In the past three years, policymakers in almost every state have considered laws to ensure the safety of student data, and the US Congress is considering seven bills on student data privacy. At the same time, the Every Student Succeeds (ESSA) Act requires that states adopt evidence-based interventions to improve school performance. The education research to inform these interventions depends on access to student data. In Policymaking on Education Data Privacy: Lessons Learned, NASBE Director of Education Data and Technology Amelia Vance outlines key lessons policymakers should contemplate before taking action.
For years, colleges have sought out applicants who have high test scores or who can throw a football. But increasingly the targets are far more precise, in part because of technology and in part because recruiters are under the gun to meet enrollment goals.
Now, it’s easier for recruiters to use millions of high school students’ personal information to target them for certain traits, including family income or ethnicity, or even to predict which students will apply, enroll and stay in college.
These tactics, which are beginning to resemble the data-driven efforts used by political campaigns, have already prompted internal discussions at the College Board. Advisers to the College Board — which has data on seven million students it sells to about 1,100 institutions each year – met early this summer and talked about doing more to police how colleges can use the board’s student data, but a committee decided not to change the current policies.
David Wright is Wichita State University‘s (WSU) Associate Vice President for Academic Affairs. In his position, Wright is responsible for overseeing the vast amounts of data WSU uses to track student and faculty performance. Like a growing number of American educational institutions, Wichita State uses predictive analysis tools to optimize their offerings and steer help to students who need it.
“We know our data better than an outside agency. We know the business practices in our system better, which outside vendors don’t do, and this allows us to do more with the data than them,” Wright tells Co.Exist. Using data points such as a student’s paper grades, the amount of hours he or she is enrolled during each semester, whether they’re working part-time or full-time or not at all, the amount of assistance from family and a host of other factors, WSU can predict which students are likely to encounter problems.
That complicates life for enrollment leaders, whose ability to meet numerous institutional goals — academic profile, tuition revenue — depends on forecasts of how many students will show up. The less colleges know about applicants, the hazier their crystal balls become. Who’s serious? Who applied only as a worst-case backup option? Such questions echo across a competitive marketplace as many administrators watch the steady decline of their yield, the percentage of accepted students who enroll.
“I would argue that the more time and energy spent on students who’ve shown us they love us, the better it is for everyone.”
But colleges are hardly letting go of the wheel. Instead, they’re using an array of high-tech strategies to keep control of the ship. They’re gathering more data than ever before, not just on who students are, but what they do, especially online. That includes tracking the behavior of prospective applicants as they click through a college’s website. Just as Zappos knows which sneakers to show a specific shopper, some colleges now know which major to tell a would-be applicant more about at the very moment he wants to know.
The Andrew W. Mellon Foundation has awarded the National Information Standards Organization a grant to develop a Consensus Framework to Support Patron Privacy in Digital Library and Information Systems. The grant will support a series of community discussions on how libraries, publishers and information systems providers can build better privacy protection into their operations and the subsequent formulation of a framework document on the privacy of patron data in these systems.
The same big data techniques that are transforming other industries are seeping into the college and university admissions process to help predict whether students will succeed and graduate.
“This is the kind of stuff that savvy parents, students and college counselors know about,” said Bruce Poch, dean of admission and executive director of college counseling at the Chadwick School, a private school in Southern California, and former dean of admissions at Pomona College.
The point is simple: to increase graduation rates by using big data to identify the kinds of students who, experience has proven, are most likely to stick around.
John Domingue, director of the Open University’s Knowledge Media Institute and professor of computing science, who contributed to the report, says more comprehensive use of learning analytics could transform the sector. “Each week students are making moves as you make moves on a chess board,” he says. “Some combinations of these moves lead to success and some lead to failure so we push students to take the nearest path that students like them have taken to lead to success.”
He concedes that there are ethical issues involved in gathering and combining such detailed data, but argues it would be unethical to ignore its value. “We are morally obliged to use this information we have to make sure we maximise outcomes for these students,” he says, adding that once students paying thousands of pounds in fees realise the possibilities for their learning, they will start demanding it.
His only concern would be if it were used for reasons other than to benefit students – for example if employers wanted to buy data on student performance and motivation to help decide who to recruit.
A week after students begin their distance learning courses at the UK’s Open University this October, a computer program will have predicted their final grade. An algorithm monitoring how much the new recruits have read of their online textbooks, and how keenly they have engaged with web learning forums, will cross-reference this information against data on each person’s socio-economic background. It will identify those likely to founder and pinpoint when they will start struggling. Throughout the course, the university will know how hard students are working by continuing to scrutinise their online reading habits and test scores.
StudentLife is the first study that uses passive and automatic sensing data from the phones of a class of 48 Dartmouth students over a 10 week term to assess their mental health (e.g., depression, loneliness, stress), academic performance (grades across all their classes, term GPA and cumulative GPA) and behavioral trends (e.g., how stress, sleep, visits to the gym, etc. change in response to college workload — i.e., assignments, midterms, finals — as the term progresses).
Retention analytics application.