Decades ago, colleges would start off freshmen orientation by pointing out how many students wouldn't succeed. The practice has gone out of style. But the graduation rate has barely budged: less than two-thirds of students who start college ever finish. So the central mystery of higher education remains the same: who will graduate? Who won't? What separates the successes from the dropouts? And how can colleges turn the latter into the former before it's too late?

Ellen Wagner's job is to answer those questions. The longtime education technology expert directs the Predictive Analytics Reporting Framework, one of the biggest data sets of higher education's nascent era of Big Data.

Once colleges know the students who are most likely to drop out, the hope is that they can help them avoid that fate

Using data on 1.8 million students from the past, Wagner can see the future. Give her the bare bones of a college freshman's biography — age, major, whether he is the first in his family to go to college, whether she has served in the military — and she can predict whether that student is likely to graduate.

"It sounds almost like science fiction," Wagner says. "But the reality is there's a lot that every one of us can be doing right now by simply looking at patterns of information."

Looking at these patterns is known as predictive analytics. This is how Amazon knows that people who buy Harry Potter and the Sorcerer's Stone are likely to buy The Lion, the Witch, and the Wardrobe and The Hunger Games. It's how Netflix recommends House of Cards to people who watch The West Wing. But it's also used to predict more serious matters: whether Target shoppers are pregnant, for example, or whether health insurance customers are more likely to end up in the emergency room.

Whether someone will graduate from college is not a question of life or death. But it's not far off, either, for both the students themselves and for the country. Graduates with bachelor's degrees make twice the hourly wage of people without a degree; over their lifetime, they will earn up to 1.8 times what a high school graduate does. They are more likely to marry. They are healthier on a wide range of measures. And the benefits don't just accrue to individual graduates; the Organization for Community and Economic Development estimates that the US spends less than $40,000 on each college graduate, and the American economy will reap nearly $200,000 in return.

A generation ago, American young adults were the best-educated in the world. Now 25- to 34-year-olds are 12th, behind Korea and Russia, among others. If predictive analytics can fulfill its promise of increasing the odds of getting a college degree, Wagner says, "this is going to be as big as computers in education."

A trend that sweeps throughout higher education — from for-profit colleges and community colleges to elite liberal arts colleges and public flagship universities — is rare. But an unusually diverse range of more than 150 colleges are now using some form of predictive analytics. Several organizations and companies, including Wagner's PAR Framework and the Education Advisory Board, a consulting firm, have offered their own analytics; some research universities are developing custom systems of their own.

Predicting the future, though, isn't enough. College faculty by and large don't believe in academic predestination, where some students are fated to succeed and others to fail. So once colleges know the students who are most likely to drop out, the hope is that they can help them avoid that fate.

The path is strewn with potential unintended consequences. Studies show teachers expend more time and attention with students they know will succeed; will professors neglect students data shows are likely to fail? States are under pressure to improve their graduation rates; if they can identify the students least likely to graduate, will it be too tempting to shut them out rather than admit them and help them through?

Along with the privacy concerns that other industries have confronted, the rise of Big Data presents an uncomfortable question specific higher education. The American ethos of college-going rests on "if you can dream it, you can become it." But when we can pinpoint the students least likely to succeed, what will happen to them?

Predicting who will graduate might not seem difficult enough to need a futuristic framework. Every college campus hosts some students who are visibly flailing and others who are evidently thriving. One of the biggest factors in whether a student will graduate college is simply how much money his or her family makes.

The majority of students, though, still fall into the muddled middle, neither obviously succeeding nor failing, and their futures are more mysterious. Is flunking a course the sign of a bad semester, or the harbinger of much worse to come? Is a student with a 2.3 GPA going to be fine — "C's get degrees," after all — or a future dropout in the making?

Many students "are getting B's and C's, chugging along, not raising any flags," says Ed Venit, a senior research consultant with the Education Advisory Board. The consulting firm has its own predictive analytics model, with more than 100 colleges as members. "But a big chunk of those students are going to not finish."

Some colleges are trying to identify those potential dropouts as early as possible. Every incoming student at the University of Texas at Austin now is entered into what the university calls its Dashboard — a giant data set based on students from the past 10 years. The Dashboard includes close to everything the college can know about an incoming freshman: family income, financial need, test scores, what classes he took in high school, whether she is the first in her family to go to college.

The system is a digital Sorting Hat. It has 16 different analyses it runs on incoming freshmen alone, says Rita Thornton, a research associate at UT-Austin, who worked on the analytics system. Some students who fall into the bottom quartile end up in a special program, the University Leadership Network, meant to help them beat the odds. That program lavishes the students with extra help and support, but it also sends a strong message: they belong on campus, and they are going to graduate.

The psychological interventions in particular seem to be helping students overcome adversity. "One way or another," education writer Paul Tough wrote in the New York Times Magazine about the UT-Austin program, "almost all of the students I spoke to were able to turn things around, often pulling themselves back from some very low places."

But the Dashboard presents another, more troubling possibility. Public universities like UT-Austin are under pressure from state legislators to improve their graduation rates or risk losing funding. They could use the wealth of predictive data another way, to find at-risk students during the admissions process and not let them in at all.

Consultants who work on predictive analytics say this is a possibility, but they are not particularly worried. The most selective colleges already use their competitive admissions process to sort students who will succeed from those they think will not.

The rest of America's colleges and universities, they say, can't afford to be that choosy. Either these schools rely on tuition revenue to keep their doors open, or they are public colleges required by state law to accept students who hit a certain academic threshold.

As a result, most colleges "don't have a lot of leeway in who they don't take," Venit says. "They're pretty much taking every student they think could potentially succeed on campus."

Even before predictive analytics arrived on campus, Southern Illinois University analyzed applicants' grade point averages and test scores to try to determine what made a successful college student, says John Nicklow, the university's provost.

"What you can't capture is the student who is strong, but they come in and something's not right, and they fail that one course," he says. With better data, "you'll know, and you'll be able to intervene."

Doing so has produced modest, but real, results. At Southern Illinois, the percentage of freshmen who continued into the second semester inched up the first year the college used predictive analytics, from 83.2 percent to 86.9 percent, and GPAs increased as well. Of course, it is possible that the college just admitted a stronger class of students last fall, Nicklow says. But that wouldn't explain why the biggest effect was among at-risk students. Southern Illinois students working with an adviser using predictive analytics were 6 percent more likely to continue than those who were not.

College administrators see two ways they can harness the power of Big Data to eventually help students. The first plunges into the heart of the college completion crisis to identify the students who are at risk of not graduating at all, as at UT-Austin. Often these are students who face odds outside their control: they are from underrepresented minorities, or from poor families, or are starting college later in life. With those students identified, colleges want to build them a support system to see them through to graduation.

The other strategy focuses on a more insidious contributing factor to both dropout rates and soaring student debt: for too many students, college takes longer than four years. Sometimes this is because of life circumstances for students who need to work or raise a family. But other delays are more preventable: students might not be taking enough credits to graduate in four years. Or they can't get into classes they need because campus is too crowded. Or they change majors too late and have to add a semester or two for newly required classes.

The two problems aren't separate. A strategy that helps one student to graduate can help another to finish on time. The University of Hawaii system, which is using the PAR Framework, is trying to address both.