海角大神

Privacy advocates听have long been pushing for laws governing how听schools and companies treat data gathered from students using technology in the classroom. Most now applaud President Obama's newly announced Student Digital Privacy Act听to ensure "data collected in the educational context is used only for educational purposes."

But while young students are听vulnerable to privacy harms, things are tricky for college students, too. This is especially true as many universities and colleges gather and analyze more data about students' academic 鈥� and personal 鈥� lives than ever before.

Jeffrey Alan Johnson, assistant director of institutional effectiveness and planning at Utah Valley University, has written about some of the main issues for universities and college students in the era of big data. I spoke with him听about the ethical and privacy implications of universities using more data analytics techniques.听

Edited excerpts follow.

Selinger:听Privacy advocates worry about companies creating profiles of us. Is there an analog in the academic space? Are profiles being created that can have troubling experiential effects?

Johnson: Absolutely. We鈥檝e got an early warning system [called Stoplight] in place on our campus that allows instructors to see what a student鈥檚 risk level is for completing a class. You don鈥檛 come in and start demonstrating what kind of a student you are. The instructor already knows that. The profile shows a red light, a green light, or a yellow light based on things like have you attempted to take the class before, what鈥檚 your overall level of performance, and do you fit any of the demographic categories related to risk. These profiles tend to follow students around, even after folks change how they approach school. The profile says they took three attempts to pass a basic math course and that suggests they鈥檙e going to be pretty shaky in advanced calculus.

Selinger: Is this transparent to students? Do they actually know what information the professor sees?

Johnson: No, not unless the professor tells them. I don鈥檛 think students are being told about Stoplight at all. I don鈥檛 think students are being told about many of the systems in place. To my knowledge, they aren鈥檛 told about the basis of the advising system that Austin Peay put in place where they鈥檙e recommending courses to students based, in part, on their likelihood of success. They鈥檙e as unaware of these things as the general public is about how Facebook determines what users should see.

Selinger: Are there concerns about how profiles prejudice the way professors look at students? And if there are legitimate concerns about this, are they being heard by the administrators who are creating or approving these systems?

Johnson: There are real concerns, but I don鈥檛 think they鈥檙e being heard at all. When I told my students I have Stoplight data, they were worried about what I thought of them coming into the class. It definitely bothered them. They wondered if instructors will think they need help, or dismiss them because it looks like they won鈥檛 succeed and it鈥檚 better to prioritize other students.听

Selinger: If students do learn about their profiles, is there any process at all for them to change information they think is inappropriate or inaccurate?

Johnson: I鈥檝e never heard anything. I think the presumption is that the data is accurate, in part because the data is either given to us by students themselves or else comes from stuff students did on campus. Universities haven鈥檛 done a lot to gather additional data. The bigger concern that I have at the moment is that universities construct data. Categories like 鈥渇reshman鈥� or 鈥渇irst-generation student鈥� don鈥檛 exist objectively; they鈥檙e created by universities, government, reform movements. For example, my mom had an associate鈥檚 degree when I started college, so am I a first-generation student? Some rules say yes. Colleges aren鈥檛 very conscious of the issues in constructing data, and students have no recourse to challenge. The data is accurate according to the data standards so no one can say it is incorrect, and the standards are technical, not substantive, so there鈥檚 no reason to allow students to challenge them.

However, higher ed is starting to move in the direction of gathering data from other sources. Arizona State is using Facebook data to improve retention by understanding a student鈥檚 social network. They take not participating in a social network as a sign that students might be thinking of dropping out.

Selinger: How does this work? Are they scraping Facebook data? Is this an opt-in program?

Johnson: As I understand it, they have a recruitment/new student page within Facebook. Once you "like" it and start interacting with it, they get access to a lot of your information, like who your friends are. And then, once you get on campus, they can see what friends you鈥檙e making. If you鈥檙e not making friends they can see that and take it as a sign that you鈥檙e at risk of withdrawing. And if you鈥檙e making friends but not interacting with them, same kind of thing. They also look at the data that comes from cards being swiped on campus facilities, and notice who else is swiping in at the same time as you are. Friendships can be inferred from this pattern.

Selinger: Do you think students have any idea that when they click "like" or swipe their cards they鈥檙e being studied in the way you outlined?

Johnson: The Chronicle of Higher Education听talked to students to find out. Turns out they had no clue and were indeed creeped out.听

Selinger: One of the things I find surprising with all of this is that universities seem to be hopping on the big data bandwagon after many companies have faced ethics and privacy blowback. Why aren鈥檛 they making better use of this information?

Johnson: Why aren鈥檛 universities tapping into the expertise of their faculty members who deal with technology ethics and basic social science and research methods who can talk about data as a social construct? If you鈥檙e a social scientist, especially a political scientist, you question the biases of data sets all the time. It鈥檚 bizarre that institutions aren鈥檛 asking for faculty to point out biases big data operations.

Selinger: You've developed a concept called "information justice," which is the idea of thinking about the use of information听technology听in terms of how it contributes to a more just society. How does applying that concept begin to make things better?

Johnson: Looking at something like Arizona State鈥檚 card swipe system, we can see that its primary purpose is to provide access to various facilities for people who have legitimate purposes for being there. Outsiders, for example, shouldn鈥檛 be in the gym. It鈥檚 a real security issue. But when we use card swipes to determine if someone is thinking of transferring and taking tuition revenue, the context changes. A new justification is needed to legitimate this surveillance, and it might not be possible to make! But if we think of privacy through control over information paradigms, it鈥檚 harder to raise objections. When you swipe your card, you鈥檙e creating data at Arizona State, and they鈥檙e not transferring it elsewhere, so there鈥檚 nothing wrong from that perspective.听

听Selinger: But is there a risk here of universities disciplining students to uncritically accept surveillance in broader social contexts?

Johnson: To some extent it is an issue. But we can also try to make students more aware of how big data is being used on them at the university and what consequences follow from those uses. That can help them become more critical about other uses of big data in society. Now, doing so doesn鈥檛 justify using big data in any of the ways we鈥檝e discussed. But it is something faculty can do to be subversive. Imagine running a class where you point out鈥� without using personal information 鈥� that the university expects 16 students will fail and asking how this makes everyone feel?

Evan Selinger听is an associate professor of philosophy at听Rochester Institute of Technology. Follow him on Twitter听@EvanSelinger.听