We’ll be presenting two papers at EDM this year. The first, “Toward Data-Driven Example Feedback for Novice Programming,” explores generating adaptive example-based feedback, which presents a partial solution to a programming problem when a student is stuck. His results suggest that by leveraging student data, we can generate higher-quality, more adaptive examples than just using an expert solution, but the results may depend on a student’s ability to select which feature they want to see completed. The second, ”One minute is enough: Early Prediction of Student Success and Event-level Difficulty during a Novice Programming Task,” is a collaboration with the D3 and Game2Learn labs at NCSU, and presents a model for predicting whether a student will succeed at a given programming problem. The model is able to predict with impressively high accuracy with only 1 minute of data on a 20+ minute programming task.