Supporting Growing Computer Science Courses with Data-Driven Feedback and Interactive Teaching

(CSE Colloquium Lecture Series)

Kelly Rivers, Carnegie Mellon

Speaker: Kelly Rivers, Ph.D. Candidate, Carnegie Mellon University

Date: Wednesday, February 15
Time: 11:00am
Location: Room 1202, CSE Building
Host: CSE Prof. Mia Minnes

Abstract:  Growing interest in computation as a field of study has caused rapidly increasing enrollment in computer science programs across the country. This has led to great opportunity, as CS programs now have access to many students from varying backgrounds. However, it has also resulted in very large class sizes, which makes it difficult to provide students with personalized instruction that can help them succeed. In this talk I'll describe the research I've done on data-driven methods for automatically generating personalized feedback for programming problems and how this work can be integrated into instruction to support student learning at large scale. I'll also discuss future directions for curricular improvement and innovation which combine teacher knowledge with supplementary technology to provide the best possible learning experience for students.

Bio:  Kelly Rivers is a PhD candidate at Carnegie Mellon University in the Human-Computer Interaction Institute, where she is advised by Ken Koedinger. Her research focuses on developing data-driven methods for generating hints and feedback for students who are learning how to code, and draws inspiration from the fields of intelligent tutoring systems, program transformations, and learning science theory. She specializes in teaching CS0 and CS1 courses at large scale, and tries to incorporate her research into her classes. Kelly graduated from Carnegie Mellon with a B.S. in Mathematics and Computer Science in 2011 and plans to defend her thesis in the summer of 2017.