Chapter 28

Handbook of Learning Analytics
First Edition

Unpacking Student Privacy

Elana Zeide


The learning analytics and education data mining discussed in this handbook hold great promise. At the same time, they raise important concerns about security, privacy, and the broader consequences of big data-driven education. This chapter describes the regulatory framework governing student data, its neglect of learning analytics and educational data mining, and proactive approaches to privacy. It is less about conveying specific rules and more about relevant concerns and solutions. Traditional student privacy law focuses on ensuring that parents or schools approve disclosure of student information. They are designed, however, to apply to paper “education records,” not “student data.” As a result, they no longer provide meaningful oversight. The primary federal student privacy statute does not even impose direct consequences for noncompliance or cover “learner” data collected directly from students. Newer privacy protections are uncoordinated, often prohibiting specific practices to disastrous effect or trying to limit “commercial” use. These also neglect the nuanced ethical issues that exist even when big data serves educational purposes. I propose a proactive approach that goes beyond mere compliance and includes explicitly considering broader consequences and ethics, putting explicit review protocols in place, providing meaningful transparency, and ensuring algorithmic accountability.

Export Citation: Plain Text (APA)     BIBTeX     RIS

Supplementary Material
No Supplementary Material Available
References (48)

Alamuddin, R., Brown, J., & Kurzweil, M. (2016). Student data in the digital era: An overview of current practices. Ithaka S+R. doi:10.18665/sr.283890

Ashman, H., Brailsford, T., Cristea, A. I., Sheng, Q. Z., Stewart, C., Toms, E. G., & Wade, V. (2014). The ethical and social implications of personalization technologies for e-learning. Information & Management, 51(6), 819–832. doi:10.1016/

Asilomar Convention for Learning Research in Higher Education. (2014). Student data policy and data use messaging for consideration at Asilomar II. Asilomar, CA.

Barnes, K. (2014, March 6). Why a “Student Privacy Bill of Rights” is desperately needed. Washington Post.

Barocas, S., & Selbst, A. D. (2014). Big data’s disparate impact. SSRN Scholarly Paper. Elsevier.

Boninger, F., & Molnar, A. (2016). Learning to be watched: Surveillance culture at school. Boulder, CO: National Education Policy Center.

boyd, d., & Crawford, K. (2011). Six provocations for big data. SSRN Scholarly Paper. Elsevier.

Calo, R. (2013). Consumer subject review boards: A thought experiment. Stanford Law Review Online, 66, 97.
Center for Democracy and Technology (2016, October 5). State student privacy law compendium.

Citron, D. K., & Pasquale, F. A. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89.

Crawford, K., & Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. Boston College Law Review, 55(1), 93.

Daggett, L. M. (2008). FERPA in the twenty-first century: Failure to effectively regulate privacy for all students. Catholic University Law Review, 58, 59.

Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56–62. doi:10.1145/2844110

Divoky, D. (1974, March 31). How secret school records can hurt your child. Parade Magazine, 4–5.
DQC (Data Quality Campaign) (2016). Student data privacy legislation: A summary of 2016 state legislation.

Drachsler, H., & Greller, W. (2016). Privacy and analytics — it’s a DELICATE issue. A checklist to establish trusted learning analytics. Open Universiteit.

Jackman, M., & Kanerva, L. (2016). Evolving the IRB: Building robust review for industry research. Washington and Lee Law Review Online, 72(3), 442.

Jones, M. L., & Regner, L. (2015, August 19). Users or students? Privacy in university MOOCS. Science and Engineering Ethics, 22(5), 1473–1496. doi:10.1007/s11948-015-9692-7

Kobie, N. (2016, January 29). Why algorithms need to be accountable. Wired UK.

Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165.

Krueger, K. R. (2014). 10 steps that protect the privacy of student data. The Journal, 41(6), 8.
Mayer-Schönberger, V., & Cukier, K. (2014). Learning with big data. Eamon Dolan/Houghton Mifflin Harcourt.

Open University. (2017). Ethical use of student data for learning analytics policy.

Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. doi:10.1111/bjet.12152

Polonetsky, J., & Tene, O. (2014). Who is reading whom now: Privacy in education from books to MOOCs. Vanderbilt Journal of Entertainment & Technology Law, 17, 927.

Prinsloo, P., & Rowe, M. (2015). Ethical considerations in using student data in an era of “big data.” In W. R. Kilfoil (Ed.), Moving beyond the Hype: A Contextualised View of Learning with Technology in Higher Education (pp. 59–64). Pretoria, South Africa: Universities South Africa.

Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182.

Privacy Technical Assistance Center. (2014). Protecting student privacy while using online educational services: Requirements and best practice.

Reidenberg, J. R., Russell, N. C., Kovnot, J., Norton, T. B., Cloutier, R., & Alvarado, D. (2013). Privacy and cloud computing in public schools. Bronx, NY: Fordham Center on Law and Information Policy.

Rubel, A., & Jones, K. M. L. (2016). Student privacy in learning analytics: An information ethics perspective. The Information Society, 32(2), 143–159. doi:10.1080/01972243.2016.1130502

Schneier, B. (2008, May 15). Our data, ourselves. Wired Magazine.

Sclater, N., & Bailey, P. (2015). Code of practice for learning analytics.

Selwyn, N. (2014). Distrusting educational technology: Critical questions for changing times. New York/London: Routledge, Taylor & Francis Group.

Siemens, G. (2014, January 13). The vulnerability of learning.

Singer, N. (2015, March 5). Digital learning companies falling short of student privacy pledge. New York Times.

Singer, N. (2013, December 13). Schools use web tools, and data is seen at risk. New York Times.

Slade, S. (2016). Applications of student data in higher education: Issues and ethical considerations. Presented at Asilomar II: Student Data and Records in the Digital Era. Asilomar, CA.

Solove, D. J. (2012). FERPA and the cloud: Why FERPA desperately needs reform.

Tene, O., & Polonetsky, J. (2015). Beyond IRBs: Ethical guidelines for data research. Beyond IRBs: Ethical review processes for big data research.

US Department of Education (n.d.). FERPA frequently asked questions: FERPA for school officials. Family Policy Compliance Office.

US Supreme Court. (2002). Gonzaga Univ. v. Doe, 536 U.S. 273.

Vance, A. (2016). Policymaking on education data privacy: Lessons learned. Education Leaders Report, 2(1). Alexandria, VA: National Association of State Boards of Education.

Vance, A., & Tucker, J. W. (2016). School surveillance: The consequences for equity and privacy. Education Leaders Report, 2(4). Alexandria, VA: National Association of State Boards of Education.

Watters, A. (2015, March 17). Pearson, PARCC, privacy, surveillance, & trust. Hack Education: The History of the Future of Education Technology.

White House. (2014). Big data: Seizing opportunities, preserving values. Washington, DC: Exceutive Office of the President.

Young, E. (2015). Educational privacy in the online classroom: FERPA, MOOCs, and the big data conundrum. Harvard Journal of Law and Technology, 28(2), 549–593.

Zeide, E. (2016a). Student privacy principles for the age of big data: Moving beyond FERPA and FIPPs. Drexel Law Review, 8(2), 339.

Zeide, E. (2016b, March 16). Interview with ED Chief Privacy Officer Kathleen Styles. Washington, D.C.

About this Chapter

Unpacking Student Privacy

Book Title
Handbook of Learning Analytics

pp. 327-335




Society for Learning Analytics Research

Elana Zeide

Author Affiliations
Center for Information Technology Policy, Princeton University, USA
Information Society Project, Yale Law School, USA
Information Law Institute, New York University School of Law, USA

Charles Lang1
George Siemens2
Alyssa Wise3
Dragan Gašević4

Editor Affiliations
1. Teachers College, Columbia University, USA
2. LINK Research Lab, University of Texas at Arlington, USA
3. Learning Analytics Research Network, New York University, USA
4. Schools of Education and Informatics, University of Edinburgh, UK

Founding Members
Previous Image
Next Image

info heading

info content