The Direct Network

Using Big Data for Student Success

Posted by Liz Schulte on Oct 18, 2017 5:30:00 AM
Topics: Higher Ed, business intelligence, Big data

College dropout rates continue to increase while enrollment falls. Many factors undoubtedly contribute to this trend, but ultimately it falls to the school to curb the decline. Which is why many colleges and universities have turned to their data to look for answers. Recently the focus has shifted from dissecting past data to a more proactive approach of compiling information on current students to better help them through predictive analysis.

Using Big Data for Student Success

Traditionally, smaller schools have been successful at knowing their students more intimately and nearly customizing their academic experiences. Now large schools, with the help of “big data” analysis, are using a similar approach. Student dropout rates are about more than just their academic performance. In fact, 40% of the students who abandoned their studies have a B average. With a little extra personalized attention, could that number significantly decrease? Many schools think so and have started connecting the dots to ensure students are receiving the guidance they need to reach graduation.

According to an article in the Atlantic, Temple University learned that students who are stressed about their finances are at greater risk of leaving college. This isn’t news to many colleges who are already offering emergency small loans and grants to students at risk of dropping out for financial reasons. Finding ways to save students money on anything from textbooks to tuition is necessary to the success of the students and reducing their stress levels.

Another factor many higher education institutions are looking at is the early predictive capabilities of certain classes on a student’s long term success. Georgia State’s nursing school discovered a consistent predictor of success in its students is their performance in an introductory math class. Arizona State University determined that a student’s timely enrollment into a specific requisite for their major was a great indicator they would reach graduation. Now advisors are alerted and graduation rates have climbed 20%.

Middle Tennessee State increased its advising staff and expanded its free tutoring due to its data analysis. Its administration also redesigned a number of courses and switched to weekly student monitoring, resulting in an increase of 5% in the freshman retention rate in just two years. A professor at the University of Michigan developed the application ECoach, which provides personalized feedback and encouragement to students. Other colleges have taken the approach even further by collecting information from incoming freshmen’s social media accounts and using it as a factor in their acceptance to the school based on predictive factors (The New Tool Colleges Are Using in Admission Decisions: Big Data).

However, all of this data mining hasn’t been met with open arms. Many people have raised concerns about privacy, data security, and whether or not the predictive analysis will direct students away from pursuing their dreams. Definitions and guidelines for such information will have to be set, but for now “big data” has given the schools a way to better understand and communicate with their students. 

About Liz Schulte

Liz Schulte is an author and business owner with a background in customer service, marketing and higher education development.

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