Reviewing Your Data: What You Might Find

With scarce resources, it’s time to become increasingly savvy about the students you recruit and enroll, how you support them in ways that make a difference in their academic success and persistence, and how you approach the student-to-alumni transition.

Success Leaves Clues

Look at your past and current students and your current donors to identify indicators of retention, academic success, giving, and engagement. This entails identifying the shared characteristics of those students and alumni who are most successful and have the most pronounced affinity for your institution.

Success leaves clues, and locating those clues can help you make better decisions at each stage of the student lifecycle, from recruitment, to retention, to graduation and transition to alumni. Predictive modeling based on current student and alumni data can inform your investments in recruiting, student support, as well as student philanthropy and young alumni engagement.

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"Predictive modeling is a powerful statistical tool because it isolates those statistically significant variables that really drive the behavior of students—to find out of all the students who inquire, who will apply; or of admits, who will enroll; or of those enrolled, who will stay or who will leave."
Jim Scannell, President, Scannell & Kurz Inc

Recruitment

Beyond high school GPA and other standard measures of which students are likely to be well-prepared academically, look at your current student data to determine what factors correlate to a student’s academic success, persistence, engagement, and affinity with your institution. You will want to test whether factors such as non-cognitive skills, distance from campus, level of academic preparedness in certain courses, and early signs of engagement with your institution are predictive of desired student (and alum) behavior.

For example, Rob Durkle, Dayton University’s assistant vice president of enrollment management, is currently testing a hypothesis (and has anecdotal evidence to confirm it) that those students who are most engaged with the university throughout the admissions process also become the most engaged students during their four years at the institution.

Durkle is looking at which students do more than just click ‘submit’ on an online application: such as which students attend campus visits and other events during the enrollment process. He treats this data as one key predictive indicator of future affinity, and he is interested in determining if these engaged students later become the most engaged alumni, as well.

Retention

While GPA is the giant in the room when it comes to predicting academic performance, there are many factors—financial, academic, and personal—that impact retention. For example, a student’s degree of resilience to stress and a student’s level of commitment to their educational goals (as measured in an assessment of non-cognitive skills) can be key predictors of their likelihood of seeing their degree through to the end.

Other factors worth testing for correlations with student retention might include geographical distance from campus, degree of financial literacy, and academic preparedness in specific subjects.

Giving & Engagement

It is well-known that though the bulk of development dollars are invested in cultivation of fundraising prospects long after graduation, affinity is established while your future alumni are still students at your institution, not afterward. Mine your data on past students and current alumni for correlations between giving or engagement and student behaviors.

For example, suppose that your institution has had a program in place for a number of years in which alumni can serve as mentors for students. Now that some of those students have graduated and become young alumni, are they more engaged than other alumni? What about students who were interviewed by alumni during the admissions process or had other opportunities to interact with alumni and donors during their experience as students? What about young alumni who were active in student clubs and organizations while on campus—how are they behaving when compared to alumni who were not?

Getting Started

The other articles in this edition will review sample predictive indicators as well as sample tactics for acting on the data you find—in the recruitment phase, the retention phase, and the transition-to-alumni phase of the student lifecycle.

Of course, what serves as a good predictive indicator of student engagement at the University of Dayton or a predictive indicator of future giving at Dalhousie University may not be a good predictive indicator for students at your institution.

If you are very new to this process, start with univariate analysis, or what Jim Scannell calls “collecting descriptive knowledge.” Identify some likely cohorts—alumni who participated in varsity sports versus alumni who did not, alumni who participated in greek organizations versus those who didn’t, alumni who have attended at least one reunion versus those who haven’t, etc. Pull the data.

See which student demographics, behaviors, or other factors correlate with yield, retention, academic performance, or future giving. That will tell you what hypotheses you want to test with a predictive model.

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"You can’t wait until you can do something sophisticated. You need to start where you are and where your institution is. Even if you are starting small, show what data you can, tell the story, and help educate your peers on how to use that data to inform actionable decisions."
Loralyn Taylor, Director of Institutional Research & Registar, Paul Smith’s College

In This Issue

A Letter from Amit Mrig, President, Academic Impressions
Reviewing Your Data: What You Might Find

Data-Informed Recruitment and Enrollment
A Data-Informed Approach to Student Retention
Mining Your Data: From Students to Alumni