Rene F. Kizilcec, Ph.D.
Assistant Professor, Cornell Information Science
Director, Future of Learning Lab
General Chair, ACM Learning at Scale 2022
PI, Behavioral Intervention Research Infrastructure (BIRI)
Jacobs Foundation Research Fellow 2022-24
Office: 208 Gates Hall, 107 Hoy Rd., Ithaca, NY 14853
Office Hour: email me to schedule a time
Rene Kizilcec is an Assistant Professor of Information Science, graduate field member in Communication and Physics, and founding director of the Future of Learning Lab at Cornell University. Kizilcec’s research is on the use and impact of technology in formal and informal learning environments (college classes, online degree programs, mobile learning, professional development, MOOCs, and middle/high school classrooms, etc.) and scalable interventions to broaden participation and reduce achievement gaps.
His research has been published in Science, Science Advances, Proceedings of the National Academy of Sciences, Journal of Educational Psychology, Computers in Human Behavior, Computers & Education, and in the proceedings of leading human-computer interaction and education conferences like ACM CHI and Learning@Scale; his work received multiple ACM Best Paper awards. In 2020, he was Program Co-Chair for the 2020 ACM Learning at Scale conference.
Kizilcec received a BA in Philosophy and Economics from University College London, and a MSc in Statistics and PhD in Communication from Stanford, with a doctoral thesis on designing psychologically welcoming online learning environments, which was awarded the Nathan Maccoby Outstanding Dissertation Award. Prior to joining Cornell, he spent a year as a research assistant professor at Arizona State University and as a research director at the Stanford Graduate School of Education.
Kizilcec is known for his research on understanding and supporting learners in online courses. He also works on developing methods for the design and analysis of experiments. His recent work examined the consequences of social identity threat, self-regulation, trust, and cultural differences on individual behavior and performance using longitudinal field experiments. He leverages techniques from data mining, machine learning, and natural language processing to examine behavior and motivation, reveal heterogeneous treatment effects, and inform user-centered design.
You can follow him on Twitter @whynotyet.