Unpacking Student Privacy

Abstract

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.

Keywords: MOOCs, Masssive Open Online Courses, Virtual Learning Environments, E-learning, E-textbook, smart tutors, personalized learning, student privacy, student data, Education data, ed tech, FERPA, SOPIPA, ethics, algorithms, learning science, data mining, learning analytics, academic analytics