Recent research in CS education has leveraged machine learning techniques to capture students’ progressions through assignments in programming courses based on their code submissions. With this in mind, we present a methodology for creating a set of descriptors of the students’ progression based on their coding styles as captured by different non-semantic and semantic features of their code submissions. Preliminary findings show that these descriptors extracted from a single assignment can be used to predict whether or not a student got help throughout the entire quarter. Based on these findings, we plan on developing a model of the impact of teacher intervention on a student's pathway through homework assignments.
Bumbacher, E., Sandes, A., Deutsch, A., & Blikstein, P. (2013, July). Student coding styles as predictors of help-seeking behavior. In Artificial intelligence in education (pp. 856-859). Springer Berlin Heidelberg.
