Modeling how students learn to program
Chris Piech, Mehran Sahami, Daphne Koller, Steve Cooper, Paulo Blikstein · 2012
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.