  This paper addresses a key challenge in Educational Data Mining, namely to
model student behavioral trajectories in order to provide a means for
identifying students most at-risk, with the goal of providing supportive
interventions. While many forms of data including clickstream data or data from
sensors have been used extensively in time series models for such purposes, in
this paper we explore the use of textual data, which is sometimes available in
the records of students at large, online universities. We propose a time series
model that constructs an evolving student state representation using both
clickstream data and a signal extracted from the textual notes recorded by
human mentors assigned to each student. We explore how the addition of this
textual data improves both the predictive power of student states for the
purpose of identifying students at risk for course failure as well as for
providing interpretable insights about student course engagement processes.
