Learning with In-Depth Student Work

Lisa Yan

Lisa Yan
Ph.D Candidate, Stanford University
Monday, January 28, 2019 @ 11:00am-12:30pm
Room 1242, CSE Building

As undergraduate computer science classes grow, instructor workload also increases. At scale, it is hard to know which students need extra help–much less inspire, challenge, and connect with learners. But there is hope–with more data, algorithms and systems can more accurately identify student outliers and visualize student trends. In this talk I introduce two such systems used in classrooms today: TMOSS detects excessive collaboration during a programming assignment, while Pensieve facilitates teacher-student conversations on student metacognition. By offloading data processing and complex grading tasks to computers, my research aims to put teachers back into the classroom. Instructors who spend less time buried in low-level metrics can focus more on directly supporting students, thus fostering an inclusive, personalized learning environment even in the largest of classrooms.

Lisa Yan is a PhD candidate in Electrical Engineering at Stanford University. Her research leverages machine learning and in-depth student data in systems that encourage student-teacher interaction in large introductory computer science courses. Lisa holds an MS in Electrical Engineering from Stanford University and a BS in Electrical Engineering and Computer Science from UC Berkeley; she is an NSF Graduate Research Fellow.

Faculty Host: Christine Alvarado