SCS Faculty Candidate

Monday, March 6, 2017 - 1:00pm to 2:30pm


3305 Newell-Simon Hall



In this talk I will discuss and demonstrate how to use interactive teaching methods to support diverse sets of students in introductory computer science courses. I'll give a short sample lecture on random numbers and Monte Carlo methods which demonstrates the use of live coding and in-class exercises woven into a lecture format. I'll then briefly discuss the work I've done on supporting personalized learning at scale, and how this work might be extended in the future.—Kelly Rivers is a PhD candidate at Carnegie Mellon University in the Human-Computer Interaction Institute, where she is advised by Ken Koedinger. She specializes in teaching CS0 and CS1 courses at large scale, and works to incorporate her research into her classes. This 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. 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.Faculty Host: David AndersenComputer Science / Institute for Software Research

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CSD Faculty Candidate