Using Data to Support the Life Cycle of Graduate Student Success
By 2025, graduate enrollment is on track to grow by 3.5 million students. Finding a best-fit student is difficult enough without having to sort through an overabundance of data. Worse, using the wrong data leads to an ineffective recruitment approach, wasting time and resources.
In this webcast, admissions experts explain how to classify your typical candidate, examine applicant data and implement the strategies that will lead to enrollment success.
Vice President of Strategic Enrollment
Professor and Program Director
University of the Pacific
Associate Provost, Dean of the Graduate College
and Professor of Communication Sciences and Disorders
Missouri State University
Robert Ruiz: Liaison was founded in 1990. We work currently with about 2.2 million applications per year throughout some 800 campuses across North America. We are indeed admission enrollment specialists, but more specifically we create communities around centralized application services.
Whether it’s dentistry or medicine, or speech and communication disorders, or graduate education or graduate management education, there are lots of lessons to be learned that can be shared across disciplines. This is an uncertain time in graduate education. Certainly, a lot is happening. It’s this challenging and tenuous uncertainty of graduate education in general that has us thinking about the role of gatekeepers.
The fact of the matter is that in graduate education, we have lots of tasks. When we think about retention, we think about outcomes, because I think we would all agree not only do we want good students to come into our programs, but we also want great students to leave our programs and go out into their respective fields and disciplines to do the hard work that they’ve been training so rigorously to do.
This is a constantly changing climate and landscape, but anything we can do that relies on empirical evidence to help us make better decisions and help our students succeed in the end is the goal of any graduate program.
Julie Masterson: Central administrators at the graduate college always set lofty goals, and that’s what we’re supposed to do. We aspire to be everything we can be, but often those goals need to be interpreted within the context of what’s feasible. For some programs, enrollment management goals often focus on ensuring that our capacity is reached. For others, the focus is on making sure that the student population has the desired demographic characteristics and academic readiness.
We talk so much now in admissions on identifying best-fit students. But it’s not simply identifying the students who have the highest GPA or the highest test scores; rather it’s about identifying best fit. For example, speech language pathology has always had a mismatch between the gender of our professionals (most are female) and the gender of the clients we serve (most are male).
So what if I expected my SLP program to fix that, or what if our funding depended on having at least half of your graduate students as males? Well, I can use a WebAdMIT reporting function to compare the gender distribution between Missouri State and national data, and I can use that data to work with my national association to see what we can do to address this gender distribution.
Larry Boles: We all want the same thing: the best, the brightest, the most diverse. We want a lot of different things in our cohorts of students. We tend to look at GRE scores, grade point averages, interviews and so on. But is there any evidence that these predict success in graduate school? You or your faculty may be just relying on what you’ve always done, but it might be a worthy discussion to say, “OK, what do we care about?”
We decided on the idea of a “local GPA”—the most recent 60 units of graduate or undergraduate work. You could instead say you want to look at the cumulative undergraduate, everything undergraduate or just senior year, and so on. All of this is possible using CAS.
We use an initial screening: the last 60 units of grade point average, and the three individual components of the GRE—the verbal, the quantitative and the written. Then we go through it with a fine-tooth comb: looking at the personal essays, videos, letters of recommendation. We collected data on about 130 of our students and found that there was no single variable that predicts success. It was very easy using this CAS system to find the regression formula. Of course, the magic of SPSS is that the software did it for me.
To watch this web seminar in its entirety, please visit universitybusiness.com/ws091218