Seminar: Harnessing Examples for Learning at Scale

Abstract: Increasing numbers of people are seeking higher education and professional development. A challenge to meet the growing demand is to scale such educational opportunities while maintaining their quality. My work tackles this challenge by harnessing examples from existing repositories to enable the creation of scalable and quality educational experiences. 

In this talk, I describe two computational methods to support learning at scale in authentic contexts. First, in college classrooms, it is challenging for instructors to design sufficient high quality learning materials. I introduce a learner sourcing technique UpGrade that takes past students’ open-ended solutions as input, and supports instructors in quickly creating high quality multiple-choice questions. A two-week classroom experiment showed that students learned as much using UpGrade as traditional open-ended assignments in 30% less time, while requiring no manual grading effort from instructors. Second, to support novices in using complex software applications, I develop a hierarchical approach that models the workflows in existing online demonstration videos and software logs, and repurpose such videos as targeted tutorials for end users.

My work suggests future pathways on harnessing existing resources for scalable content creation. I will conclude with insights around how to leverage the complementary strengths of peers, experts and machine intelligence to support learning at scale.

Bio: Xu Wang is a PhD candidate in the Human-Computer Interaction Institute in the School of Computer Science at Carnegie Mellon University, advised by Dr. Ken Koedinger and Dr. Carolyn Rose. She is an associate with PIER (Program in Interdisciplinary Education Research), and was named a Rising Star in EECS 2019. She conducts interdisciplinary research within the fields of Human-Computer Interaction, Cognitive Science, Artificial Intelligence and Education. Before coming to CMU, she received a Masters in Education from Harvard Graduate School of Education and a Bachelor’s in Science from Beijing Normal University. She has also worked in the User Interface Research Group at Autodesk Research.