Princeton University

School of Engineering & Applied Science

Technology and Pedagogy: Using Big Data to Enhance Student Learning

Christopher Brinton
Engineering Quadrangle B327
Tuesday, May 10, 2016 - 12:00pm to 1:30pm

The “big data revolution” has penetrated many fields, from network monitoring to online retail. Education and learning are rapidly becoming part of it, too: today, course delivery platforms can collect unprecedented amounts of behavioral data about students as they interact with learning content online. This data includes, for example, each click made while watching a lecture video, while submitting an answer to a quiz question, or while posting a question on the discussion forum. The ability to capture this data presents novel opportunities to study the complex process by which learning occurs, and also raises interesting questions around how behavioral data can be leveraged to improve the quality of each student's learning experience, especially as learning is scaled to the size of Massive Open Online Courses (MOOCs).
In this talk, I will detail three research thrusts we have undertaken in using big data to study learning and enhance its quality: Learning Data Analytics (LDA), Social Learning Networks (SLN), and Integrated and Individualized Courses (IIC). These interrelated areas involve extracting recurring patterns from student behavior, studying the social networks by which students learn from each other, and designing algorithms for individualizing course delivery based on behavioral analytics. To investigate these thrusts, we have used real-world data and experiences from our own teaching in the development of models and prototypes. We have in turn worked with industry to build systems around our algorithms for helping students, for helping instructors help their students, and for helping students help each other.
At the end of the talk, I will also discuss some of the next steps that we are currently investigating in this emerging research area.