Principles for Collecting, Managing, Sharing, and Protecting Student Data

Because of the diversity and number of college and university students who use writing center services, writing centers are often seen by student researchers, scholars, and administrators as sites for research about everything from accessibility, to literacy practices, to disciplinary habits. More recently, a number of university administrators across the country have also come to see them as sites for mining student data in order to make determinations about retention, graduation rates, and student usage of campus support services. The former, more traditional research projects, are clearly covered by IRB guidelines. Data mining requests for purposes of predictive analytics and other “big data” projects are more recent and entail a complicated set of ethical, legal, and technical concerns that are only beginning to be understood and sorted out nationally. We carefully follow the (rapidly changing) conversations about the uses of big data in higher education in order to ensure we understand its affordances and constraints as they concern the students we serve.

A writing center’s primary purpose is as a learning site, and protecting and advocating for the student writers who use our services is our chief concern. We seek to balance the needs and privacy of the students who use our services with the promise and risks entailed by big data projects. In order to respond to these varied needs, we have drafted the following set of principles regarding student data. These are informed by the work of organizations such as Stanford CAROL, Ithaka S+R, New America, Data & Society, and scholars such as Cathy O’Neil, Jacob Metcalf, and Kate Crawford, among many others.

Those interested in accessing identifiable student data and records for the purposes of big data, research, and assessment projects in our writing center are invited to familiarize themselves with our guiding principles and engage in conversation with us about how best to meet student needs, protect student privacy, and further the educational goals of the university.

Of course, we comply with Ohio Open Records laws.

Guiding Principles for Work with Writing Center Data

These principles are guided by the resources and scholarship cited at the end of this document.

  1. Identifiable information about student visits to writing centers is protected by FERPA laws, although FERPA laws are increasingly inadequate in an era of Big Data. (See: “Student Privacy Principles for the Era of Big Data” by Elana Zeide, Drexel Law Review 2016.)
  2. Students are adults and have a right to know that data is being collected about them, and why.
  3. Students’ informed consent should be obtained for any data collected about their writing center visits, and they should be able to decline to give such consent.
  4. Students have the right to see the reports resulting from any analysis of data collected about their writing center usage.
  5. Data collected about students in writing centers should benefit students and writing centers.
  6. Data is not neutral and its analysis and uses are not neutral.
  7. Context matters. If the writing center context is not taken into account during analysis, information about students could be easily misinterpreted or used for purposes other than those initially intended.
  8. We seek the least intrusive and most context-aware methods for collecting and analyzing data about our students that align with recommended practices for educational research.
  9. We are obligated to seek clarity about a) where student data is going; b) what will be done with it, now and in the future; c) who will have access both to the data and to any findings about that data; and d) what algorithms will be used to analyze the data.
  10. We are obligated to educate ourselves about both the benefits and the risks of predictive analytics software. While currently many big data initiatives at institutions of higher education do not require IRB approval, we believe that they should and will be required to obtain such approval in the future.

How to Request Writing Center Data at Miami

Individuals, groups, or committees requesting from the writing center raw, identifiable student data or site access to collect student data themselves are asked to talk with us first and provide the following information as applicable:

  • The goal(s) of the research;
  • What data will be collected or is being requested;
  • How the research benefits the writing center and its students;
  • The research participants;
  • How consent will be obtained from the participants;
  • How participants will be recruited;
  • How student identities and privacy will be ensured;
  • How data will be analyzed and by whom;
  • Who will have access to the data, and any analysis of or report about it; and for what purposes;
  • How access to the data will be limited and secured (particularly if data will be housed in third-party databases);
  • How results of analysis will be shared with the writing center staff and students and, if applicable, with writing center scholars more broadly;
  • How ethical use of this data will be ensured, both now and in the future;
  • How data will be appropriately contextualized;
  • Whether this data set will be combined with other data sets, what those other data sets are, and why they are being combined;
  • What algorithms will be used to analyze the data and who designed them.


Alamuddin, Rayane, Jessie Brown, Martin Kurzweil. Student Data in the Digital Era: An Overview of Current Practices. Ithaka S+R Research Report. September 6, 2016.

Blumenstyk, Goldie. “Group Unveils a ‘Model Policy’ for Handling Student Data.” The Chronicle of Higher Education. September 6, 2016.

Blumenstyk, Goldie. “Big Data is Getting Bigger. So are the Privacy and Ethical Questions.” The Chronicle of Higher Education. July 31, 2018.

Ekowo, Manuela and Iris Palmer. Predictive Analytics in Higher Education: Five Guiding Principles for Ethical Use. New America. March 6, 2016.

Metcalf, Jacob and Kate Crawford. “Where are Human Subjects in Big Data Research? The Emerging Ethics Divide.” Big Data and Society. June 1, 2016.

O’Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, 2016.

Responsible Use of Student Data in Higher Education. A Project of Stanford CAROL and Ithaka S+R.

Sample Policies Regarding Use of Student Data. Responsible Use of Student Data in Higher Education. A Project of Stanford CAROL and Ithaka S+R.

Zeide, Elana. “Student Privacy Principles for the Era of Big Data.” Drexel Law Review 2016.