Educational Systems for Maximizing Learning Online and in the Classroom

The goal of my research is to develop novel computing systems that optimize student learning for any environment. Learning is impacted by a wide variety of factors, and it can be challenging for teachers to personalize instruction to meet diverse student needs. In this talk, I will describe my work on developing interactive technologies that capture fine-grained information about the learning process and leverage this data to meet the unique needs of each student, teacher, and classroom. My research approach combines techniques from computer science and the learning sciences to first understand the factors that impact learning, then build novel systems that optimize learning given those factors, and finally evaluate the resulting systems through large-scale online and classroom experimentation. My talk will demonstrate how I have used this approach to create an incentive structure that promotes the growth mindset in an educational game, develop new frameworks for generating instructional scaffolding, and evaluate a personalized learning system that visualizes student data in real-time for classroom teachers. These interventions have been used by over 100,000 students online, adopted by companies, and used in classrooms throughout the Seattle area. My work demonstrates that personalized data-driven systems can transform how we teach, assess, communicate, and collaborate in learning environments.