Learning at Scale as a Driver of Innovation

How can we educate the world’s population in a scalable, affordable way?  This question is driving fascinating research at the intersection of human-computer interaction, social computing, natural language processing, machine learning, and learning sciences.    I’ll discuss the state-of-the-art in what is becoming known as learning at scale, with a focus on how to improve peer feedback, how to automate grading, and how to help instructors understand what the students understand.  I will emphasize how tackling this problem is leading to new socio-technical innovations.

Speaker Details

Dr. Marti Hearst is a Professor in the School of Information and the Computer Science Division at UC Berkeley.  Before joining Berkeley as a professor in 1997, she was a Member of the Research Staff at Xerox PARC.  She is the author of Search User Interfaces, the first academic book on that topic, and has written more than a hundred research articles in the areas of computational linguistics, information visualization, search user interfaces, human-computer interfaces, and how to improve learning at scale.  In summer of 2007, she visited MSR in Redmond in the search group.

Dr. Marti Hearst is currently Vice Present Elect of the Association for Computational Linguistics and a Fellow of the ACM, and has received 4 student-initiated Excellence in Teaching Awards.  She received her PhD in Computer Science from UC Berkeley.

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