Does Big Data Know Best? NSA and College Admissions

While it's certainly possible a student will to try to "game" the system to an advantage -- with a financial aid package, for example -- colleges, like the NSA, will continue to be one step ahead in identifying some new system with better predictive value than the next.
This post was published on the now-closed HuffPost Contributor platform. Contributors control their own work and posted freely to our site. If you need to flag this entry as abusive, send us an email.

Recent weeks have seen some people outraged over the NSA's collection of massive amounts of data in its intelligence-gathering efforts. The concerns are wide-ranging and many are legitimate. The NSA's defense of the effort revolves around the need to gather and analyze information so they can piece bits together to predict behavior.

This defense sounds oddly familiar to this enrollment professional. Although the foremost concern in my field is not national security, I can't help thinking about how many college admissions offices nationwide engage in very sophisticated data-gathering efforts to try to predict the behavior of students in the process of choosing a college.

Before I go any further, I want to make clear that there is a great difference between what college admissions offices do and what people believe the NSA does. First, admissions offices engage in permission-based marketing and typically have received permission to solicit and communicate with students. Next, college admissions offices collect data with the purpose of trying to determine a good match, which has a mutual benefit for colleges and students. Finally, college admissions offices store and use data for a fairly short period of time during the recruitment process.

However, like the NSA, college admissions offices do identify, solicit, collect, maintain, analyze, massage and leverage all sorts of data in their work to recruit, admit and enroll students. College admissions offices spend substantial money, devote considerable human resources and rely on big data to help them do their job and bring clarity to a cloudy crystal ball.

Below are a few examples of how the field of college recruitment and enrollment uses big data in its work.

Finding students-- Perhaps one of the clearest examples of the use of big data is how colleges go about targeting and finding students each year. Many schools invest heavily in buying the names of students through several organizations that provide lots of information, which is made available simply by virtue of the student being a student -- engaging in school-sponsored activities, taking standardized tests, etc. Among the list providers are The College Board, ACT, NRCCUA and CBSS. The typical name costs a college $0.35 (plus the hundreds of dollars that will be spent once recruitment begins). While each list provides different information, colleges typically have access to information about a student's academic performance, intended major, co-curricular interests, preference for a public or private education, test scores, and main influences on final college choice. It's a lot of information and very useful in targeting students who appear to be a good fit.

Source codes-- Colleges track how they first learn about a student and often use this information to predict the likelihood of a student applying and ultimately enrolling. For many colleges, an "unsolicited inquiry" is the best type of inquiry. From day one, a student who called, emailed or wrote a letter requesting information may receive more care and attention during the recruitment process. Why? Big data reminds us that the student took the initiative and sought our college out. We say thanks to big data.

Demonstrated interest-- More colleges are being direct in describing how "demonstrated interest" impacts an admissions decision, because big data has told us that those students who show the most interest (visiting, engaging with a counselor, returning the information we request) are most likely to enroll. Because big data has told us this and because the date seems reliable, many colleges have developed sophisticated formulas to track demonstrated interest. These systems might include tracking every form of communication the student initiates with the college and assigning points for demonstrated interest, or assigning a code to each student based on what they've done (or not done) at the time of admission. Big points might be assigned when a prospective student sends a tweet about the college or joins a Facebook page. (And perhaps points will be taken away when a student accidently refers to another institution in an essay?)

FAFSA position-- In recent years, colleges have started to look to items not as closely associated with admission to identify patterns of behavior. A new focus for predicting a student's level of interest in a particular college is related to the position the student has listed that college within the grouping he or she has identified on the FAFSA. Because students who place a college in position number one on the FAFSA are perceived as those most likely to enroll, they might be the first to receive financial aid awards, or might receive a personal phone call to discuss the award letter. Why? Because big data, which provides us that information, also tells us those "conversion" rates are high.

Date of application-- Big data tells us when a student applies has some predictive value, too. Students who apply earliest in the process often are the most interested and most likely to enroll. These students might receive more attention throughout the process, and even special invitations to events and priority consideration for scholarships and financial aid. At a college with rolling admissions, students who apply very late might convert the best because they've been shut out of a first choice. The point here is that the middle group might not get as much attention, because the data has predicted they are not as likely to enroll.

While it's certainly possible a student will to try to "game" the system to an advantage -- with a financial aid package, for example -- colleges, like the NSA, will continue to be one step ahead in identifying some new system with better predictive value than the next.

Not every college finds value in the data described above, but you can rest assured big data of some variety is informing their decision-making. Is it worth thinking about whether or not all of this information and resulting data profiles are positively impacting our work and behaviors? I'll bet NSA is asking the same question.

Go To Homepage

Popular in the Community