their educations. Despite all the obstacles, though, ensuring better retention in education is a win-win proposition. It helps students develop into well-rounded adults and enables schools to continue thriving with full classrooms.

According to the Tutor.com Blog, big data can offer a solution to the retention problem.

The silo effect
Big data works to classify students into different "silos," or categories based on their statuses and educational backgrounds. Students can be classified by demographics, financial aid levels, residence halls, class schedules or advisors. Once they're broken down into these categories, school administrators can organize their data and make better sense of it.

Data quality is crucial in this process. If officials have inaccurate or unverified information about students' locations or educational levels, they may wind up finding false positives in their data, leading them to make poor decisions.

Trend spotting
Once students are siloed, administrators can begin to look for trends, with the ultimate goal of leveraging their information into better decisions about how to retain students.

One example of this came recently from Georgia State University in Atlanta, where the financial aid office was able to use big data to isolate students who were particularly in need of tuition help. By identifying students who were doing well and on track to graduate with honors but needed just a few dollars to finish their payments, the university was able to find the best allocation of its aid funding.

There are other examples everywhere. By finding trends among their student bodies, schools and their administrators can glean valuable lessons about their students and what motivates them to stay in school. However, if not for data quality, all their efforts could be moot.