Deciding what tactics to put in your default prevention plan to target students while they still are enrolled can be difficult.
The steps a school can take to prevent default often are limited by time and resources. We know that default prevention starts while the student is in school — but schools often must focus default prevention efforts on helping borrowers after they enter repayment, to improve upcoming cohort default rates.
So, when there is little time to do “in-school” default prevention, how do you decide how to spend that precious time?
One way to determine how best to use your limited time and resources for in-school default prevention is to know who is most likely to default. Then you can target those specific populations in your efforts. The outcome of default is complicated, and so are the experiences that influence it.
It is easy to speculate about the student characteristics that are most predictive of default, but it has been my experience that schools sometimes are surprised by the results when they examine their students’ data closely.
How do you figure out the characteristics of your students who repay their loans, versus those who default? Start with National Student Loan Data System portfolio reports. You can download data about everyone in a specific cohort. To do a defaulter analysis, you’ll want to use the last closed cohort window, which is 2009.
Once you have all this NSLDS data in a spreadsheet, you’ll want to add some institutional data to help you.
You can add your own columns to your spreadsheet, or perhaps you’ll want to ask your information technology department to use the student Social Security number in the file to access your internal student information system and to populate some extra columns for you.
Here are some student characteristics you might want to examine:
- Number of terms completed.
- Hours earned/hours attempted.
- Grade point average.
- Satisfactory academic progress information.
- Program of study.
- Incoming-test score data.
- Date of declaring a major.
- Number of remedial courses required.
- Date of admission.
- Expected Family Contribution.
- Number of dependents.
Once you have all this data, it is simply a matter of asking the right questions. For example, did students who had satisfactory academic progress probation dates on their records default at a higher rate than those who didn’t?
No matter what kind of data you examine, it is important to remember to always compare defaulters to non-defaulters. Here’s an example to show you why: Let’s say 30 of your 100 defaulters were on satisfactory academic progress probation at some time. We might be tempted to think, “Wow, that’s 30 percent, so I should do something for SAP students.”
But be careful. What if 30 percent of all students in that same cohort were on SAP probation at some time during their education? Then that satisfactory academic progress data really doesn’t tell us anything meaningful with respect to default.
Now you might be saying, “Oh, yeah, I remember that stuff from my statistics class.” So dust off that textbook — or better yet, make friends with someone in your school’s math department, or with a graduate student taking an education statistics course. I’ll bet that faculty member or student will be able to help you identify those characteristics that are most associated with your current defaulters.
If you can find a higher incidence of default among specific sets of students in the past, then you can be confident in designing targeted programs and services that reach out to these student groups in the present. You’ll know that efforts specifically for these groups are the best place to spend your time and energy on default prevention while students still are in school. For example, we know that withdrawal often is associated with default — and if, by examining the data, you find that is the case on your campus, then partnering with your retention office also might help you target your default prevention efforts.