Predicting Progress in a Large-Scale Online Programming Course.

With vast amounts of educational data being generated in schools, educators are increasingly embracing data mining techniques to track student progress, especially in programming courses, a growing area of computer science education research. However, there are few accurate and interpretable methods to track student progress in programming courses. To bridge this gap, we propose a decision tree approach to predict student progress in a large-scale online programming course. We demonstrate that this approach is highly interpretable and accurate, with an overall average accuracy of 88% and average dropout accuracy of 82%. Additionally, we identify important slides such as problem slide which significantly impact student outcomes.