Will Big Data Increase Retention Rates?

We’ve been making the rounds at tradeshows this month and the hot issue continues to be how to increase student retention and persistence.  Public, private, 2-year and 4-year colleges – everyone is facing this challenge.

“Big Data” projects—a catch-all term for leveraging existing data to identify at-risk students and get them the support they need—is rising to the top as a key solution, but will it work?  Here’s a breakdown of the issues big data can solve for schools, as well as challenges schools will face while trying to implement data projects that drive results.

Challenges to Successful Big Data Projects

Getting Reliable Data:

Faculty adoption of SIS/LMS systems is an ongoing issue, and when student data (like grades, attendance, information from residence hall advisors, etc) doesn’t live in an electronic system, it’s hard to pull into a big data project.

Losing the Silos:

Colleges and universities that are collecting data share that much of it gets put into a silo—a closed system where many key decision-makers don’t have access to it.  For big data projects to work effectively, all the relevant data needs to be in one place where analysis can take happen.

Implementing System-Wide Improvements:

Using data to inform interventions on a student-by-student level is important, but the real promise of big data is that it can improve instructional design and address retention problems systematically. Schools will need a structure to review data and make these larger decisions campus-wide.

Data Solutions

Some schools are running their own Big Data projects, while others are turning to outside partners to make the process easier.  Regardless of the approach your college takes, you’ll want to make sure your data project solves these issues:

The Silo Effect:

Breaking down the silos and putting all information from diverse sources (whether it’s SIS/LMS, financial aid, demographics, residence halls, advisors, etc) into one system that can be accessed by different groups on campus for a variety of needs.

Trend Spotting:

A data project should be able to leverage information in new ways to predict which students are at risk for failure/withdrawal/dropout and mobilize the proper resources to mitigate these risks.

The Big Three:

Steering financial, advising and academic resources to at-risk students to boost persistence.

big dataWhile Tutor.com is best known for our online tutoring solutions, we also offer comprehensive data and analytic services that can be plugged into just about any data project.

Tutor.com’s Predictive Insights™ Data Analysis provides immediate and actionable data on what students are struggling with (at the subject, topic and application level), when they’re looking for extra help, and what techniques our tutors used to help students break through and achieve mastery.  Students who lack prerequisite knowledge or struggle to achieve mastery of core concepts are flagged by our expert tutors for early alerts, which can be sent to faculty and advisors as email notifications.  Administrators may also review this data regularly, often catching issues long before other systems would have reported a problem.

Is your university implementing a big data project?  Tell us and your peers  about it in the comments section!

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