VEST Education and Research Speaker Series: Ryan Baker

Abstract

Contemporary large language models (LLMs) have exploded into much greater use in education in the last year. In this talk, I will discuss ongoing efforts at the Penn Center for Learning Analytics to leverage large language models to support learners and learning, focusing on two projects. In the first project, we use a LLM within a virtual teaching assistant, JeepyTA, who responds to student posts on a discussion forum and gives formative feedback on assignments. In the second project, we use an LLM to improve the depth and quality of feedback given to students learning to program. In this talk, I will discuss the successes and failures of these approaches, and what lessons we can draw from these projects for the broader use of large language models in education.

Speaker Bio

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.