  Blended courses that mix in-person instruction with online platforms are
increasingly popular in secondary education. These tools record a rich amount
of data on students' study habits and social interactions. Prior research has
shown that these metrics are correlated with students' performance in face to
face classes. However, predictive models for blended courses are still limited
and have not yet succeeded at early prediction or cross-class predictions even
for repeated offerings of the same course.
  In this work, we use data from two offerings of two different undergraduate
courses to train and evaluate predictive models on student performance based
upon persistent student characteristics including study habits and social
interactions. We analyze the performance of these models on the same offering,
on different offerings of the same course, and across courses to see how well
they generalize. We also evaluate the models on different segments of the
courses to determine how early reliable predictions can be made. This work
tells us in part how much data is required to make robust predictions and how
cross-class data may be used, or not, to boost model performance. The results
of this study will help us better understand how similar the study habits,
social activities, and the teamwork styles are across semesters for students in
each performance category. These trained models also provide an avenue to
improve our existing support platforms to better support struggling students
early in the semester with the goal of providing timely intervention.
