Educational Data Mining Learning Analytics Learning Engineering Disengagement
Ryan Baker (Ryan S. Baker)                                                              


I am Professor in the Graduate School of Education at the University of Pennsylvania. My primary appointment is in the Teaching, Learning, and Literacies Division. I am also affiliated with the Policy, Organizations, Leadership, and Systems Division and the Department of Computer and Information Science.

I direct the Penn Center for Learning Analytics and am Faculty Director of Penn's Masters in Learning Analytics (Online)

I also have courtesy appointments at the University of Edinburgh Moray House School of Education and Sport and at Ashoka University Department of Computer Science.

I am editor of Computer-Based Learning in Context. I am also Associate Editor of the Journal of Educational Data Mining.


I teach the MOOC Big Data and Education on EdX.

I am co-Director of the MOOC Replication Framework (MORF) project and the JeepyTA project.

Our Center developed a wiki detailing the evidence around Algorithmic Bias in Education.

I proposed the Baker Learning Analytics Prizes and co-authored the reports Transforming Educational Technology Through Convergence and High-Leverage Opportunities for Learning Engineering.

My research involves the use of Educational Data Mining/Learning Analytics to study learners and learning. I develop and use methods for mining the data that comes out of the interactions between students and educational software, in order to better understand how students respond to educational software, and how these responses impact their outcomes. I study these issues within intelligent tutors, simulations, MOOCs/online courses, and educational games. I study these issues in the context of K-12 formal and informal learning, higher education, and lifelong learning.

My colleagues and I have developed automated detectors that make inferences in real-time about students' affect and motivational and meta-cognitive behavior, using data from students' actions within educational software (no sensor, video, or audio data). We have in particular studied gaming the system, off-task behavior, carelessness, self-regulated learning strategies, boredom, frustration/confrustion, engaged concentration, and the appropriate use of help and feedback. We use these models to study the conditions under which these behaviors or affective states arise, and their impacts, including supporting in-the-moment qualitative research. Many of these models are developed using data collected through the Baker Rodrigo Ocumpaugh Monitoring Protocol (BROMP), and the HART Android app. Our app Quick Red Fox informs qualitative researchers about detector outputs in real time.

I have made some tools for EDM research available here.

This webpage offers additional resources, including data sets.

I have created some games with and for my children.

My kids and I also write children's stories for fun.

Please check out my publications web page for recent papers.

Follow my research group on Twitter or Facebook.

Or follow me on LinkedIn.

Educational Data Monkey (art courtesy of Maria Baker)

Quantitative Field Observation Affective Computing Human-Computer Interaction Quantitative Ethnography

Ryan Baker