AIM Analytics: Making Online Learning Work For Everyone

Mon, 01/22/2018 - 12:00pm to 1:30pm

North Quad, Space 2435

AIM Analytics was created to bridge the gaps in the support of UM learning analytics researchers with respect to the building of technical skills, sharing knowledge of educational datasets, and facilitating collaborative investigations.

Making Online Learning Work For Everyone


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. I present a theory-driven approach to identifying and lowering barriers to achievement at scale with interventions in online environments. Harnessing big data with experimental and computational methods can advance our understanding of how to design inclusive systems for diverse populations to pursue educational goals. 

Speaker Bio: René Kizilcec is an Assistant Research Professor in the School of Engineering at Arizona State University and the Director of Digital Learning Research in Stanford’s Graduate School of Education, where he co-founded the interdisciplinary Stanford Lytics Lab. He will transition to Cornell's Department of Information Science as an Assistant Professor in July 2018.

He holds a PhD in Communication and MS in Statistics from Stanford University, and a BA in Philosophy and Economics from University College London. Kizilcec’s research addresses issues of equality and inclusion in online learning from a psychological and system design perspective. He is particularly interested in the psychological challenges to realizing the potential of digital environments for diverse and global audiences.

His recent work focused on psychological barriers to academic achievement among online learners from negatively stereotyped backgrounds; how cues in digital learning environments can signal belonging and reduce psychological threats, and cultural variation in effective self-regulation strategies. His research appears in Science, PNAS, Journal of Educational Psychology, and the proceedings of CHI, LAK, and Learning at Scale; and it was awarded an ACM Best Paper Award and Stanford's Nathan Maccoby Outstanding Dissertation Award.

Twitter: @whynotyet