Student LMS Data: Data Pipeline and Privacy Considerations

Slides Available Here

Abstract: Students at Harvard are generating immense amounts of data on the campus Learning Management System. In this talk we will cover how this data is handled with privacy considerations in mind. We will review not just our data workflow but also our architecture for allowing for flexibility in whether identifiable data is produced. This is important because different uses of the data have different privacy considerations.


Dustin Tingley is Professor of Government in the Government Department at Harvard University. He received a PhD in Politics from Princeton in 2010 and BA from the University of Rochester in 2001. His research interests include international relations, international political economy, statistical methodology, and experimental approaches to political science. His book on American foreign policy, Sailing the Water's Edge, was published in fall 2015, and was awarded the Gladys M. Kammerer Award for the best book published in the field of U.S. national policy. Recent projects include attitudes towards global climate technologies and policies, and the intersection of causal inference and machine learning methods for the social sciences. Dustin is the Director of Graduate Studies for the Harvard Government Department, Faculty director for the Vice Provost for Advances in Learning Research Group (Harvard higher education data science group), faculty director of IQSS's Undergraduate Research Scholar program, is the founding director of the Program on Experience Based Learning in the Social Sciences, which founded and helps maintain ABLConnect, and is the former (and founding) editor of the APSA Experimental Section newsletter, The Experimental Political Scientist. Dustin initiated and organized the Harvard Government Department annual poster session, and has organized interdisciplinary conferences on causal mechanisms, climate change politics, negotiation in international relations, active learning, and the intersection of causal inference and machine learning. Dustin is a scientific adviser to EconVision.

Glenn Lopez received his Master’s degree in Systems Engineering from Cornell University and a B.S. in Computer Engineering from the University of California, Irvine. His current interests include system design, data analytics and visualization software, big data frameworks/technologies & modeling and simulation of machine learning algorithms. He currently oversees the research data infrastructure and data analysis workflow for all HarvardX online courses, including developing software to streamline common data requests for researchers. Previously, Glenn worked as a Principal Systems Engineer for nine years where he developed embedded software products and data analytics software, simulated control system and image processing algorithms, and performed data analysis on massive data sets collected from aircraft mission computers, high resolution sensors and inertial navigation systems.