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This post is written by Sarah Horn, VP of Retention Services at Helix Education.
Nearly ½ of students who start their college education don’t end up finishing.

National Center for Education Statistics

Retention statistics across higher education are alarming. And while tailoring your institution’s retention strategies to ensure each student receives the personalized attention he or she might need is inspiring, is it realistic? Too often, institutions are limited by the resources they have to dedicate to student retention, making it difficult to effectively scale intervention strategies and deliver the one-on-one support so many struggling students need.

But it doesn’t have to be this way. The combination of data + coaching can help.

Integrating Data + Coaching

You’ve heard the saying, “It takes two to tango.” Well, it applies to student retention, too. We have found that while data is invaluable, without anyone acting on it, all it is, is data. Similarly, while your advisors and student success coaching can provide a level of support, without the information to drive effective outreach, coaches have no basis to make meaningful connections with struggling students. Data has little impact without action, and coaches have little impact without information.

Fortunately, that is changing as more and more institutions discover the power of combining data with student success coaching. By incorporating enrollment, academic and behavioral data into predictive retention models, you can predict which students are most likely to struggle and your student success coaches can reach out before they do.

Getting Started with Predictive Retention Models

Your institution is full of valuable student data sources. The power of your retention strategy comes in finding correlations between all of this student information and how your students actually persist – connecting data between systems and creating predictive scoring models based upon the insights.

Rather than relying on a limited supply of advising and institutional resources to sort through massive amounts of student information, by creating a predictive retention model, you can automate the at-risk identification process. Your advisors and student success coaches can then take action with this prioritized subset of potentially at-risk students, in order to ask questions, work with the student to create an action plan that matches their goals, and successfully connect the student with institutional resources to help achieve them.

As many as one in three first-year students won’t make it back for sophomore year, says U.S. News and World Report. Yet, a unique combination of data + coaching can empower you to streamline your outreach, scale retention strategies, and provide more personalized support to more of these students.

To learn more about best practice predictive retention technologies and proactive coaching interventions in higher education, download Helix Education’s free quick guide: Predictive Student Retention: The Power of Data + Coaching.


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Higher Ed Live

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  • Amanda

    I’m a huge supporter of using data to improve student retention. Analytics are the key to discovering where problems lie so that you know where to target your solutions. It’s one of the reasons mobile apps are so useful for higher ed. The OOHLALA app, for instance, collects extensive data, allowing institutions to better communicate with their students, solve problems, keep them engaged and retain them for the length of their academic program.

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