  In this paper, we compare predictive models for students' final performance
in a blended course using a set of generic features collected from the first
six weeks of class. These features were extracted from students' online
homework submission logs as well as other online actions. We compare the
effectiveness of 5 different ML algorithms (SVMs, Support Vector Regression,
Decision Tree, Naive Bayes and K-Nearest Neighbor). We found that SVMs
outperform other models and improve when compared to the baseline. This study
demonstrates feasible implementations for predictive models that rely on common
data from blended courses that can be used to monitor students' progress and to
tailor instruction.
