Join us here this Friday, December 14, at 2:00 p.m. ET for an instant e-chat on this critical issue. We'll be talking with Brian Parish, president of IData, a higher ed technology consulting and software solutions firm, about big-data's arrival on campus and the impact it's poised to make.
"Looking at big-data can be daunting for schools, but it does present huge opportunities," Parish told me in a recent interview. He pointed out three of them: predicting course demand, optimizing enrollment, and increasing retention.
Predicting course demand IData is working with some schools and knows of others that use predictive analytics to determine which courses and which sections students are going to want to take this semester, next semester, and up to three years out, Parish said. They can ask and answer, "How do we fill these courses and make the best use of our faculty load?" -- much like airlines do in determining passenger loads. "This is a good thing because it can help reduce the cost of education."
Additionally, predicting course demand can help assure students get the classes they need in order to graduate on time -- a huge consideration given that donning a cap and gown after four years is an increasing rarity, according to the National Center for Education Statistics. Georgetown University is a master at this, Parish said.
This D.C. institution uses a pre-registration system that requires students to list the classes and sections they want, sequentially. They overload the list, requiring students to rank 10 classes when only five are needed, for example. Advisors look at the ranks, interact with the students, and once all the data is set, Georgetown runs an algorithm against it, Parish said. But before it tells a student of the outcome, it'll adjust the curriculum, class offerings, and some variables -- running the models over and over again -- until it's happy that its reached a maximum matchup between students and classes.
This process contributes to Georgetown's exceedingly high -- slightly above 90 percent -- four-year completion rate. "That's one of the highest in the country, which is the goal," Parish said. "Colleges should be saying, 'Let's get these kids out in four years and not burden them with anymore debt.' "
Schools can tap into lots more data sources to use for prospecting and determining how to make the best use of their marketing dollars, Parish said. These aren't new endeavors, of course, but big-data helps them do so with greater accuracy, he added.
This includes determining which students receive scholarship and financial aid, Parish noted. "Schools don't just give money to the best applicant. They offer money to the best students who are most likely to accept."
Schools, as well external parties like the CollegeBoard and the US Department of Education, offer tools that help students pick the schools that are likely to provide the best fit for them. These recommendation engines suggest matches based on a variety of dimensions, including financial fit, Parish said. "We're trying to solve the problem of students leaving school because they don't like the environment with technology and data."
One initiative to watch in particular is the Predictive Analytics Reporting (PAR) Framework supported by 16 member institutions of the Western Interstate Commission for Higher Education. Using de-identified student data, PAR can help schools spot indicators that retention may be a problem, Parish said.
While lots of schools are still struggling with the traditional reporting they're required to do, an increasing focus on big-data will come in these three plus other areas, Parish said. "It'll happen because of the need and opportunity -- how else are schools going to get from where they are today to where they need to be if they don't do more sophisticated analytics with the new data they have?"
Good question, and one which we can explore during Friday's e-chat. Join us here!