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Student Coding Styles as Predictors of Help-Seeking Behavior

Bumbacher, E., Sandes, A., Deutsch, A., & Blikstein, P
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Artificial Intelligence in Education

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.