  Student performance prediction - where a machine forecasts the future
performance of students as they interact with online coursework - is a
challenging problem. Reliable early-stage predictions of a student's future
performance could be critical to facilitate timely educational interventions
during a course. However, very few prior studies have explored this problem
from a deep learning perspective. In this paper, we recast the student
performance prediction problem as a sequential event prediction problem and
propose a new deep learning based algorithm, termed GritNet, which builds upon
the bidirectional long short term memory (BLSTM). Our results, from real
Udacity students' graduation predictions, show that the GritNet not only
consistently outperforms the standard logistic-regression based method, but
that improvements are substantially pronounced in the first few weeks when
accurate predictions are most challenging.
