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. © 2012 ACM.
CITATION STYLE
Piech, C., Sahami, M., Koller, D., Cooper, S., & Blikstein, P. (2012). Modeling how students learn to program. In SIGCSE’12 - Proceedings of the 43rd ACM Technical Symposium on Computer Science Education (pp. 153–158). https://doi.org/10.1145/2157136.2157182
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