Educational Data Mining Intelligent Tutoring Systems The Learning Sciences Gaming the System
Ryan Baker (Ryan S. Baker)                                                              


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

I direct the Penn Center for Learning Analytics.

I also have an affiliate appointment at Worcester Polytechnic Institute, in the Department of Social Science and Policy Studies, and have courtesy appointments in the Department of Human Development at Teachers College Columbia University, at the University of Edinburgh Moray House School of Education and Sport, and at the University of Texas at Arlington Link Research Lab.

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

I am co-Lead of the Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub.


I teach the MOOC Big Data and Education on EdX.

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

I proposed the Baker Learning Analytics Prizes.

My research is at the intersection of Educational Data Mining and Human-Computer Interaction. 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 learning. 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, the military, and lifelong learning.

In recent years, 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, "WTF behavior", boredom, frustration, engaged concentration, and appropriate use of help and feedback. We use these models to make basic discoveries about human learning and learners. Many of these models are developed using data collected through the Baker Rodrigo Ocumpaugh Monitoring Protocol (BROMP), and the HART Android app.

I have made some tools for EDM research available here.

My daughter and I created a card game, Academic Squabble

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

Selected Current and Upcoming Projects

  • Predicting STEM Career Choice from Computational Indicators of Student Engagement within Middle School Mathematics Classes (funded by NSF ITEST)
  • Classroom Environment, Allocation of Attention, and Learning Outcomes in K-4 Students (funded by IES)
  • Detectors of Affect in Educational Software (funded by NSF and Gates Foundation)
  • Detecting, Studying, and Adapting to Affect in Military Training (cooperative agreement with Army Research Laboratory)
  • Creating Design Patterns for More Engaging Educational Software, Based on Evidence from EDM (funded by NSF REAL)
  • Studying Social Factors that Impact Community Participation After Use of MOOCs (funded by NSF DIRITL)
  • Studying Participation in Online Courses By Students From Underrepresented Groups (funded by Gates Foundation)
  • Student Behavior in Educational Software Across Cultures

Please check out my publications web page for recent papers.

Follow my research group on Twitter or Facebook.

Educational Data Monkey (art courtesy of Maria Baker)

Quantitative Field Observation Affective Computing Human-Computer Interaction Psychometric Machine-Learned Models

Ryan Baker