Jan 19, 2018
Lopata Hall, Room 101
Towards Development of Better Real-Time Sensor-Free Detectors of Student Affect
Dr. Ryan Baker
Associate Professor and Director of the Penn Center for Learning Analytics
University of Pennsylvania
Over the last several years, my group has worked to develop sensor-free detectors of affect for a variety of online learning platforms, in times in partnership with research groups who also work using physical sensor data. In this talk, I will discuss our work to improve the speed of detector development and the quality of the resultant detectors, from our early work to develop the BROMP protocol and HART handheld app, to our more recent work to leverage deep learning. I will also briefly review the uses that we have found for our detectors, both in basic research on affect and engagement, and through embedding them into affect-sensitive interventions.
Ryan Baker is Associate Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics. His lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but which predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments, and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 150 researchers in 4 countries. He was the founding president of the International Educational Data Mining Society, is currently serving as Associate Editor of two journals, has taught four MOOC instances, and was the first technical director of the Pittsburgh Science of Learning Center DataShop, the world's largest public repository for data on the interactions between learners and online learning environments. Baker has co-authored published papers with over 250 colleagues.