Modeling How Students Learn to Program

Author: Piech, C., Sahami, M., Koller, D., Cooper, S. & Blikstein, P.
Year: 2012
Type: Refereed Conference Paper/Poster/Demo (with Proceedings)
Conference/Journal: SIGCSE 2012

Chris Piech, Mehran Sahami, Daphne Koller, Steve Cooper, and Paulo Blikstein. 2012. Modeling how students learn to program. In Proceedings of the 43rd ACM technical symposium on Computer Science Education (SIGCSE ’12). ACM, New York, NY, USA, 153-160.



Despite the potential wealth of educational indicators expressed in a student’s approach to homework assignments, how students arrive at their final solution is largely overlooked in university courses. In this paper we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a homework assignment. We subsequently show that this model is predictive of which students will struggle with material presented later in the class.