Vanderbilt Journal of Entertainment & Technology Law

First Page



This Article is the most comprehensive study to date of the policy issues and privacy concerns arising from the surge of ed tech innovation. It surveys the burgeoning market of ed tech solutions, which range from free Android and iPhone apps to comprehensive learning management systems and digitized curricula delivered via the Internet. It discusses the deployment of big data analytics by education institutions to enhance student performance, evaluate teachers, improve education techniques, customize programs, and better leverage scarce resources to optimize education results.

This Article seeks to untangle ed tech privacy concerns from the broader policy debates surrounding standardization, the Common Core, longitudinal data systems, and the role of business in education. It unpacks the meaning of commercial data uses in schools, distinguishing between behavioral advertising to children and providing comprehensive, optimized education solutions to students, teachers, and school systems. It addresses privacy problems related to "small data"--the individualization enabled by optimization solutions that "read students" even as they read their books-as well as concerns about "big data" analysis and measurement, including algorithmic biases, discreet discrimination, narrowcasting, and chilling effects.

This Article proposes solutions ranging from deployment of traditional privacy tools, such as contractual and organizational governance mechanisms, to greater data literacy by teachers and parental involvement. It advocates innovative technological solutions, including converting student data to a parent-accessible feature and enhancing algorithmic transparency to shed light on the inner working of the machine. For example, individually curated "data backpacks" would empower students and their parents by providing them with comprehensive portable profiles to facilitate personalized learning regardless of where they go. This Article builds on a methodology developed in the authors' previous work to balance big data rewards against privacy risks, while complying with several layers of federal and state regulation.