This paper examines whether interaction data from Marmoset, an auto-grading system for programming assignments, can predict students' midterm and final exam grades. Features include per-task passing rate, testcase outcomes (public/release/hidden), time between submission and deadline, and number of submissions. The authors built decision-tree classification models (grouping students as good/satisfactory/poor performers) and linear-regression models (predicting numeric grades) from these features. The regression approach performed much better than classification, with a mean difference of 0.92 on a maximum grade of 120; when regression predictions were converted into performance categories for direct comparison with the classifiers, results also improved. The authors separately tested a regression model using only the submission-time-interval feature. Sections: Introduction, Related Work, Student Performance and Marmoset, Modeling Techniques, Features, Experiments and Evaluation.