  Educational software data promises unique insights into students' study
behaviors and drivers of success. While much work has been dedicated to
performance prediction in massive open online courses, it is unclear if the
same methods can be applied to blended courses and a deeper understanding of
student strategies is often missing. We use pattern mining and models borrowed
from Natural Language Processing (NLP) to understand student interactions and
extract frequent strategies from a blended college course. Fine-grained
clickstream data is collected through Diderot, a non-commercial educational
support system that spans a wide range of functionalities. We find that
interaction patterns differ considerably based on the assessment type students
are preparing for, and many of the extracted features can be used for reliable
performance prediction. Our results suggest that the proposed hybrid NLP
methods can provide valuable insights even in the low-data setting of blended
courses given enough data granularity.
