Professional Biography

Dr. Baker researches how students use and learn from educational games, intelligent tutors, and other kinds of educational software. Drawing on the fields of learning analytics and learning engineering, he develops methods for mining the data that come out of the interactions between students and educational software. He then uses this information to improve our understanding of how students respond to educational software, and how these responses influence their learning.

Prior to joining Penn GSE, Dr. Baker was an associate professor in the Department of Human Development at Teachers College, Columbia University. He has been teaching the “Big Data and Education” MOOC for over a decade, with total enrollment of more than 100,000 students. He has served as founding president of the International Educational Data Mining Society, where he currently serves on the board of directors. He has been co-author on over a dozen award-winning papers and received the Educational Research Award from the Council of Scientific Society Presidents.

Research Interests and Current Projects

Dr. Baker is currently serving as Director of the Penn Center for Learning Analytics.

In recent years, Dr. Baker and his colleagues have developed automated detectors that make inferences in real-time about students' affect and motivational and meta-cognitive behaviors, using data from students' actions within educational software (without using any sensor, video, or audio data). Areas of focus include gaming the system, off-task behavior, carelessness, boredom, frustration, engaged concentration, and appropriate use of help and feedback. They 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) — an electronic coding procedure developed by Baker and Ma. Mercedes R. Roderio—as well as the HART Android app.

Dr. Baker and his colleagues have also studied engagement in MOOC courses, developing models to predict which learners submit scientific papers related to the MOOC afterwards. Dr. Baker and his colleagues are developing enhanced MOOCs that incorporate features from intelligent tutoring systems.

Other current projects include work to predict K–12 and undergraduate success, the development of detector-driven classroom interviewing methods that inform interviewers in real-time about changes in student affect and self-regulated learning behavior, analysis of which features in the design of online mathematics homework lead to differences in engagement, and analysis of student behavior in educational software across cultures.