Cornell University
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René Kizilcec – March 8, 2017

Wednesday, March 8, 2017 - 4:00pm
Gates 114

The Department of Information Science welcomes colloquium speaker, René Kizilcec, a doctoral candidate in the Department of Communication at Stanford University. At Stanford, he co-founded the Lytics Lab, an interdisciplinary research community focused on advancing the learning sciences with big data and computational methods. His research leverages experimental and computational methods to advance an understanding of how cultural and social-psychological factors influence people’s engagement with interactive technologies and how scalable interventions can support people in achieving their goals. His research has been published in leading journals such as Science, Proceedings of the National Academy of Sciences, Journal of Educational Psychology, Computers & Education, Transactions of CHI, and in the proceedings of major human-computer interaction and education conferences such as ACM SIGCHI, Learning at Scale, and Learning Analytics & Knowledge. This research has been supported by a Computational Social Science Fellowship, a Stanford Interdisciplinary Graduate Fellowship, and a Faculty Seed Grant for Innovation in Research at Stanford. René has worked with Facebook to conduct research on social influence in communication behavior on social media. Prior to graduate school he worked as a web developer. He holds an M.Sc. in Statistics from Stanford and a B.A. in Philosophy and Economics from University College London.

Title: Intervention Science: Making Online Learning Work for Everyone

Abstract: Persistent educational disparities worldwide and the shortage of skilled labor in the digital economy call for more efficient and equitable approaches to learning. Online courses can offer affordable and flexible learning opportunities; however, they may inadvertently reinforce disparities by failing to support the needs of a population with diverse demographic, socioeconomic, and cultural backgrounds. In this talk, I present a theory-driven approach to identifying and lowering barriers to achievement at scale with interventions in online environments. An 8-minute metacognitive self-regulation intervention helped thousands of online learners achieve their learning goals, though its effects were culturally bounded. It was effective in cultures with a more agentic and independent self-concept. Two different interventions targeted online learners in countries where people contend with a national stigma in global learning and work settings. The interventions closed the global achievement gap in Massive Open Online Courses by raising the average performance in developing countries to the level in the developed world. The findings contribute to the literatures on self-regulation and social identity threat by demonstrating how these psychological processes matter globally and unfold in online communities. Harnessing big data with experimental and computational methods can advance our understanding of how to design inclusive systems for diverse populations to pursue achievement goals.